Javier Garcia Lopez, Paula Benedetti, Luis Puente Maest and Javier de Miguel Diez.

The Volume reduction in COPD (COPD) ‘Effective in all variants’. However, it does require a comprehensive study of patients who can benefit from these treatments, as explained by pulmonologists in Gregorio Maranon Hospital Who participated in the new Interhospital conference on pulmonology in Madridorganized by medical writing.

During the meeting, sponsored by Neumomadrid Foundation In cooperation with GSK s Oxymsa Nippon gasesA review of treatment options for patients with severe COPD, whose daily habits are limited, was conducted. He explained that “the patient expresses this great feeling of lack of air and the need to breathe very superficially.” Paola BenedettiDr., a medical specialist in the Respiratory Service of the Gregorio Marañone Hospital, has focused on the basics and indications for these alternatives.

Discussion table on volume reduction in severe COPD.

The main goal, as explained by the expert, is “a little more help” to the patient, that is, Reduce hyperinflation Improve respiratory mechanics and diaphragm muscle function. Also, improving the elastic contraction of the lung, favoring the gas exchange capacity of the remaining lung tissue or reversing the chronic decrease in the supply of tissues with oxygen.

Like all treatment, Benedetti warned, Size reduction has indications and contraindications. The first is that he is a patient with severe emphysema, with “severe” shortness of breath or who meets certain functional criteria. Among the contraindications, on the other hand, we find the presence of bronchiectasis or cancer as well Continuation of the tobacco habit or treatment with prednisone, among others.

The specialist explained that the types of lung volume reduction Valves for acute emphysema with intact incisionsAnd steam in acute emphysema with attached fissures.

One of the primary advantages of valves, the pulmonologist emphasized, is that they are reversible, while steam is “not a reversible option.” At this point, the expert indicated the speed with which this technique is being applied, not the exception to it Possible complications such as shortness of breath, fever or severe pneumonia.

“An essential thing is Careful selection of patientsthe main candidate to refer to these treatments, “notes the specialist, who emphasized that it is necessary”Offer benefit over risk. Another important aspect, he added, “is that there is no age limit and does not interfere with lung transplantation.”

Javier García-López, Paola Benedetti during the Inter-Hospital Conference on Pulmonology.


The experience of cashew nuts in reducing volume in chronic obstructive pulmonary disease

On the table, runs it Luis Puente MaestoHead of the Department of Pulmonology; s Javier de Miguel DiezHead of the Respiratory Service Division. I also intervened Javier Garcia Lopezwho is also the center’s chief of pulmonology services, which chronicled the hospital’s experience with this technology through a clinical case that demonstrated how the specialists worked.

In this case a 71-year-old woman, a former smoker, with a Long-term chronic obstructive pulmonary disease And that he has had two incomes in the past three years. With her, and with any patient, “the potential causes of exclusion must be carefully evaluated,” Garcia explained, emphasizing that “The patient must know the risks before deciding whether to seek treatment“.

After evaluating a file Emphysema via computed tomographyAn echocardiogram, which cannot be evaluated, is performed Pulmonary arterial hypertension Very concretely, the specialist recalls. At this point, he highlights that one of the contraindications, pulmonary hypertension, is the only one available to the patient, so “you have to go further and have a catheterization”, as pulmonologists assure it is neither severe nor moderate, so you can continue with treatment .

“Knowing that it’s a filter, we have to choose how we handle it: whether with valves or steam,” says the specialist. “We always prefer valves because they are reversible in case of complications,” he adds. The patient, remember, was laid Three valves four millimeters. All this just three months after he went to counseling. He remembers that after four days without immediate complications, he was discharged from the hospital. However, ten days after discharge, the patient went to the emergency department referring to a Source That after conducting a study, it was confirmed that it was LSD . atelectasisAlthough his shortness of breath improved.

Since 2015, the specialist noted, The hospital treated 21 patients through coils, valves and steam; With subjective improvement in 80 percent of patients, even after 5 years. However, these procedures “are not free,” Garcia emphasized, so they are associated with risks. In the hospital experience, there was a case of pneumothorax, a massive hemoptysis patient And three patients with serious infections. However, the pulmonologist stressed that “volume reduction is effective in all of its variants,” noting the need for a “comprehensive study” of each case because they are very fragile patients.

Javier de Miguel Diez, Luis Puente Maisto, Paula Benedetti and Javier Garcia Lopez.

Although it may contain statements, statements, or notes from health institutions or professionals, the information in medical writing is edited and prepared by journalists. We recommend the reader to consult a health professional for any health-related questions.

Source link

The presence of lung disease was low among infants with cystic fibrosis (CF) and was not associated with respiratory infections, yet respiratory symptoms, like cough or wheezing, may be a sign that lung disease is progressing, according to a recent study.

“Respiratory symptoms in infants with CF deserve attention as potential indicators of emerging structural lung disease,” the researchers wrote, adding that there is a “window of opportunity” in which the symptoms could be addressed to prevent lung disease from developing.

The study, “Association between early respiratory viral infections and structural lung disease in infants with cystic fibrosis,” was published in the Journal of Cystic Fibrosis

Recommended Reading

new parents | Cystic Fibrosis News Today | image of infant sleeping

Infants with CF often begin to develop signs of lung disease at an early age. While a role for respiratory infections in contributing to lung disease in adults with CF has been established, the role of these infections early in life remains unclear.

To learn more, a research team examined the relationship between respiratory infections and signs of lung disease on CT scans among 73 infants who were evaluated at CF centers in the U.S. and Australia.

Among the infants, the mean age at enrollment was 3.1 months, and participants remained in the study for a mean follow-up of 8.7 months.

In total, 820 nasal swabs — a median of 10 per infant — were collected to test for the presence of respiratory viruses. Of them, 304 swabs (37%) were positive for at least one virus. These infections occurred in 65 (89%) of the infants, for whom the mean number of positive swabs was 4.7.

The human rhinovirus — which causes the common cold — was the most commonly observed virus, found in 85% of participants.

Most infants (60 children) had CT scans at a mean age of 13 months (just over a year). Overall, minimal signs of structural lung disease were observed.

This relative lack of observed lung disease could be due to advancements in early screening and care for CF, the researchers noted, adding that some of the infants were concurrently participating in a study testing treatment with the antibiotic azithromycin (NCT01270074), which could have influenced their lung health.

No measured parameters of lung disease were significantly correlated with ever having a respiratory virus, the number of infections, or the age at detection of an infant’s first virus.

The team did find that the percent of total airway disease was higher in infants from the U.S. than Australia, which was likely driven by a greater amount of airway wall thickening — a lung symptom that makes breathing more difficult. An older age was also associated with more airway wall thickening.

Parents and physicians often reported respiratory symptoms in the CF infants, which did not overall correlate with a positive nasal swab for infection. However, nasal swabs were collected in less than half of the times that symptoms were reported, the team noted.

An analysis of reported symptoms and signs of lung disease on CT showed a significant association between a child ever having symptoms of wheezing and a higher amount of airway wall thickening, which led to an association between wheezing and levels of total airway disease.

Cough was also associated with a greater percent of airway wall thickening and with atelectasis — a partial or total lung collapse. Lower respiratory symptoms like wheezing or altered breathing were similarly associated with atelectasis.

The researchers found that detection of rhinovirus within two weeks of respiratory symptoms was associated with CT findings, namely, airway wall thickening and bronchiectasis — a condition in which the airways become damaged, leading to inflammation and scarring.

Overall, the findings “failed to show that the detection of respiratory viruses significantly contribute to the presence of structural lung disease assessed at approximately one year of age,” but do suggest that “frequent respiratory symptoms may be an indication that structural lung disease is progressing,” the researchers wrote.

The team noted that clinicians should carefully monitor and treat respiratory symptoms to prevent further lung damage in children with CF.



Source link

Around 25 million Americans suffer from asthma each year, and the new store expansion ensures that they can improve their breathing at home. The AirPhysio device is lightweight, easy to use, and can fit inside any bag.

For more information, please visit: lifewellnesshealthcare.com/products/airphysio-device

The latest store expansion has been announced due to popular demand for the award-winning, Australian-made breathing aid. Featured on several major platforms, it’s ideally suited to those with asthma, COPD, or bronchiectasis. It clears the airway and strengthens the lungs without requiring extensive use.

Asthma causes bronchoconstriction, a condition where bronchus muscle contraction limits the air that people breathe. It also triggers inflammation of soft tissue, and can often lead to excess mucus buildup.

By ordering the AirPhysio from Life Wellness Healthcare, asthma sufferers can strengthen their lungs through oscillating positive expiratory pressure. This OPEP technology initiates the body’s natural mucus-clearing process using vibration.

The company explains that the device only needs to be used for a few minutes at a time. No extra materials, batteries, or liquids are required for this. Customers simply exhale through the product to begin the process.

Life Wellness Healthcare continually updates its store to ensure that customers across America have access to the best respiratory devices.

In addition to the AirPhysio, a variety of other products are available in the store. These include filters for the AirPhysio for optimal lung health, protective masks, and spacers for better breathing.

A spokesperson for the company states: “Life Wellness Healthcare was established to meet the needs of people who live each day with respiratory conditions. We know first hand what it’s like to live with such conditions. It can be hard, and that’s why we are committed to offer products that meet the needs of the customers we serve. Our aim is to help people breathe again.”

The AirPhysio OPEP device has currently garnered over 2,000 five-star reviews for its effectiveness. Customers can order with ease through the Life Wellness Healthcare site, and there are several payment options available.

Those wishing to find out more can visit: lifewellnesshealthcare.com/products/airphysio-device

Contact Info:
Name: Matthew
Email: Send Email
Organization: Life Wellness Healthcare
Address: PO BOX 6662, Tweed Heads, NSW 2486, Australia
Phone: +61-7-3608-5683
Website: lifewellnesshealthcare.com/

Release ID: 89071661

If you detect any issues, problems, or errors in this press release content, kindly contact [email protected] to notify us. We will respond and rectify the situation in the next 8 hours.

comtex tracking

COMTEX_406988708/2773/2022-05-10T15:09:12

Source link

New funding has allowed the Department of Respiratory Medicine to grow the physiotherapy service to outpatient and community based respiratory patients. This exciting new position offers an experienced Cardio-Respiratory Physiotherapist the opportunity to enhance their skills and knowledge and contribute to the established research culture within the Respiratory outpatient service. Post-graduate education is actively encouraged and supported.

You will be part of an interdisciplinary team who primarily provide respiratory care to youth and adults with respiratory conditions including COPD, asthma and bronchiectasis, as well as people with co-existing neuro-muscular conditions. Opportunities also exist to work in the interdisciplinary post-COVID clinic. These physiotherapy services are provided through pulmonary rehabilitation, disordered breathing clinics, bronchiectasis and interdisciplinary clinics across multiple sites, including the community within the Counties Manukau Health catchment area. 

These innovative teams work to provide better, sooner and more convenient care for our patients with respiratory conditions, while supporting other cardio-respiratory physiotherapy services. Our team includes a section head physiotherapist, nurse manager, physiotherapists including physiotherapy advanced clinicians, respiratory nurses, respiratory consultants and physiotherapy assistants. The wider team includes other care providers including community stakeholders, non-profit organisations and tertiary care providers. 

To be successful in this role, you will need to have ideally a minimum 12 months of cardio-respiratory experience, excellent communication skills and the ability to build relationships with patients and their whānau/families, and the other members of the health team. 

Source link

Having been told by doctors that she “probably wouldn’t live beyond the age of 10”, Conley is defying the odds. The star, who shot to fame after publishing her low-fat diet and exercise programme, The Hip & Thigh Diet back in 1988, was diagnosed with asthma at the mere age of two, but was determined not to let the condition hold her back. In fact, as she said in her own words, as a child she was “always active”, having not known what life was like to live asthma-free. A natural-born performer, Conley quickly established herself within the health and fitness industry, but at the age of 20 she suffered from her first asthma attack. After this she was advised by medical professionals to try a different sort of inhaler. But away from all the medication and inhalers to curb her respiratory issues, Conley credits her career for saving her life.

In addition to asthma, around 10 years ago Conley was diagnosed with pseudomonas, a type of bacteria that can cause lung infections. Unlike normal chest infections, pseudomonas may not go away using normal treatments, and can cause a funny taste in a person's sputum (phlegm).

Speaking about her diagnosis, Conley said: “I said, there's something not right with my lungs, because this [phlegm] doesn't taste right. So my doctor said to bring a sample in, and then a few days later he rang me and I was driving and he said ‘Right, you need to pull over. You've got pseudomonas in your lungs.’ I said, ‘What does that mean?’

“He went on to explain that it means I had a really bad infection in my lungs. But he said it's serious. And I said, ‘Is it going to kill me?’

“And so he said, well, ‘Hopefully not.’”

READ MORE: Two Covid symptoms you should take 'really seriously' as new variants reported in the UK

Conley went on to explain that for pseudomonas there is only one drug that tends to work, which is known as ciprofloxacin. If this fails to work in tablet form, individuals are usually taken into hospital for the drug to be administered intravenously.

Fortunately for Conley the drug rid the pseudomonas, but unfortunately her struggle with respiratory conditions was not over. At the end of 2016 she developed whooping cough and soon after that an incurable condition known as bronchiectasis.

According to Asthma + Lung UK, bronchiectasis is a long-term incurable condition that affects the airways in the lungs. Affecting the part of the lung known as the bronchi, the condition causes airways to become inflamed and thick mucus develops. This mucus build up makes it extremely difficult for airways to remain clear, easily becoming infected with bacteria.
This can cause individuals to suffer from frequent chest infections, which if not treated can cause further damage to the lungs.

Amazingly, and through her work as a health and fitness specialist, Conley was able to remain active, and not let her bronchiectasis or asthma interfere with her career. In fact, she claims it was the complete opposite, with her career helping to save her life.

She added: “After I appeared on Dancing On Ice we choreographed and filmed a routine with my professional partner on the show and I took it to show my doctor, Professor Ian Pavord, who said, ‘I can’t believe what you do because your lung capacity is so minimal.’”

DON'T MISS: 

Conley explained that by the time she was going to school around age four and five, her lungs were still not developed properly, the potential source to all of her ongoing respiratory trouble. She said: “If I had not followed the fitness journey that I had, my life would be a very different story.

“Through divine intervention, I think, I found a career that basically saved my life.”

When asked how she manages her asthma and bronchiectasis now, Conley spoke about a transformative medication known as Nucala. The monthly injection was one of the first in a new line of treatments designed to treat a specific type of asthma known as severe eosinophilic asthma. Asthma + Lung UK explains that this type of asthma is one of the most debilitating forms of the condition, involving an inflammation of the airways linked to a particular type of white blood cell (eosinophils).

For Conley personally, the treatment has helped to increase her fitness capacity by 25 percent, making her substantial routine of 10,000 steps a day, 30-minute daily walk, twice a week gym sessions, ballet classes and her own personal fitness classes a little easier every time.

She said: “As you get older, everything else starts deteriorating, and so do your lungs. So I need to work on how to maximise them and since I have been on Nucala, my output has been dramatically different. By the time I hit 70, I wondered if I would reach 80 and I did have that conversation with Professor Pavord, but now thanks to Nucala, I think well stuff that, we are going to go much longer than that!”

In addition to keeping fit, Conley spoke about following a healthy diet, going on to say that those who are overweight and are inactive are following a “recipe for disaster”.

It is for this reason that the star thinks that the newly enforced rules of calories on menus selling non-prepacked food and soft drinks is a “brilliant idea”. She said: “I went out about three weeks ago and we went to this lovely little pub but there were three things on the menu that were over 1,000 calories just for a main course.

“And instead of having what I might have had in the past, I opted for a salad with avocado, fruit, feta cheese and all sorts of stuff. It was only 560 calories, it was healthy and I really enjoyed it. It was important because I could make an educated judgement.”

Exercise and diet is critical to Conley’s daily routine, and has been throughout her career, but for those with severe asthma or other respiratory conditions who might be worried about overexerting themselves, Andrew Whittamore, Clinical Director at Asthma + Lung UK, and a practising GP, provides some advice.

He said: “Although exercise can be a trigger for some people with respiratory disease, being active and doing exercise brings many benefits. By making our bodies work a little bit harder we can help our lungs to work more efficiently; this improves how we transport oxygen around the body and improves how we use that oxygen. Because of this, doing regular exercise means that we may start to feel less breathless and less tired and more in control of our breathing.

“As well as that, exercise can help us to maintain a healthy weight, strengthen our muscles and bones and can have a big impact on our mental health. If you have asthma and want to exercise regularly, it’s important to be aware of your triggers.

“For some people, cold, dry air can cause your airways to tighten and get narrower. This can lead to asthma symptoms like coughing, wheezing and breathlessness. Other common exercise triggers are pollution, pollen, or dust. Regardless of what triggers your asthma. Always have your reliever inhaler with you, and to use your preventer as prescribed.”

Asthma + Lung UK is the only charity in the UK fighting for everyone with a lung condition, aiming for a world where everyone can breathe with healthy lungs. The charity funds research and provides advice and support for the 12 million people who will get a lung condition during their lifetime. It campaigns for clean air and for better NHS diagnosis and treatment. For further information visit asthmaandlung.org.uk



Source link

Cuiqiong Dai,* Zihui Wang,* Zhishan Deng, Fan Wu, Huajing Yang, Shan Xiao, Xiang Wen, Youlan Zheng, Jianwu Xu, Lifei Lu, Ningning Zhao, Peiyu Huang, Yumin Zhou, Pixin Ran

State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, People’s Republic of China

Correspondence: Pixin Ran; Yumin Zhou, The State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, No. 195 Dongfeng Xi Road, Guangzhou, 510000, People’s Republic of China, Tel +86-020 83205187, Fax +86-020 81340482, Email [email protected]; [email protected]

Background: Serum total bilirubin has been reported to have antioxidant properties against chronic respiratory diseases. The objective of our study is to evaluate the association of total bilirubin (TB) with annual lung function decline in COPD patients with different GOLD stages.
Methods: This study used pooled data from two observational and prospective cohorts of 612 COPD patients whose TB levels were measured at baseline. The associations between TB and postbronchodilator FEV1, FEV1pred, FVC, FVCpred, FEV1/FVC, and the rate of their decline were all determined using linear regression models in the total population and strata of GOLD stages.
Results: Serum TB was positively related to FEV1 and FVC in the total group (β 0.02, 95% CI 0.001∼ 0.02, P = 0.025 and β 0.02, 95% CI 0.002∼ 0.03, P = 0.022, respectively). Additionally, TB was inversely associated with the annual decline in FEV1 and FEV1pred (β 4.91, 95% CI 1.68∼ 8.14, P = 0.025 and β 0.21, 95% CI 0.06∼ 0.36, P = 0.022, respectively) when adjusted for multivariables. After stratification, the significant associations merely persisted in COPD patients with GOLD 2 and GOLD 3– 4.
Conclusion: Increased TB level was related to less annual decline in FEV1 as well as FEV1pred in moderate-to-severe COPD but not mild COPD, which indicated the different status of TB in different COPD severity and the possible role as potential biomarker merely in moderate-to-severe COPD. Future researches to determine whether TB could be served as biomarker for COPD and the mechanisms should be focused on some target patients with a certain disease severity.

Introduction

Serum total bilirubin (TB), an end product of heme degradation, is demonstrated to scavenge oxidation free radicals and thus suppress oxidative stress.1 Experimental evidence from animal models indicated that TB could protect respiratory tissues against environmental stressors by cytoprotective properties.2,3 Additionally, accumulating clinical studies supported that mild higher bilirubin levels could decrease the risk of cardiovascular disease (CVD) and CVD-related diseases.4–6 An increased total bilirubin level is also associated with a lower risk of chronic respiratory diseases, including lung cancer and chronic obstructive pulmonary disease (COPD), as well as all-cause mortality.7–9 Furthermore, Kirstin E. et al found that elevated bilirubin concentrations would decrease the acute exacerbation of COPD.10

A few longitudinal cohort studies have explored the relationship between total bilirubin and the rate of lung function decline. For example, Ah YL et al11 showed that serum bilirubin concentration was significantly related to the annual changes in FEV1, FVC, and FEV1 /FVC in general Koreans adjusted for sex, age, body mass index (BMI), and baseline lung function. Similarly, only in mild COPD, serum bilirubin is inversely related to the rate of FEV1 decline over 3 to 9 years.12 In a Swiss general population sample, per interquartile range increase of bilirubin exposure is concomitant with 1.1% increase in FEV1/FVC and 116.2 mL in FEF25-75% in smokers with high BMI.13 Also, David M. et al demonstrated that TB is significantly associated with higher FEV1 and FVC in baseline lung function in AIDS individuals.14 However, all these studies focused on the general or specific target population. And the data between bilirubin concentrations and the annual decline in lung function in all different GOLD stages of COPD have not been reported before. To fill this evidence gap, we hypothesized that higher TB level is associated with less annual lung function decline in COPD patients with different GOLD stages. To test the hypothesis, we pooled data from two observational and prospective cohort studies to assess the relationship between serum TB and lung function decline in the total COPD population and then further in different GOLD stage subgroups.

Methods

Study Subjects

The data were pooled from two population-based, multi-center, and prospective cohort studies, including the National Key Technology Research and Development Program of the 12th National 5-year Development Plan (2012–2015 Program; Trial registration number ChiCTR-OO-14004264) and the Early COPD (ECOPD; Trial registration number ChiCTR1900024643). The details of these two cohorts have been reported previously.15,16 Briefly, two cohorts recruited participants aged 20 years or older and completed bilirubin measurement, questionnaires, and lung function tests at baseline. Subjects included in this study completed spirometric measurements again for an interval of 7 to 9 years follow-up duration in 2012–2015 Program as well as 1 to 2 years in ECOPD. Only participants defined as COPD (postbronchodilator FEV1/FVC <0.7)17 with reliable data were included in our analysis. Participants were excluded if they had any comorbidity that might significantly influence bilirubin, such as hemolytic disorders, hepatobiliary diseases, or renal insufficiency by self-report. Additionally, TB concentrations >1.75 mg/dL for women and >2.34 mg/dL for men were also excluded for suspicious Gilbert syndrome, a benign hereditary disease caused by UGT1A1 genotypes.18 The original two studies were performed in line with the principles of the Declaration of Helsinki. Besides, the two studies were approved by the Ethics Committee of Scientific research project review of the First Hospital of Guangzhou Medical University (No.2013–37 for 2012–2015 Program and N0.2018–53 for ECOPD) and written informed consent was also attained from all the participants.

Bilirubin Measurement

At baseline, venous blood samples were collected from eligible participants required to fast for more than 8 hours by trained and professional staff. The samples will be stored at room temperature for at least 30 min and then quantified by biochemical assays performed by the clinical laboratory to test total bilirubin. Bilirubin concentrations were measured or transformed to the nearest 0.06 mg/dL (1 μmol/L).

Lung Function Measurement

Spirometry was performed by well-trained staff using a portable spirometer (CareFusion™ MasterScreen Pneumo, Germany) on the same day of bilirubin detection. Only at least three acceptable curves and two reproducible tests can be considered as acceptable for our study. We also performed postbronchodilator Spirometry (salbutamol sulfate aerosol, 400 μg 20min later) and then recorded and retrieved postbronchodilator FEV1, FVC, FEV1pred, FVCpred, and FEV1/FVC. Annual Lung function decline was calculated as the parameters at follow-up minus corresponding baseline values divided by the number of years for follow-up duration. All spirometric maneuvers and quality control were performed following European Respiratory Society/American Thoracic Society standards.19 The definition of GOLD stages in our study was in line with GOLD criteria for COPD, and we considered GOLD 3–4 COPD as severe COPD.17

Questionnaire

A questionnaire interview was carried out using a standardized questionnaire recommended by Zhong N et al.20 We abstracted information about demographic characteristics, risk factors (smoke, occupational exposure, comorbidities, and family history of respiratory diseases), and respiratory symptoms, including any one of wheeze, cough, sputum production, and dyspnea. We classified smoking status as never smoker, former smoker, and current smoker. Subjects who have smoked less than 100 cigarettes in the past are identified as never smokers. Current smokers are referred to someone who were smoking at baseline, and the others are regarded as former smokers. The definition of the smoking index is multiplying the number of packs of cigarettes smoked per day by the number of years the person smoked. We took subjects whose parents, siblings, or children had any of the listed respiratory diseases (chronic bronchitis, emphysema, asthma, COPD, cor pulmonale, bronchiectasis, lung cancer, interstitial lung disease, obstructive sleep apnea hypopnea syndrome) as having a family history of respiratory diseases.

Statistical Analysis

All variables are expressed as mean ± SD or number (%). Characteristics of participants among GOLD 1, GOLD 2, and GOLD 3–4 compared using One-Way analysis of variance (ANOVA) test for continuous variables and the χ2 or Fisher's exact test for categorical variables. The associations of TB with pulmonary function parameters at baseline or annual lung function decline were assessed using univariable and multivariable regression models. All linear regression models were adjusted for age, sex, BMI, smoking status, smoke index, passive smoker, occupational exposure, comorbidities, and family history of respiratory diseases. To ensure the authenticity of the data, there was no imputation for missing values of interest in our analysis. Data analysis was performed using IBM SPSS Version 25.0 and the statistical software R version 4.0.3. Two-sided p < 0.05 were considered statistically significant.

Results

Baseline Characteristics

Of the 1053 patients with COPD, 442 were excluded for various reasons (Figure 1). The remaining 612 subjects were divided into three subgroups by GOLD stages. Characteristics of the total group and three subgroups are presented in Table 1. Compared with patients with mild COPD (GOLD 1), patients with moderate-to-severe COPD (GOLD 2, 3 and 4) were more likely to be old, female, breathless and have lower BMI, heavier smoking exposure, more comorbidities, more family history of respiratory diseases as well as worse lung function. There was no significant difference in TB levels among the three subgroups (P = 0.486). The mean follow-up duration for our patients was about two years.

Table 1 Baseline Characteristics of the Study Subjects

Figure 1 Flow chart of participant selection.

Abbreviation: COPD, chronic obstructive pulmonary disease.

Note: COPD was diagnosed by postbronchodilator FEV1/FVC <0.7.

Associations of Total Bilirubin with Baseline Lung Function Parameters

Univariable and multivariable linear regressions of baseline pulmonary function parameters such as FEV1, FEV1pred, FVC, FVCpred, and FEV1/FVC on TB concentrations are shown in Table 2. After adjustment for age, sex, BMI, smoking status, smoke index, passive smoker, occupational exposure, comorbidities, and family history of respiratory diseases, TB level (μmol/L) was significantly related to FEV1 and FVC in the total COPD patients (β 0.02, 95% CI 0.001~0.02, P = 0.025 and β 0.02, 95% CI 0.002~0.03, P = 0.022, respectively). Stratified by GOLD stages, the relationship between TB and FEV1 was only present in GOLD 1 stage (β 0.01, 95% CI 0.001~0.02, P = 0.032), whereas FVC became tending to be slightly associated with bilirubin (P = 0.054).

Table 2 Univariable and Multivariable Linear Regression Analysis for TB and Baseline Lung Function Parameters in the Total Group and Subgroups by GOLD Stages

Total Bilirubin and Annual Lung Function Decline

Totally, higher bilirubin concentrations were associated with a reduced rate of decline in lung function, which is consistent in the univariable and multivariable regression analysis approximately (Figure 2). In the total COPD group, we found that per 1 μmol/L increase in TB will reduce 4.91 mL/L decline in FEV1 (P = 0.003), 0.21% decline in FEV1pred (P = 0.005), 6.26 mL/L decline in FVC (P = 0.039), and 0.23% in FVCpred (P = 0.023). However, following stratification according to GOLD stages and multi-adjustments, the similar associations of FEV1 and FEV1pred with TB merely existed in GOLD 2 and GOLD 3–4 patients. Besides, bilirubin only showed a protective effect on the rate of decline in airflow limitation (FEV1/FVC) in severe COPD (GOLD 3–4).

Figure 2 Linear regression analysis for the relationship between bilirubin and annual lung function decline, by overall COPD population and stratified by GOLD stages.

Notes: Annual Lung function decline was calculated as the parameters at follow-up minus corresponding baseline values divided by the number of years for follow-up duration. Multivariable linear regression analysis models were adjusted for age, sex, BMI, smoking status, smoke index, passive smoker, occupational exposure, comorbidities and family history of respiratory diseases. Per unit increase of TB in all these models is 1 μmol/L. Crude β means the unstandardized β estimate from univariable analysis, Crude P values mean the P values from univariable analysis, adj.β means the unstandardized β estimate from the multivariable analysis, adj.P values mean the P values from multivariable analysis. Abbreviations see abbreviation section in Table 1.

Discussion

In the pooled data from two prospective and longitudinal cohort studies, we found that TB was positively associated with FEV1 in the total COPD population at baseline. Besides, TB also significantly reduced the rate of FEV1 and FEV1pred decline in the total COPD group at follow-up duration after adjustment for age, sex, BMI, smoking status, smoke index, passive smoker, occupational exposure, comorbidities, as well as family history of respiratory diseases. Surprisingly, when stratifying by disease severity according to GOLD stages, relationships significantly persisted only in patients with moderate-to-severe COPD. To our best knowledge, our study was the first to explore TB and lung function in the target COPD population with different GOLD stages.

What we found between TB and FEV1 were in line with those of Ah YL et al11 who found that higher TB concentration was associated with higher FEV1 and less annual FEV1 decline in the general Koreans and those of Apperley S et al12 who also showed that consistent associations in mild-to-moderate COPD after adjusting for age, sex, race, BMI and baseline measures of lung function. All these demonstrations indicated the potential protective role of TB played on lung function. We could reasonably speculate about the mechanisms of mild higher TB level protecting lung function. Summarily, the most important heme oxygenase that influences the biochemical production of bilirubin is heme oxygenase-1 (HO-1),21 which would be up-regulated by oxidative stress22 and hypoxia.23,24 HO-1 is expressed in pulmonary cells including type II pneumocytes and alveolar macrophages.25 COPD is a chronic pathological process accompanied by endogenously or exogenously produced oxidative stress and inflammation.26,27 Higher HO-1 levels expressed in COPD would increase TB concentrations. Furthermore, bilirubin has been reported to be an endogenous bioactive substance possessing substantial antioxidant activities by several experiments.28–30 Thus, TB may improve the lung function in COPD in this way.

Another extended finding was that the significant protection effects merely persisted in moderate-to-severe COPD. It may be implied that only when oxidative stress increased to a certain degree can bilirubin perform a function against oxidation. We can get some evidence from the current studies. A Mendelian Randomization analysis using UK Biobank showed that smokers with genetically raised serum bilirubin levels have lower rates of lung cancer and these relationships are strongest in current heavy smokers (20 or more cigarettes per day),31 whereas Hyun et al found that high serum TB was not significantly associated with the presence of COPD in never-smokers.32 Likewise, our data showed that patients in GOLD 2 and GOLD 3–4 had more smoking exposure (more smoking index, pack-yrs). As we know, heavy cigarettes exposure aggravated inflammation and oxidative damage of COPD, which would be suppressed by bilirubin reduction to attenuate pulmonary injury.33 Differently from previous studies,11,13 we found no significant association between TB and airflow limitation (FEV1/FVC) in the total group. The conflicting results may be attributed to the differences of adjusted covariates in the proportion of sex, distribution of age, BMI, smoking status and index, comorbidities, as well as family history of respiratory diseases.

Our study also has a few limitations. First, the sample size in moderate-to-severe COPD was small, so we could not respectively evaluate the relationship of TB and GOLD 3 COPD or GOLD 4 COPD, given that the participants were community-based. However, GOLD 3 and GOLD 4 were usually incorporated as severe COPD in clinical epidemiology. Second, we only collected one blood sample, which did not allow for continuous evaluation of intraindividual variability, although bilirubin levels are demonstrated to keep stable after adolescence.34 Besides, we could not exclude the influence of alcohol intake on TB for lacking the related information. However, we have excluded other factors affecting bilirubin as we can by incorporating hepatic disease into comorbidities, which may be caused by heavy alcohol consumption.

Conclusions

In conclusion, we confirmed again that TB is inversely associated with annual lung function decline in FEV1 and FEV1pred in COPD. And that association persists only in moderate-to-severe COPD adjusted for multi-covariates, which implicated the different functional status of TB being in different degrees of COPD severity. It is also suggested that TB may be a biomarker for only moderate-to-severe COPD but not mild COPD. We should focus more on the specific COPD patients with a certain disease severity when validating bilirubin as a biomarker of COPD potentially in future studies, given the protective effects of TB. Besides, further researches are also needed to investigate the mechanisms of TB action on different severity levels of COPD to explain the different influences on lung function decline.

Abbreviations

TB, total bilirubin; COPD, chronic obstructive pulmonary disease; GOLD, the Global Initiative for Chronic Lung Disease; mMRC, Modified Medical Research Council dyspnea; BMI, body mass index; FVC, forced vital capacity; FVC, %predicted, the ratio of FVC to its predicted value; FEV1, forced expiratory volume in the first second; FEV1,%predicted, the ratio of FEV1 to its predicted value; FEV1/FVC, the ratio of FEV1 to FVC; ANOVA, analysis of variance; SD, standard deviation; CI, confidence interval; CVD, cardiovascular disease; ECOPD, the early COPD; HO-1, oxygenase-1.

Data Sharing Statement

The datasets used and analyzed in this study are available from the corresponding author on reasonable request.

Ethics Approval and Consent to Participate

The original two studies were performed in line with the principles of the Declaration of Helsinki. All the participants from two studies gave written informed consent and two studies were approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University (No.2013-37 and No. 2018-53 respectively).

Acknowledgments

The authors thank for the contributions of all the subjects who agreed to donate their information for analysis. Besides, the authors thank Bijia Lin, Shaodan Wei and Xiaopeng Ling (State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Nanshan Medicine Innovation Institute of Guangdong Province) for their help in collecting the data.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

The study was funded by The Nature Key Research and Development Program (2016YFC1304101), the Local Innovative and Research Teams Project of Guangdong Pearl River Talents Program (2017BT01s155), the independent project of the State Key Laboratory of Respiratory Diseases (SKLRD-QN-201913), the National Science Foundation of China (81970045), and Zhong Nanshan Medical Development Foundation of Guangdong Province (ZNSA-202003, ZNSA-2020012, ZNSA-2020013).

Disclosure

All the authors declare they have no real or potential competing interests in this work.

References

1. Stocker R, Yamamoto Y, Mcdonagh A, Glazer A, Ames B. Bilirubin Is an Antioxidant of Possible Physiological Importance. Science. 1987;235:43.

2. Wang H-D, Yamaya M, Okinaga S, et al. Bilirubin Ameliorates Bleomycin-Induced Pulmonary Fibrosis in Rats. Am J Respir Crit Care Med. 2002;165(2):406–411.

3. Ryter SW, Morse D, Choi AM. Carbon monoxide and bilirubin: potential therapies for pulmonary/vascular injury and disease. Am J Respir Cell Mol Biol. 2007;36(2):175–182.

4. McCarty MF. “Iatrogenic Gilbert syndrome”–a strategy for reducing vascular and cancer risk by increasing plasma unconjugated bilirubin. Med Hypotheses. 2007;69(5):974–994.

5. Vitek L, Jirsa M, Brodanova M, et al. Gilbert syndrome and ischemic heart disease a protective effect of elevated bilirubin levels. Atherosclerosis. 2008;198:1–11.

6. Schwertner HA, Vitek L, Schwertner HA. Gilbert syndrome, UGT1A1*28 allele, and cardiovascular disease risk: possible protective effects and therapeutic applications of bilirubin. Atherosclerosis. 2008;198(1):1–11.

7. Horsfall LJ, Rait G, Walters K, et al. Serum Bilirubin and Risk of Respiratory Disease and Death. JAMA. 2011;305:691–697.

8. Monroy-Iglesias MJ, Moss C, Beckmann K, et al. Serum Total Bilirubin and Risk of Cancer: a Swedish Cohort Study and Meta-Analysis. Cancers. 2021;13:21.

9. Lim JE, Kimm H, Jee SH. Combined effects of smoking and bilirubin levels on the risk of lung cancer in Korea: the severance cohort study. PLoS One. 2014;9(8):e103972.

10. Brown KE, Sin DD, Voelker H, et al. Serum bilirubin and the risk of chronic obstructive pulmonary disease exacerbations. Respir Res. 2017;18(1):179.

11. Leem AY, Kim HY, Kim YS, Park MS, Chang J, Jung JY. Association of serum bilirubin level with lung function decline: a Korean community-based cohort study. Respir Res. 2018;19(1):99.

12. Apperley S, Park HY, Holmes DT, et al. Serum Bilirubin and Disease Progression in Mild COPD. Chest. 2015;148(1):169–175.

13. Curjuric I, Imboden M, Adam M, et al. Serum bilirubin is associated with lung function in a Swiss general population sample. Eur Respir J. 2014;43(5):1278–1288.

14. MacDonald DM, Zanotto AD, Collins G, et al. Associations between baseline biomarkers and lung function in HIV-positive individuals. AIDS. 2019;33(4):655–664.

15. Liu S, Zhou Y, Liu S, et al. Association between exposure to ambient particulate matter and chronic obstructive pulmonary disease: results from a cross-sectional study in China. Thorax. 2017;72(9):788–795.

16. Wu F, Zhou YM, Peng JQ, et al. Rationale and design of the Early Chronic Obstructive Pulmonary Disease (ECOPD) study in Guangdong, China: a prospective observational cohort study. J Thorac Dis. 2021;12:87.

17. Agusti A, Vogelmeier C, Papi A, et al. Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease: 2021 report. Available from: goldcopd.org/2021-gold-reports/. Accessed July 1, 2021.

18. Duzenli T, Maden O, Tanoglu A, Kaplan M, Yazgan Y. Associations between Gilbert’s syndrome and personality characteristics. Trends Psychiatry Psychother. 2021;43(2):151–158.

19. Miller MR, Hankinson J, Brusasco V, et al. Standardisation of spirometry. Eur Respir J. 2005;26(2):319–338.

20. Zhong N, Wang C, Yao W, et al. Prevalence of chronic obstructive pulmonary disease in China: a large, population-based survey. Am J Respir Crit Care Med. 2007;176(8):753–760.

21. Tenhunen R, Marver HS, Schmid R. Microsomal Heme Oxygenase. Biol Chem. 1969;244(23):6388–6394.

22. Carter EP, Garat C, Imamura M. Continual emerging roles of HO-1: protection against airway inflammation. Am J Physiol Lung Cell Mol Physiol. 2004;287:L24–L25.

23. Christou H, Morita T, Hsieh C-M, et al. Prevention of Hypoxia-Induced Pulmonary Hypertension by Enhancement of Endogenous Heme Oxygenase-1 in the Rat. Circ Res. 2000;86:1224–1229.

24. Minamino T, Christou H, Hsieh C-M, et al. Targeted expression of heme oxygenase-1 prevents the pulmonary inflammatory and vascular responses to hypoxia. PNAS. 2001;98(15):8798–8803.

25. Fredenburgh LE, Perrella MA, Mitsialis SA. The role of heme oxygenase-1 in pulmonary disease. Am J Respir Cell Mol Biol. 2007;36(2):158–165.

26. Kirkham PA, Barnes PJ. Oxidative stress in COPD. Chest. 2013;144(1):266–273.

27. Harijith A, Natarajan V, Fu P. The Role of Nicotinamide Adenine Dinucleotide Phosphate Oxidases in Lung Architecture Remodeling. Antioxidants(Basel). 2017;6(4):87.

28. Huang H, Guo M, Liu N, et al. Bilirubin neurotoxicity is associated with proteasome inhibition. Cell Death Dis. 2017;8(6):e2877.

29. Vitek L. Bilirubin as a signaling molecule. Med Res Rev. 2020;40(4):1335–1351.

30. Stocker R, Glazert AN, Ames BN. Antioxidant activity of albumin-bound bilirubin. Medical Sciences. 1987;84(2):5918–5922.

31. Horsfall LJ, Burgess S, Hall I, Nazareth I. Genetically raised serum bilirubin levels and lung cancer: a cohort study and Mendelian randomisation using UK Biobank. Thorax. 2020;75(11):955–964.

32. Lee H, Hong Y, Lim MN, et al. Inflammatory biomarkers and radiologic measurements in never-smokers with COPD: a cross-sectional study from the CODA cohort. Chron Respir Dis. 2018;15(2):138–145.

33. Wei J, Zhao H, Fan G, Li J. Bilirubin treatment suppresses pulmonary inflammation in a rat model of smoke-induced emphysema. Biochem Biophys Res Commun. 2015;465(2):180–187.

34. Wilding P, Rollasox JG, Robinson D. Patterns of change for various biochemical constituents detected in well population screening. Clin chim Acta. 1972;1:375–387.

Source link

Huajing Yang,1,&ast; Zihui Wang,1,&ast; Shan Xiao,1 Cuiqiong Dai,1 Xiang Wen,1 Fan Wu,1,2 Jieqi Peng,1 Heshan Tian,1 Yumin Zhou,1,2 Pixin Ran1,2

1National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China; 2Guangzhou Laboratory, Bio-Island, Guangzhou, People’s Republic of China

Correspondence: Pixin Ran; Yumin Zhou, National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China, Tel +86 2083205187, Fax +86 20-81340482, Email [email protected]; [email protected]

Background: The effect of serum uric acid (SUA) levels on lung function in chronic obstructive pulmonary disease (COPD) people remained unclear. We aimed to investigate the association between SUA and lung function.
Methods: A cross-sectional study was performed to measure the SUA levels and lung function in 2797 consecutive eligible individuals. Of these, individuals in our study were divided into two groups, the COPD group (n=1387) and the non-COPD group (n=1410). The diagnosis of COPD is defined as post-bronchodilator first second of forced expiratory volume (FEV1)/forced vital capacity (FVC) ratio of less than 0.70. Multivariable adjustment linear models were applied to estimate the effect of SUA levels on FEV1% predicted, FVC% predicted, and FEV1/FVC stratified by COPD status.
Results: After multivariable adjustment, each 1 mg/dL increase of SUA was significantly associated with a decrease in FEV1% predicted (− 1.63%, 95% confidence interval [CI] − 2.37 to − 0.90), FVC % predicted (− 0.89%, 95% CI − 1.55 to − 0.24), and FEV1/FVC (− 0.70%, 95% CI − 1.10 to − 0.30). In the COPD group, each 1 mg/dL increase of SUA was significantly associated with decreases in FEV1% predicted (− 1.87%, 95% CI − 2.91 to − 0.84), FVC% predicted (− 1.35%, 95% CI − 2.35 to − 0.34), and FEV1/FVC (− 0.63%, 95% CI − 1.18 to − 0.08). However, no significant association between lung function and SUA was found among people without COPD.
Conclusion: High SUA levels were associated with lower lung function, especially in COPD patients. However, no statistically significant effect of SUA on lung function was found in people without COPD.

Introduction

Serum uric acid (SUA) is the final breakdown product of purines or purine-containing compounds and is present at high concentrations in the epithelial lining fluid of the airway and in plasma.1–3 SUA has the double-edged characteristic of having antioxidant properties as well as pro-oxidant and pro-inflammatory properties.4,5 Based on these characteristics, there are complicated interpretations of whether SUA has a beneficial or noxious effect on lung function.6–8 An experimental study revealed that high SUA levels could improve emphysematous phenotype and lung dysfunction by reducing oxidative stress in mice with chronic obstructive pulmonary disease (COPD), and also found no significant effects of SUA on the lung function in non-diseased mice.9 What they found suggests that SUA levels may only affect lung function in individuals with impaired lung tissue but not normal lung structure.

Impairment of lung tissue reduces oxygen intake, which may result in tissue hypoxia. Tissue hypoxia elevates the SUA levels by inducing the degradation of adenosine.10 Previous studies have found a negative association between SUA levels and measures of lung function, such as forced vital capacity (FVC) and the first second of forced expiratory volume (FEV1) in individuals with COPD.8,11 Another study found no effect of SUA on lung function in the same population.12

For the population with normal lung structure, the effect of high SUA levels on lung function have been conflicting in cross-sectional studies; while a positive effect was found in a large Korean population (n=69,928) without any clinical diseases,6 a negative effect was observed in the Korean National Health and Nutrition Examination Survey,13 and also no significant effect was found in young adults aged 22–29 years.14

Current researchers have paid greater attention to differential effects of SUA on lung function stratified by smoking status15 or gender status,13 but no attention to respiratory disease status. To the best of our knowledge, this is the first epidemiological study focusing on the different effects of SUA on lung function in individuals with or without COPD. In currently available research, the relationships between SUA and lung function stratified by COPD status are not well-characterized for reasons of different populations and the heterogeneous analysis methods among others.

Based on this, our study aimed to identify the relationship between SUA and lung function in individuals with or without COPD.

Methods

Study Population and Blood Tests

Our study applied the baseline data set of a cohort study of people with chronic airway disease in Guangdong, China (ChiCTR1900024643), which was a population-based, multicenter randomized survey of COPD, conducted from June 2019 to June 2021. This study included people: 1) people aged over 30 years old; 2) people who had signed informed consent; 3) who returned complete COPD-related questionnaires; 4) who had undergone the standardized spirometry; 5) who had completed blood tests. Exclusion criteria were the following: 1) a history of malignancy; 2) acute inflammatory diseases or infectious diseases (such as pneumonia, bronchiectasis with infection and active pulmonary tuberculosis); 3) acute exacerbation of COPD within four weeks; 4) cardiovascular or chronic pulmonary diseases (such as hypertension, asthma, bronchiectasis, pneumoconiosis, and interstitial lung diseases), which can affect SUA levels.

Initially, a total of 3160 study subjects were considered as eligible subjects and included in our study. After excluding those without a laboratory examination (n=118), without a complete questionnaire (n=62) and lacking available spirometry data (n=183), 2797 participants were enrolled in our study (Figure 1). Invited participants were required to undergo anthropometric measurement, the spirometer examination, laboratory assessment, and also answered COPD related-questionnaires. The study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University (No.2018–53). All participants gave written informed consent. This present study was in line with the principles of the Declaration of Helsinki.

Figure 1 Study flow chart.

Blood samples were obtained from invited participants after 12 h of fasting. SUA levels were determined by the uricase-peroxidase method and by the creatinase-peroxidase method, respectively.16

Outcome Definitions

Invited participants were required to complete a questionnaire based on the questionnaires from the International Burden of Obstructive Lung Disease Study17 and a 2007 Chinese epidemiological study,18 that included potential risk factors for COPD and also chronic respiratory symptoms (such as cough, phlegm production, and dyspnoea). The technicians who were responsible for administering this questionnaire had been strictly trained and also passed a training test. The presence of cough was assessed with “Do you usually cough for three consecutive months or more per year for two years? ” Phlegm production was assessed with “Do you usually bring up phlegm for three consecutive months or more per year for two years?” Dyspnoea was assessed with “Have you had shortness of breath either when walking up a slight hill or brisk walking on the level?”

Lung Function Measures

Participants aged over 30 years were required to finish standardized spirometry. Participants who were physically incapable of taking standardized spirometry (ie, thoracic, abdominal, or eye surgery, retinal detachment or myocardial infarction in past three months; pregnant or breastfeeding; antibacterial chemotherapy for tuberculosis) were excluded.19 Before and after bronchodilator spirometries were performed by using a portable spirometer (CareFusion MasterScreen Pneumo, Germany) according to the European Respiratory Society/American Thoracic Society standards (ERS/ATS 2005).19 Manoeuvre of American Thoracic Society quality grade C or above were acceptable for analysis.20 Standardized spirometry was conducted during the summer, from 2019 to 2020. The diagnosis of COPD is defined by post-bronchodilator (Salbutamol Sulfate Aerosol, 400 μg, 20 min later) FEV1/FVC ratio of less than 0.70.21 The predicted value for FVC and FEV1 is calculated according to the Report Working Party Standardization of lung function tests,22 adjusted by an equation obtained in a representative Chinese population.23

Covariate Definitions

We collected demographic data, including sex, age, and also body index mass (BMI). Never smokers were defined as adults who reported having smoked less than 100 cigarettes in their lifetime. Current smokers were defined as adults who reported having smoked more than 100 cigarettes in their lifetime and also currently smoke some days or every day. Former smokers were defined as adults who reported having smoked more than 100 cigarettes in their lifetime but quit smoking more than three months.

Statistical Analyses

The normality of distribution of variables was evaluated with the Kolmogorov–Smirnov test. Continuous variables were exhibited as the mean ± SD when in a normal distribution, and as medians (interquartile ranges) when in a skewed distribution. Student’s t-test was applied to compare differences among individuals with and without COPD. Categorical variables were expressed as numbers (percentages), and the Chi-square test or Fisher’s exact test were used to assess the inter-group difference. Continuous SUA values were also transformed into categorical variables according to their terciles. The ANOVA test was applied to investigate the significant differences between different SUA- levels groups.

Binary logistic models were applied to investigate the relationships between the SUA levels, the presence of COPD, and the chronic respiratory symptoms (cough, phlegm production, and dyspnea), either adjusted or unadjusted sex, age, smoking status, cumulative tobacco smoking, and body mass index (BMI). To investigate the different effects of SUA on lung function, we also conducted a multivariate analysis among individuals with or without COPD. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated to estimate the strength of this association.

Multivariable adjustment linear models were implied to estimate the effect of SUA levels on FEV1%, FVC%, and FEV1/FVC. We also tested the assumptions of normality, linearity, and homoscedasticity graphically by using plots of observed versus predicted values as well as also plots of residuals versus predicted values or the observed exposure values. No major violations were found.

In the sensitivity analysis, the same analyses were performed in the different groups by smoking statuses to explore any differential effects of SUA based on smoking status. Analyses of the gender subgroups were also conducted. The relationship between SUA and spirometer measurement after bronchodilators was also estimated.

All tests were two-sided, and p-values less than 0.05 were considered statistically significant. Data were analyzed using R statistical software (version 4.1.0).

Results

Study Population

A total of 2797 participants who met inclusion criteria and had available data were enrolled in our study, including 1410 (50.41%) non-COPD subjects and 1387 (49.58%) COPD patients. The clinical characteristics and biochemical biomarkers of invited participants are presented in Table 1. Participants were divided into two groups based on their current status of COPD. Significant differences between the two groups were found, such as sex, age, BMI, smoking status, pre-bronchodilator spirometric values, and also chronic respiratory symptoms. Additionally, overall SUA levels were higher in individuals with COPD, as 4.17 ± 1.10 mg/dl, versus 3.79 ± 1.14 mg/dl in the non-COPD group (Table 1; Figure 2). Individuals with high SUA levels were older, with higher values of BMI, and more likely to be current smokers compared to individuals in the lowest SUA group. Those in the highest terciles were also more likely to have lower FEV1% predicted, lower FVC % predicted, and low FEV1/FVC. Compared to the lowest SUA tertiles, individuals in the two highest terciles were more likely to report a risk of cough, phlegm production, and also dyspnoea.

Table 1 The Association of Baseline Participant Characteristics with SUA and COPD (N=2797)

Figure 2 Serum uric acid levels in people with and without chronic obstructive pulmonary disease. ***p value less than 0.001.

Abbreviations: COPD, chronic obstructive pulmonary disease; Non-COPD, without chronic obstructive pulmonary disease.

Uric Acid and COPD

Unadjusted logistic regression analysis showed no significant effect of SUA on the prevalence of COPD (unadjusted OR,1.33; 95% CI 1.25 to 1.44) (Table 2). After multivariable adjustment, the OR (95% CI) of the prevalence of COPD was 1.15 (95% CI 1.06 to 1.25) with p-value less than 0.001 per 1 mg/dL increase of SUA (Table 2; Figure 3). Similar results were also found both in the never-smoker and ever-smoker groups (online supplementary Figure A1), but not in the female population (online supplementary Figure A2).

Table 2 Association Between SUA, Lung Function and Chronic Respiratory Symptom in People with or Without COPD

Figure 3 Association of SUA levels with study outcomes. Shown are odds ratio or estimate effect for each outcome for each 1 mg/dl increase in serum uric acid, adjusted for age, sex, BMI, smoking status, and cumulative tobacco consumption. Bold values means that all participants were in the analysis. *p value less than 0.05.

Abbreviations: COPD, chronic obstructive pulmonary disease; Non-COPD, without chronic obstructive pulmonary disease; FEV1% predicted, percent predicted forced expiratory volume in 1 s; FVC% predicted, percent predicted forced vital capacity; 95% CI, 95% confidence interval.

Uric Acid and Lung Function

After multivariable adjustment, each 1 mg/dl increase of SUA was associated with a 1.63% decrease in FEV1% predicted (95% CI −2.37 to −0.90) (Table 2; Figure 3; Figure 4). Each 1 mg/dl increase of SUA was significantly associated with a 1.87% (95% CI −2.91 to −0.84) decrease in FEV1% predicted, but no significant relationship was found in the non-COPD group (0.39%,95% CI −1.18 to 0.40). After multivariable adjustment, each 1 mg/dl increase of SUA levels was associated with a −0.89% decrease (95% CI −1.55 to−0.24) in FVC % predicted. Similar results were found in the COPD group, (−1.35% [95% CI −2.35 to −0.34]) but not in the non-COPD group (−0.42% [95% CI −1.26 to 0.43]). Additionally, after multivariable adjustment, each 1 mg/dL increase in SUA levels was associated with a −0.7% (95% CI −1.10 to −0.30) decrease in FEV1 /FVC. Each 1 mg/dl increase in SUA was associated with a 0.63% decrease in FEV1 /FVC in the COPD group, while no significant association between SUA levels and FEV1 /FVC was found in the Non-COPD group (p-value 0.987). The associations between SUA levels and lung function after using bronchodilators were also evaluated, with similar results were found (online supplementary Figure A3.).

Figure 4 Regression of lung function on SUA in people with or without COPD. The analysis was multi-variable adjusted for age, sex, BMI, smoking status, and cumulative tobacco consumption. Regression values in the top and 95% CIs were shown as the shaded area around the regression line.

Abbreviations: COPD, chronic obstructive pulmonary disease; Non-COPD, without chronic obstructive pulmonary disease; FEV1% predicted, percent predicted forced expiratory volume in 1 s; FVC% predicted, percent predicted forced vital capacity.

Uric Acid and Symptoms of Airway Disease

The OR of dyspnea was 1.12 (95% CI 1.05 to 1.21) with each 1 mg/dl higher SUA (Table 2; Figure 3). This association remained significant after adjustment for potential confounders. People with COPD had a higher risk of dyspnea than did those without COPD (adjusted ORs, 1.11 in the COPD group and 1.07 in the non-COPD group). No significant effect of SUA on dyspnoea was found in the non-COPD group (adjusted OR,1.00; 95% CI 0.88 to 1.13).

Discussion

This observational study analyzed 1387 individuals (49.58%) with COPD. Individuals with COPD had significantly higher SUA levels than did individuals without COPD (4.17 ± 1.10 vs 3.79 ± 1.14, respectively). In addition, we found that increased SUA levels were significantly associated with decreased in FEV1% predicted, FVC% predicted, and FEV1/FVC, and with increased risk of COPD as well as chronic respiratory symptoms. Negative associations between SUA and FEV1% predicted, FVC% predicted, and FEV1/FVC were found in the COPD group, but no significant association between lung function and SUA levels was found in the non-COPD group. To the best of our knowledge, this is the first epidemiological study focusing on the different effects of SUA on lung function based on individuals with or without COPD.

Cross-sectional studies have estimated that higher SUA levels were positively6 and inversely24 associated with lung function. Previous epidemiological results have been rather inconsistent whether in COPD populations or healthy populations. Two studies found that increased SUA levels accelerated lung function decline in COPD patients,12,25 while another found no significant effect of SUA on lung function in individuals with COPD.12 Similarly, the contradictory effect of SUA on lung function was found in individuals without COPD. In comparison, a positive effect was observed in a large Korean population (n=69,928) of healthy subjects,6 a negative effect was reported in the Korean National Health and Nutrition Examination Survey,13 and another analysis of young adults aged 22–29 years found no significant effect.14 With the heterogeneity of the above studies, such as in term of demographic data and statistical analysis, and so on, it is difficult to draw a clear relationship between SUA and lung function and to explore potential mechanisms, which may explain the discrepancy in current epidemiological studies.

The potentially different effects of SUA on lung function may depend on differential mechanisms. Shaheen suggested that interpretation of previous studies need to be careful26 and provided several possible mechanisms, such as the pro-oxidant and pro-inflammatory properties of SUA, a poor proxy for epithelial lining fluid concentrations, and also potential for confounding. Previous studies have demonstrated that SUA levels were inversely correlated with lung function in the female general population but not the male population.13,24 Though the cause of these sex differences between SUA and lung function remain uncertain, one study has suggested that sex hormones may affect SUA metabolism, making the relative health effect of SUA may be stronger in female generations.27 Further, our study provides a new insight to explain the contradictory relationship between SUA and lung function, the health effect of SUA levels on lung function, which is that health effect of SUA levels on lung function could be influenced by COPD status.

Previous experimental studies have estimated that high SUA levels do not affect reactive oxygen species levels, which can initiate inflammation or airway remodeling26,28–30 under normal conditions, and do not affect lung function under the same condition.9 Experimentally induced hypoxia models found that SUA levels were higher in hypoxia status compared to normal status in lung tissue,31 which means that hypoxia may promote purine catabolism,32,33 which could increase the levels of SUA, and those elevated SUA levels can cause systemic inflammation, potentially damaging lung function. A previous epidemiological study revealed that SUA levels were higher in people with more severe airflow limitation, and were also increased in the presence of hypoxia and systemic inflammation.25 Braghiroli et al suggested that compared to the healthy population, SUA levels were significantly increased in individuals with COPD in hypoxia status but not in those without.32 In a cross-sectional study, Nicks et al found that lower SUA levels were associated with COPD severity in the cross-sectional study.7

This is consistent with our findings; high SUA levels impaired the lung function in the COPD patients but not in non-COPD people with normal oxygen saturation.34,35 Although oxygen saturation values were not collected in our studies, we identified the positive correlation between the high SUA levels and the risk of dyspnoea. As people with the symptom of dyspnoea have different levels of hypoxia,36 that may support our assumptions. Meanwhile, further research is needed to explore the relationships among SUA, lung function, and oxygen saturation in respiratory disease, especially in COPD patients.

A variety of factors such as air pollution and smoking are suggested to have more influence on lung function in COPD patients and therefore have attracted significant attention. Quitting smoking and avoiding air pollution are important suggestions to prevent decreased lung function in COPD patients, but blood biomarkers such as higher SUA levels cannot be ignored. A meta-analysis demonstrated that SUA levels might be a useful biomarker for COPD,37 and an independent predictor of mortality, and are associated with a higher risk of acute exacerbation of COPD.25,38 For better management of COPD, further research about the effect of SUA on lung function, especially in COPD patients, is required.

The strengths of this study include its large sample size and also the amount of data available. Subjects in our study were enrolled from the community but not the clinic, without any severity underlying disease except COPD. Additionally, we were also able to analyze the effect of SUA on lung function after bronchodilation, which could not observed in the previous studies. Similar results were found when compared to SUA levels and lung function before bronchodilation.

Some limitations in our study should be considered. First, the population in our study consisted mostly of males (71.9%), and the percentage of females (28.1%) was lower than in other studies,6,39 which may have influenced the overall results. Nonetheless, the observed association between SUA and lung function persisted in a gender-adjusted model. Second, several possible factors that may influence SUA levels were not completely ruled out, including chronic kidney disorders, alcohol consumption, food intake, metabolic syndrome, and also cardiovascular disease. However, after adjustment for major confounders (age, gender, BMI, smoking status, and cumulative tobacco consumption), logistic regression analysis showed that SUA levels continued to be a significant predictor of COPD risk. Similar results were seen in the linear regression model. Based on this, we believe that the influence of biases from unknown confounding that the model did not adjust for did not significantly affect the outcome. Thirdly, though SUA levels have been suggested to be an imperfect proxy for epithelial lining fluid concentration,1 SUA from epithelial lining fluid concentration is thought to be secreted by submucosal nasal glands after uptake from plasma.3 Lastly, because the design of our study was retrospective and cross-sectional, the causal relationship between uric acid and lung function could not be determined.

Conclusion

In conclusion, the high SUA level was associated with a higher risk of COPD and chronic respiratory symptoms, and lower lung function. What’s more, significant effects of SUA on lung function were found in individuals with COPD, but not individuals without COPD.

Abbreviations

SUA, serum uric acid; COPD, chronic obstructive pulmonary disease; FEV1, forced expiratory volume in 1 second; FVC, forced vital capacity; BMI, body mass index; ORs, odds ratios.

Data Sharing Statement

With the permission of the corresponding authors, we can provide participant data without names and identifiers. The corresponding authors have the right to decide whether to share the data based on the research objectives and plan provided. Data will be immediately available after publication. No end date. Please contact correspondence author for data requests.

Ethics Approval and Informed Consent

The study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University. All participants gave written informed consent.

Consent for Publication

This article has not been published elsewhere in whole or in part. All authors have read and approved the content, and agree to submit it for consideration for publication in your journal. There are no ethical/legal conflicts involved in the article.

Acknowledgments

We thank all the participants who contributed to this study. Thanks are due to Zhishan Deng, Youlan Zheng, Lifei Lu, Ningning Zhao, Jianwu Xu, Peiyu Huang, Xiaopeng Ling, Shaodan Wei, Qiaoyi He, Wenjun Lai and Yunsong Chen (National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou. Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Nan shan Medical Development Foundation of Guangdong Province) for Data collection.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

The present study was supported by The National Key Research and Development Program of China (Grant number 2016YFC1304101), the Local Innovative and Research Teams Project of Guangdong Pearl River Talents Program (2017BT01S155), the National Natural Science Foundation of China (81970045), Zhongnanshan Medical Foundation of Guangdong Province (ZNSA2020003, ZNSA-2021012, and ZNSA-2020013) Basic and Applied Basic Research Fund of Guangdong Province (2020A 1515110915) and National Natural Science Foundation of China (82000044).

Disclosure

We declare that there are no financial or personal competing interests associated with the study.

References

1. van der Vliet A, Neill O, Cross CE, et al. Determination of low-molecular-mass antioxidant concentrations in human respiratory tract lining fluids. Am J Physiol. 1999;276(2):L289–L296. doi:10.1152/ajplung.1999.276.2.L289

2. Kelly FJ, Blomberg A, Frew A, et al. Antioxidant kinetics in lung lavage fluid following exposure of humans to nitrogen dioxide. Am J Respir Crit Care Med. 1996;154:1700–1705. doi:10.1164/ajrccm.154.6.8970358

3. Peden DB, Hohman R, Brown ME, et al. Uric acid is a major antioxidant in human nasal airway secretions. Proc Natl Acad Sci U S A. 1990;87:7638–7642. doi:10.1073/pnas.87.19.7638

4. So A, Thorens B. Uric acid transport and disease. J Clin Invest. 2010;120(6):1791–1799. doi:10.1172/JCI42344

5. Lyngdoh T, Marques-Vidal P, Paccaud F, et al. Elevated serum uric acid is associated with high circulating inflammatory cytokines in the population-based Colaus study. PLoS One. 2011;6(5):e19901. doi:10.1371/journal.pone.0019901

6. Song JU, Hwang J, Ahn JK. Serum uric acid is positively associated with pulmonary function in Korean health screening examinees. Mod Rheumatol. 2017;27:1057–1065. doi:10.1080/14397595.2017.1285981

7. Nicks NE, O’Brien MM, Bowler RP. Plasma antioxidants are associated with impaired lung function and COPD exacerbations in smokers. COPD. 2011;8:264–269. doi:10.3109/15412555.2011.579202

8. Kobylecki CJ, Vedel-Krogh S, Afzal S, et al. Nordestgaard, Plasma urate, lung function and chronic obstructive pulmonary disease: a Mendelian randomisation study in 114 979 individuals from the general population. Thorax. 2018;73(8):748–757. doi:10.1136/thoraxjnl-2017-210273

9. Fujikawa H, Sakamoto Y, Masuda N, et al. Higher blood uric acid in female humans and mice as a protective factor against pathophysiological decline of lung function. Antioxidants. 2020;9(5):387. doi:10.3390/antiox9050387

10. Elsayed NE, Nakashima JM, Postlethwait EM. Measurement of uric acid as a marker of oxygen tension in the lung. Arch Biochem Biophys. 1993;302(1):228–232. doi:10.1006/abbi.1993.1204

11. Kahnert K, Alter P, Welte T, et al. Uric acid, lung function, physical capacity and exacerbation frequency in patients with COPD: a multi-dimensional approach. Respir Res. 2018;19(1):110. doi:10.1186/s12931-018-0815-y

12. Garcia-Pachon E, Padilla-Navas I, Shum C. Serum uric acid to creatinine ratio in patients with chronic obstructive pulmonary disease. Lung. 2007;185(1):21–24. doi:10.1007/s00408-006-0076-2

13. Jeong H, Baek SY, Kim SW, et al. Gender-specific association of serum uric acid and pulmonary function: data from the Korea National Health and Nutrition Examination Survey. Medicine. 2021;57:953.

14. Garcia-Larsen V, Chinn S, Rodrigo R, et al. Relationship between oxidative stress-related biomarkers and antioxidant status with asthma and atopy in young adults: a population-based study. Clin Exp Allergy. 2009;39(3):379–386. doi:10.1111/j.1365-2222.2008.03163.x

15. Horsfall LJ, Nazareth I, Petersen I. Serum uric acid and the risk of respiratory disease: a population-based cohort study. Thorax. 2014;69(11):1021–1026. doi:10.1136/thoraxjnl-2014-205271

16. Domagk GF, Schlicke HH. A colorimetric method using uricase and peroxidase for the determination of uric acid. Anal Biochem. 1968;22(2):219–224. doi:10.1016/0003-2697(68)90309-6

17. Buist AS, McBurnie MA, Vollmer WM, et al. International variation in the prevalence of COPD (the BOLD Study): a population-based prevalence study. Lancet. 2007;370(9589):741–750. doi:10.1016/S0140-6736(07)61377-4

18. Zhou Y, Hu G, Wang D, et al. Community based integrated intervention for prevention and management of chronic obstructive pulmonary disease (COPD) in Guangdong, China: cluster randomised controlled trial. BMJ. 2010;341(2):c6387. doi:10.1136/bmj.c6387

19. Miller MR, Hankinson J, Brusasco V, et al. Standardisation of spirometry. Eur Respir J. 2005;26:319–338. doi:10.1183/09031936.05.00034805

20. Enright PL, Studnicka M, Zielinski J. Spirometry to detect and manage chronic obstructive pulmonary disease and asthma in the primary care setting. Eur Respir Mon. 2005;31:1–14.

21. Vogelmeier CF, Criner GJ, Martinez FJ, et al. Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease 2017 Report: GOLD executive summary. Am J Respir Crit Care Med. 2017;195:557–582. doi:10.1164/rccm.201701-0218PP

22. Quanjer PH, Tammeling GJ, Cotes JE, et al. Lung volumes and forced ventilatory flows. Eur Respir J. 1993;16:5–40.

23. Zheng J, Zhong N. Normative values of pulmonary function testing in Chinese adults. Chin Med J. 2002;115(1):5054.

24. Aida Y, Shibata Y, Osaka D, et al. The relationship between serum uric acid and spirometric values in participants in a health check: the Takahata study. Int J Med Sci. 2011;8(6):470–478. doi:10.7150/ijms.8.470

25. Bartziokas K, Papaioannou AI, Loukides S, et al. serum uric acid as a predictor of mortality and future exacerbations of COPD. Eur Respir J. 2014;43(1):43–53. doi:10.1183/09031936.00209212

26. Shaheen SO. Uric acid, lung function and COPD: a causal link is unlikely. Thorax. 2018;73(8):697–698. doi:10.1136/thoraxjnl-2017-211230

27. De Vera MA, Rahman MM, Bhole V, et al. The independent impact of gout on the risk of acute myocardial infarction among elderly women: a population-based study. Ann Rheum Dis. 2010;69:1162–1164. doi:10.1136/ard.2009.122770

28. McNeil JD, Wiebkin OW, Betts WH, et al. Depolymerisation products of hyaluronic acid after exposure to oxygen-derived free radicals. Ann Rheum Dis. 1985;44:780–789. doi:10.1136/ard.44.11.780

29. Uchiyama H, Dobashi Y, Ohkouchi K, et al. Chemical change involved in the oxidative reductive depolymerization of hyaluronic acid. J Biol Chem. 1990;265:7753–7759. doi:10.1016/S0021-9258(19)38993-8

30. McKee CM, Penno MB, Cowman M, et al. Hyaluronan (HA) fragments induce chemokine gene expression in alveolar macrophages. The role of HA size and CD44. J Clin Invest. 1996;98:2403. doi:10.1172/JCI119054

31. Ozanturk E, Ucar ZZ, Varol Y, et al. Urinary uric acid excretion as an indicator of severe hypoxia and mortality in patients with obstructive sleep apnea and chronic obstructive pulmonary disease. Rev Port Pneumol. 2016;22:18–26. doi:10.1016/j.rppnen.2015.06.002

32. Braghiroli A, Sacco C, Erbetta M, et al. Overnight urinary uric acid: creatinine ratio for detection of sleep hypoxemia. Validation study in chronic obstructive pulmonary disease and obstructive sleep apnea before and after treatment with nasal continuous positive airway pressure. Am Rev Respir Dis. 1993;148:173–178. doi:10.1164/ajrccm/148.1.173

33. Saito H, Nishimura M, Shibuya E, et al. Tissue hypoxia in sleep apnea syndrome assessed by uric acid and adenosine. Chest. 2002;122(5):1686–1694. doi:10.1378/chest.122.5.1686

34. Sundh J, Ekström M. Risk factors for developing hypoxic respiratory failure in COPD. Int J Chron Obstruct Pulmon Dis. 2017;12:2095–2100. doi:10.2147/COPD.S140299

35. Wells JM, Estepar RS, McDonald MN, et al. Clinical, physiologic, and radiographic factors contributing to development of hypoxemia in moderate to severe COPD: a cohort study. BMC Pulm Med. 2016;16:169. doi:10.1186/s12890-016-0331-0

36. Higashimoto Y, Honda N, Yamagata T, et al. Exertional dyspnoea and cortical oxygenation in patients with COPD. Eur Respir J. 2015;46(6):1615–1624.

37. Li H, Chen Y. Serum uric acid level as a biomarker for chronic obstructive pulmonary disease: a meta-analysis. J Int Med Res. 2021;49(1):300060520983705. doi:10.1177/0300060520983705

38. Zhang X, Liu L, Liang R, Jin S. Hyperuricemia is a biomarker of early mortality in patients with chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis. 2015;10:2519–2523. doi:10.2147/COPD.S87202

39. Fukuhara A, Saito J, Sato S, et al. The association between risk of airflow limitation and serum uric acid measured at medical health check-ups. Int J Chron Obstruct Pulmon Dis. 2017;12:1213–1219. doi:10.2147/COPD.S126249

Source link


We could not process your request. Please try again later. Please contact if you still have this problem [email protected].

The Healio Editors compiled the most read news in pulmonology, which was published in April.

Highlights include a study on COPD misclassification in older adults; new electronic nose technology for diagnosing sarcoidosis; news from the Society of Critical Care Medicine Congress; and more.



Source: Adobe Stock.

Read these articles and others below, in no particular order.

Prolonged lung abnormalities common 1 year after COVID-19 pneumonia

Long-term CT abnormalities were common up to 1 year after COVID-19 pneumonia in a study published in Radiology. Read more

COPD misclassification common in older adults

Among older adults, COPD misclassification is high and is associated with higher respiratory symptom burden, health utilization, and lower physical performance compared to the general population. Read more

Inhalation therapy receives FDA breakthrough designation for bronchiectasis of non-cystic fibrosis

The FDA granted pioneering treatment designation for colistimethate sodium powder for nebulization solution to reduce pulmonary exacerbations in adults with non-cystic fibrosis bronchiectasis and Pseudomonas aeruginosa. Read more

Electronic nose technology can facilitate accurate diagnosis of sarcoidosis

Electronic nose technology accurately distinguished patients with sarcoidosis from those with interstitial lung disease and healthy controls, researchers in Breast. Read more

Burnout, stress, insecurity: Pandemic reaps widespread effects on intensive care units

Taking care of the professional is key during the COVID-19 pandemic. This was the topic of a panel discussion at the Society of Critical Care Medicine Congress. Read more

Bronchiolitis associated with respiratory disease that persists into young adulthood

Long-term outcomes of early childhood bronchiolitis include a higher incidence of asthma and more obstructive lung function patterns in young adulthood, researchers in BMJ Open Respiratory Research. Read more

Sleep apnea-related mortality has increased continuously in black men in the United States over the past 2 decades

A recent study published in Sleep medicine highlights a trend in sleep-related mortality and associated cardiovascular disease among black men in the United States Read more

Respiratory muscle wasting associated with mortality in patients with severe COVID-19

The presence of respiratory muscle wasting in patients with severe COVID-19 was associated with increased mortality in inpatients, according to a study presented at the Society of Critical Care Medicine Congress. Read more

E-cigarettes contribute to young people’s failed attempts to quit nicotine

A recent study published in JAMA highlights the prevalence of failed cessation attempts among young people who use either e-cigarettes or cigarettes over the past 13 years. Read more

Racial, ethnic differences continue in acute use of pediatric asthma treatment

A new study highlights different patterns of use of clinic and acute ED for asthma among non-Spanish black and bilingual Latino children compared to non-Spanish white children. Read more



Post Views:
23

Source link

Doctors and scientists see a direct link between lung function and quality of life, yet lung health often declines with age. As a caregiver for an older adult, it’s incredibly important to do everything you can to keep your loved one’s lungs healthy, but poor air quality, allergies, existing disease and the general effects of aging can work against us. 

After all, the lungs can be greatly affected by changes brought about by aging: Thinning bones can change the shape of the ribcage and impact the ability to expand and contract during breathing. Muscles that support breathing may also become weakened, lowering the oxygen level in the body. Changes to lung tissue may also cause airways to close easily, and air sacs can lose their shape, making it hard to breathe. 

On top of those changes, seasonal allergies can also get worse with age and give rise to a condition known as geriatric rhinitis—mucus and discomfort in the nose due to aging. In fact, mucus buildup (also known as phlegm or sputum) is a common symptom in chronic lung diseases such as COPD (including chronic bronchitis and emphysema), cystic fibrosis, bronchiectasis, nontuberculous mycobacteria (NTM) lung disease or asthma. 

This mucus buildup is hard to avoid, too: The World Health Organization (WHO) recently reported that 99% of all people in the world breathe air that exceeds WHO quality limits and threatens their health. Nitrogen dioxide, found in higher levels in unhealthy air, is associated with respiratory diseases, particularly asthma, and leads to respiratory symptoms such as coughing, wheezing or difficulty breathing, hospital admissions and visits to emergency rooms.

All this paints a dire picture for seniors, but the good news is that older adults don’t need to suffer through breathing issues or be forced to turn to potentially dangerous prescription medications. In fact, certain herbs and supplements may help reduce mucus buildup in the lungs and protect against the ill effects of phlegm buildup. 

Introducing mullein

A common plant with furry leaves and small yellow flowers, mullein is an herb with a long history of use for many respiratory diseases. The leaves can relieve irritation in the body’s mucus membranes in the nose, mouth and throat and can also work as an expectorant, which thins and loosens phlegm—breaking up congestion associated with a cold or other respiratory problems, just like a modern cough medicine.

“Leaves from mullein are helpful with lung congestion and mucus production,” writes Suzy Cohen, a registered pharmacist, in the Marco Eagle. “It appears to work by dilating capillaries and therefore increasing circulation. This helps relieve stagnancy and congestion, making it an interesting adjunctive remedy to people with COPD, bronchitis, asthma and dry coughs.”

While it’s possible to harvest and produce your own mullein leaf teas and tinctures, BetterLungs by Betterbrand thankfully offers a modern-day version of the mullein leaf herbalists have used for decades. Designed to support healthy respiratory and immune systems, preserve stamina and manage mucus, these pills contain mullein leaf, elderberry (used for traditional immune support), vitamin D and other beneficial herbs. No prescription is necessary, and users rave about improvements to breathing and lack of stuffy noses.

“I can’t believe how this cleared up my breathing on the first day,” Charlene P. commented. “I stopped coughing all the time and feel a whole lot better!”

The company recommends two capsules each morning for the typical senior looking for complete respiratory and immune support, or four capsules (two in the morning and two in the evening) for those who may need additional support, including smokers, travelers and those who especially suffer from seasonal changes. A 30-day supply (60 capsules) is $34.95, but those who wish to subscribe to a monthly delivery can save 15%.

The company also offers BetterLungs Immunity with IMMUSE, designed to reduce fatigue from exercise and help with work productivity by supporting the immune system. 

Learn more about BetterLungs and the other products by Betterbrand at trybetterbrand.com.

Supporting healthy lungs

Working with your loved one to support and maintain lung function can be a major component in ensuring they live long, happy, comfortable lives. A combination of supplements, exercise and a healthy environment is a great way to proactively fend off the natural toll aging takes on the lungs. 

Reduce exposure to triggers

Decrease exposure to allergy triggers by taking note of your loved one’s environment during flare-ups. Staying indoors with closed windows is the best way to avoid triggers such as pollen, grass and pine trees. Inside, try to eliminate dust and mold buildup. Fans and air purifiers can make a difference in improving circulation and eliminating allergens. Your loved one should also bathe and change clothes after being outdoors in allergy season.

Source link

BARROVIANS living with long-term respiratory conditions can now access ‘Breathe Easy’ groups based in the community.

The groups are an Integrated Care Community (ICC) initiative supported by volunteers and are affiliated with Morecambe Bay Respiratory Network and Asthma + Lung UK.

They will offer people with respiratory conditions such as asthma, Chronic Obstructive Pulmonary Disease (COPD), bronchiectasis and Interstitial Lung Disease advice and support from health and care professionals.

The groups are open to anyone aged 18 and above, giving them the chance to share their experiences and learn from others who are living with the same or a similar long-term respiratory condition.

The sessions are free, no appointment or referral is needed and friends, family and carers are also welcome.

As well as in Barrow, groups have launched in Morecombe, Lancaster, and Kendal.

Dr Pat Haslam, founder and Clinical Lead of Morecambe Bay Respiratory Network, said: “We're so excited to see the Integrated Breathe Easy groups start up across the Bay. They are a fundamental part of improving the quality of life of our patients with respiratory disease.

“There is evidence to show that patients who attend these groups have improved quality of life, reduced admissions to hospital and improved knowledge of how to self-manage their condition. We are looking forward to seeing more groups across the Bay so that all of our patients can benefit from them."

The Furness Breathe Easy group will relaunch on June 15 at St Mary’s Living Well Centre on Duke Street.

Anji Stokes, Development Lead for Bay ICC, added: "Breathe Easy will be great for our area. The sessions provide an opportunity for people with lung conditions to hear from local specialist speakers in an informal environment with plenty of opportunities for questions, the sessions are led by two volunteers and people use their own experiences to help and support each other.

“If you are over 18 and have a lung condition or are supporting someone with a lung condition, I would encourage you to attend."



Source link

Introduction

Despite advances in symptomatic management of chronic obstructive pulmonary disease (COPD) with new combination inhalers and in therapeutic options for patients with severe emphysema such as endobronchial valves, patients with COPD still have significant morbidity and mortality.1 Patients newly diagnosed with early COPD tend to have variable disease courses that remain difficult to predict even with readily available in-office spirometry.2 The healthcare burden of COPD in the US is significant and exacerbations account for $18 billion in direct costs annually.3 Thus, there is a pressing need to define clinically meaningful subtypes in COPD that better categorize patients with vastly different disease trajectories to identify those at risk for accelerated lung function decline and, ultimately, improve disease outcomes with targeted therapies.

The Fleischner Society published a statement in 2015 detailing computed tomography (CT) subtypes based on visual and quantitative evaluation of images to classify emphysema as well as other important features such as airway wall thickening, inflammatory small airways disease, interstitial abnormalities, and bronchiectasis.4 The combination of visual assessment and quantitative metrics can further help identify COPD phenotypes and provide information on disease progression and mortality.5 Commercially available software using novel radiographic features has become increasingly available to further aid in COPD subtyping. The use of quantitative CT (QCT) imaging has been harnessed by several companies discussed in detail in this review. These software packages can assess changes in airway architecture, vascular morphology, and parenchymal density on inspiratory and expiratory scans to measure the extent of emphysema, air trapping, and functional small airways disease (fSAD).

Search Strategy and Selection Criteria

The intention of this review is to present an overview of commercial platforms that provide quantitative CT applications for improving COPD subtyping. While there are many software platforms that offer this capability, those described here were selected based on our experience and communications with members of the COPDGene and SPIROMICS studies as well as with clinicians from other institutions. We searched published articles reported on the websites of VIDA, Imbio, Thirona, FLUIDDA, 4D Medical, and CoreLine, the companies that produce the platforms we review. We also searched terms such as “COPD” and “computed tomography,” company names (“VIDA,” “Imbio,” “Thirona,” “FLUIDDA,” “4D Medical,” “CoreLine”), and different combinations of these terms for all fields on PubMed and Web of Science before December 4, 2021. Only clinical studies using non-contrast QCT were included in this review; as such, the terms “preclinical” and “contrast enhanced” were used for exclusion criteria. All articles were published in English and related to COPD, QCT, and clinical studies. We excluded some articles whose resources were not available. We evaluated reviews and original research in this area, then cited relevant articles.

Quantitative Analysis of Computed Tomography

X-ray CT, with its high spatial resolution and air-soft tissue contrast, is used extensively in the clinical management of COPD patients. For radiographic assessments, thoracic radiologists routinely use the extensive array of analytical techniques that have been developed to improve the diagnostic and prognostic value of QCT.6,7 Measurements of low attenuation areas are by far the most extensively used readouts for quantifying obstructive regions of the lungs. When applying a threshold of <950 Hounsfield Units (HU) to CT scans acquired at inspiration (ie, full inflation), this quantitative index, presented as the relative volume of the lung parenchyma, has been pathologically validated as a measure of emphysema.8,9 Similar strategies have been applied to expiratory CT scans to assess the extent of air trapping, a hallmark of small airways disease (SAD).10 Spatially aligned paired CT scans acquired at different inflation levels have provided readouts of ventilation, ventilation heterogeneity, and quantification of SAD when emphysema is present.11–13 The high air-tissue contrast on the inspiration CT scan has also been exploited to develop methods for airway and vessel measurements, as well as fissure completeness.14–17 Combined, these analytical techniques provide detailed quantitative information on airway and vessel remodeling and alterations in local parenchyma.

Commercial Platforms

FLUIDDA

FLUIDDA, founded in 2005 and based primarily in Belgium, has harnessed the power of functional respiratory imaging (FRI) through the Broncholab platform. Using high-resolution CT (HRCT), FRI can create 3-dimensional airway models for computational fluid dynamics simulations.18 In addition to FRI, Broncholab provides additional QCT-based metrics that include lobe volumes and densities, emphysema scores, and pulmonary airway and vascular measurements (Figure 1).

Figure 1 Representative clinical report of blood vessel density with coronal image and summary statistics from a COPD patient,courtesy of FLUIDDA.

Researchers have demonstrated how FRI can be used to understand the heterogeneity of COPD. Among studies of exacerbations, van Geffen et al19 and Hajian et al20 used FRI to assess regional heterogeneity by looking at hyperinflation, airway diameter, and resistance, both during a COPD exacerbation and after resolution. Improvements in hyperinflation and airway resistance correlated to improved quality-of-life and pulmonary function testing (PFT) metrics, suggesting that therapy should focus on decreasing airway resistance, mostly distally, during exacerbations. FRI could further visualize variability in ventilation and airway resistance among subjects during exacerbations. In addition, FRI revealed changes in airway structure and volume in different regions of the lungs, including both central and distal airways,21 after inhaling a combination of a long-acting beta agonist (LABA) and inhaled corticosteroid (ICS).22 De Backer et al23 later demonstrated that administration of inhaled extra fine beclomethasone/formoterol with a lower ICS dose improved lung function and hyperinflation. Furthermore, FRI identified regional changes in medication deposition not detected by spirometry. More recently, this group found the combination long-acting muscarinic antagonist (LAMA) glycopyrrolate and LABA formoterol improved airway volumes and resistance, as measured by FRI, as well as forced expiratory volume in 1 second (FEV1), inspiratory capacity (IC), and hyperinflation. These findings support the use of dual bronchodilator therapy in patients with moderate-to-severe COPD.24,25 Orally administered roflumilast further reduced areas of hyperinflation, suggesting its ability to redistribute ventilation, which could enhance concomitant inhaler use.26 FRI parameters also predicted COPD exacerbations using machine learning algorithms in a cohort of 62 patients.27 Eleven baseline FRI parameters, specifically further decreases in airway volumes leading to higher airway resistances in chronically narrowed airways, could predict an impending exacerbation. No other clinical data, notably PFTs, had this predictive power.27 Most recently, Cahn et al28 showed that the phosphoinositide 3-kinase δ (PI3Kδ) inhibitor nemiralisib, combined with standard of care, helped patients recover from exacerbations and led to improved respiratory parameters, including FEV1 and distal-specific imaging airway volume, over a 28-day period and was well tolerated.

Apart from studies of COPD exacerbations, FRI is also being used to assess whether non-invasive ventilation (NIV) has the long-term benefit of improving oxygenation and/or chronic hypercarbia in patients with severe COPD.29 In patients treated with at least 6 months of NIV, mass flow was redistributed to areas of the lung with better perfusion and less emphysema to improve ventilation-perfusion matching and recruit previously occluded small airways. Patients had improved gas exchange, 6-minute walk distance (6MWD), and anxiety.30 These results suggest that patients with SAD may benefit from long-term NIV use. In patients with severe COPD with secondary pulmonary hypertension, inhaled nitric oxide caused pulmonary vasodilation as measured by increases in vessel volume through FRI. While subjective improvements in dyspnea were seen, long-term data is not yet available.31

VIDA Diagnostics

VIDA Diagnostics Inc., founded in 2004 and headquartered in Coralville, IA, has developed the Apollo Pulmonary Evaluation Software, their flagship QCT application. VIDA provides advanced algorithms for airway wall measurements, ventilation maps, and disease probability maps (DPM) obtained from the spatial alignment of paired CT scans at varying inflation levels, and Topographic Multi-Planar Reformat (tMPR) that displays an optimized view of non-overlapping airways in context with surrounding tissue (Figure 2). VIDA, using Apollo, serves as the image analysis core for COPDGene (Genetic Epidemiology of COPD), ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints), SPIROMICS (SubPopulations and InteRmediate Outcome Measures in COPD Study), SARP (Severe Asthma Research Group), and the MESA (Multi-Ethnic Study of Atherosclerosis) Lung Study.

Figure 2 Representative clinical report (left) and tMPR (Topographic Multi-Planar Reformat; right) with air trapping map, courtesy of VIDA Diagnostics, Inc.

In a study of current or former smokers with preserved spirometry who were symptomatic as measured by the COPD Assessment Test (CAT), VIDA software detected submillimeter increases in airway wall thickening compared to asymptomatic current and former smokers.32 Differences in airway anatomy on CT scans from the MESA Lung Study and SPIROMICS were associated with COPD development and subjects with the accessory sub-superior airway, the most common airway branch variant, were at higher risk.33 Those with the second most common variant, absence of the right medial-basal airway, had a familial FGF10 gene inheritance pattern, and were at increased risk of developing COPD, dyspnea, and smaller airway lumens. Central airways collapse greater than 50% of the airway lumen during exhalation, ie, expiratory central airway collapse (ECAC), was associated with worse St. George’s Respiratory Questionnaire (SGRQ) and Medical Research Council Scale (mMRC) scores in smokers with and without COPD in the COPDGene Study.34 Emphysema affects subsegmental airway anatomy and airflow obstruction, likely related to loss of airway tethering,35 and these changes in wall area correlated to the chronic bronchitis phenotype.36 Kirby et al37 quantified degree of fSAD using DPM in the longitudinal Canadian Chronic Obstructive Lung Disease (CanCOLD) study by registering full inspiration and expiration CT images to classify each voxel as emphysematous, gas trapping, or normal. DPM measurements were associated with PFTs, bronchodilator response, and symptoms, particularly dyspnea, as measured by the mMRC.

Kirby’s team has also used VIDA’s airway segmentation tool to evaluate total airway count (TAC), airway inner diameter, and wall area in CT scans from the CanCOLD Study.38 GOLD I and GOLD II subjects had reduced TAC and thinner airway walls with narrower lumens than never smokers and at-risk individuals. Since airway remodeling can be associated with declining lung function, such changes may serve as biomarkers to predict individuals at risk for accelerated disease progression. In two other cohorts –one from SARP and another from SPIROMICS– differences in airway structures of asthma and COPD subjects with post-bronchodilator FEV1 <80% were assessed.39 COPD patients had more severe emphysema, SAD, and reduced tissue fraction and regional lung deformation compared to asthmatics, with greatest differences in upper and middle lobes.

For patients with advanced emphysema undergoing interventional procedures, Apollo was used to evaluate fissure completeness in subjects undergoing bronchoscopic thermal vapor ablation, an alternative to valve placement due to collateral ventilation.40 Apollo showed that a target lobe volume reduction (TLVR) of about 50% would give patients improved quality of life and lung function following endoscopic valve therapy.41 Apollo software revealed QCT characteristics, such as low attenuation cluster (LAC) that reflect the size of the “emphysematous holes,” predictive of subjects who would respond positively to lung volume reduction with coils42 and valves.43 Beyond airway analysis, Apollo was used for vessel segmentation in the MESA study to demonstrate that peripheral total pulmonary vascular volume was greater after long-term black carbon exposure, suggesting air pollution could affect vascular remodeling, and, ultimately, gas exchange.44

Thirona

Thirona, headquartered in Nijmegen, Netherlands, and founded in 2014, develops artificial intelligence software products focusing on thoracic CT imaging. Thirona’s commercial software LungQ is capable of quantifying anatomical volumes, disease distribution, airway and vascular morphology, and fissure completeness.

Early studies using Thirona’s technology included work by Boueiz et al45 that evaluated lobar distribution of emphysema in the COPDGene cohort. They found that subgroups of smokers with upper-lobe predominant emphysema had greater disease progression over a 5-year period, gas trapping, and dyspnea. Thirona’s LungQ segmentation protocol was used in a separate study of COPDGene subjects to approximate the total lung capacity-adjusted lung density at the 15th percentile of predicted (TLC-PD15) as a means of monitoring disease progression.46 In smokers at risk for developing COPD, LungQ showed that lung tissue density increased over 5 years, suggesting ongoing inflammation and airway remodeling. In contrast, end-stage COPD patients (GOLD III or IV) had loss of TLC-PD15 over time, thus displaying a more classic picture of progressive emphysema and tissue destruction. Over the same 5-year period, smokers with and without COPD had increased evidence of emphysema and air trapping, but these radiographic findings accounted for less than half the decline in FEV1 in GOLD stages II–IV.47 The role of inflammation in airway wall thickening was validated by Charbonnier et al,48 who found that, in the COPDGene cohort, higher airway wall thickness (Pi10) was associated with worse lung function, 6MWD, and SGRQ scores in all GOLD stages. Further, subjects who quit smoking had lower Pi10 between their first visit and at 5-year follow-up; in contrast, Pi10 increased in subjects who started smoking, suggesting a reversible component of smoking-related inflammation. Most recently, Bodduluri et al49 confirmed that progressive airway narrowing and remodeling in COPD could be quantified by the CT imaging-derived ratio of airway luminal surface area to volume (SA/V) using Thirona’s airway quantification software. SA/V increased with airway narrowing and decreased with airway loss. Overall, subjects with predominantly airway loss had worse survival, although both changes were associated with increased respiratory morbidity.

In the therapeutic sphere, Thirona’s LungQ software for evaluating fissure completeness was used to identify the TLVR for bronchoscopic lung volume reduction with endobronchial valves, similar to VIDA’s technology. This study found that a minimal difference of –563 mL in a patient provided a clinical benefit.50 Measurements of lobar oxygen uptake also helped identify the least functional, and therefore target lobe for valve placement.51 Patients from the Lung Volume Reduction Coil Treatment in Patients with Emphysema (RENEW) Trial with significant hyperinflation (residual volume >200% predicted) had the best clinical outcomes when QCT analysis was used to identify lobar treatment location and adequate emphysema (>20%) for endobronchial coil treatment.52

Imbio

Imbio, a Minneapolis, US-based medical imaging software company founded in 2012, uses imaging biomarkers to enhance personalized medicine. Imbio’s Lung Density Analysis (LDA) software identifies key functional information from CT scans in COPD patients. The functional LDA image analysis tool maps regions of functionally healthy lung, air trapping, and emphysema. LDA uses parametric response mapping (PRM) to produce a voxel-wise map to pair inspiratory and expiratory CT scans to quantify fSAD (Figure 3). In addition to PRM, LDA includes other QCT metrics such as emphysema scores and lobe volume and density measurements.

Figure 3 Representative clinical report that contains PRM images in all orientations and summary statistics from a COPD patient, courtesy of Imbio.

Various studies have evaluated the efficacy of PRM as an accurate readout of SAD. These radiographic regions of fSAD pathologically correspond to areas of lung tissue with loss of terminal bronchioles, airway lumen narrowing, and obstruction identified by microscopic examination of resected lung. PRM is the only technique that has been histologically validated to date.13 An early study using PRM (2012), which analyzed data from 194 COPD subjects from COPDGene, showed that this technology can provide regional information on disease activity and, notably, that fSAD often preceded the development of emphysema.11 This association was more evident in subjects in the COPDGene cohort with mild- to moderate-stage COPD, where worsening PRM-derived fSAD (PRMfSAD) was also associated with declining FEV1 and diffusing capacity for carbon monoxide (DLCO).53,54 PRM metrics have been proven to be strongly associated with both the development and severity of COPD when compared to other biomarkers of emphysema, including expiratory-to-inspiratory ratio of mean lung density (MLD), an indirect measure of air trapping, and Perc15 (the Hounsfield Units [HU] where less than 15% of the voxels on an inspiratory CT are found).55

PRM, as part of the LDA platform, was used to analyze CT scans from the SPIROMICS study. In a 3-year follow-up of 1,105 COPD subjects from SPIROMICS, those who suffered from exacerbations during this time had greater small airways abnormalities as defined by PRMfSAD.56 As subjects progressed in their disease courses, emphysema increased as expected; however, this change was associated with a decrease in mean PRMfSAD values, suggesting that fSAD may be a transitional period between normal lung parenchyma and development of irreversible emphysema. fSAD may, therefore, offer a promising indicator for early, directed treatment.57 Of note, aging in and of itself, in both smokers and ever-smokers without airflow obstruction, was found to increase PRMfSAD in an analysis of 580 SPIROMICS subjects.58 This PRM approach was also successfully used in smokers without COPD, where it showed that these metrics can identify unique patterns of progression, and that both PRMfSAD and PRMEMPH, ie, PRM-derived emphysema, can independently predict future development of emphysema.59,60 Bronchiectasis was also found to be associated with increased emphysema in smokers and those with both bronchiectasis and emphysema had lower FEV1 and 6MWD.17 In current or former smokers, the low attenuation area on low-dose CT imaging ordered as part of routine lung cancer screening may be able to detect quantitative emphysema and diagnose subjects with early COPD, allowing clinicians to monitor patients closely and perhaps initiate appropriate treatment when needed.61 These findings suggest that PRM could allow clinicians to further understand the variable disease progression of COPD, resulting in better-tailored treatments.

Coreline

Coreline, based in Seoul, Korea and founded in 2012, has developed the AI-based technology AVIEW COPD. This commercial platform includes quantitative analyses and visualization software that performs automated segmentation to evaluate emphysema, SAD, pulmonary vasculature and airways, and fissure integrity.

At the 2018 annual meeting of the Radiological Society of North America, Coreline presented a voxel-by-voxel segmentation using a 2.5D convolutional neural net and compared it to their gold standard semi-automated algorithm, which uses the airway segmentation module of AVIEW and requires additional processing by research assistants. This AI technology, AVIEW Metric, was found to be practical and reliable when tested on inspiratory CT scans of both healthy subjects and subjects with COPD from the Korean Obstructive Lung Disease (KOLD) study.62 By fully automating various image analysis algorithms, AVIEW Metric segmented all airways in a few minutes and allowed for inspiratory and expiratory lung registration. Researchers have also shown the potential of AVIEW technology for classifying COPD phenotypes. Kwon et al63 assessed how ambient air pollution may predispose subjects to different phenotypes in a study of a Korean cohort of 457 subjects with and without COPD. Using AVIEW software to measure spirometry (FEV1, FVC [forced vital capacity]), degree of emphysema, airway wall thickness, and fSAD, this group evaluated the association of these measurements to the average concentration of environmental particulate matter less than or equal to 10 µm (PM10) in diameter and nitrogen dioxide (NO2). While imaging phenotypes were not associated with NO2, increased exposure to PM10 was associated with lower FVC, increased emphysema, and airway wall thickness. AVIEW software revealed no associations between fSAD and air pollutants. Elsewhere, use of QCT emphysema air-trapping composite (EAtC) maps from AVIEW segmentation software correlated to GOLD staging and lung function, which researchers are studying as a potential biomarker of disease progression.64 Most recently, AVIEW has been used to analyze longitudinal changes over a 6-year period in pulmonary vascular parameters obtained from CT images in 288 COPD patients.65 Degrees of emphysema were classified as five subtypes based on Hounsfield Units and used to assess severity on inspiratory and expiratory scans. Total and small vessel numbers per lung surface area (LSA) were obtained and shown to decrease as COPD progressed. However, these markers had weaker correlations to PFTs. COPD can be distinguished by the emphysema versus bronchitis phenotypes, and these vascular parameters demonstrated that total and small vessel numbers per LSA were higher in the SAD/bronchitis phenotype, confirming that changes in the pulmonary vasculature were more prominent for subjects with predominantly emphysema.

4DMedical

4DMedical, founded in 2012 and based both in Melbourne, Australia and Los Angeles, CA, uses X-ray Velocimetry (XV) Technology to capture simultaneous X-ray images from different acquisition angles to measure the motion of lung tissue at multiple locations during various breath stages. XV then creates colored heat maps of ventilation measurements.66,67 The limited angles from which images are acquired by XV allows for much lower radiation doses than are used for conventional CT scans. XV Lung Ventilation Analysis Software (XV LVAS) is an FDA-approved software that generates reports of areas of high and low ventilation in all phases of breathing.

Reports consist of coronal and axial images rendered in 4-dimensional animation, where red depicts regions of low ventilation, green depicts average ventilation regions, and blue depicts regions of high ventilation (Figure 4). Once the report is generated, it is saved directly onto a hospital’s Picture Archiving and Communication System (PACS). New versions of these reports are actively being developed, and include contrast-free pulmonary angiography, ventilation-perfusion reports, and airway flow and expiratory quantification. Researchers at Johns Hopkins School of Medicine are actively studying XV Technology to validate the clinical benefit of XV LVAS, with the goal of detecting earlier changes in airway function, ventilation defects, and disease progression than clinically available spirometry data and CT images can provide.68 At Vanderbilt University, researchers are comparing the degree of hyperinflation in COPD subjects, as measured by XV image analysis software, to traditional PFTs. This study will also compare lobar expiratory time constraints with fissure completeness measured by StratX software (pulmonx.com/stratx/) with the goal of improving the ability to evaluate patient outcomes after endobronchial valve placement (4D Medical X-ray Velocimetry for Bronchoscopic Lung Volume Reduction Targeting, ClinicalTrials.gov ID NCT04786171).

Figure 4 Representative clinical report of lung ventilation with coronal map, histogram showing specific ventilation and quantitation of ventilation heterogeneity, courtesy of 4D Medical.

Discussion

The commercial platforms reviewed here provide regional functional and anatomic information that enhances our understanding of the heterogeneity of COPD and aids clinicians and researchers in assessing which patients are more likely to experience disease progression and respond to tailored treatments. These novel markers can be harnessed to individualize patient care, moving beyond the widely accepted global metrics obtained from PFTs and patient-reported symptoms. FLUIDDA’s FRI technology highlights key alterations in both airway structure and resistance during COPD exacerbations and with medication use. VIDA’s QCT software Apollo provides similar information by looking at airway remodeling with its airway segmentation tool. Both FLUIDDA and VIDA also explore vessel segmentation. Thirona’s LungQ software has been used to quantify lung volumes and disease distribution, and, more recently, to evaluate fissure completeness, similar to VIDA’s technology. Imbio’s unique capability lies in its PRM maps that can quantify the area of SAD in the lungs to show how these regions evolve with disease progression. Recently, Coreline’s AI-based technology AVIEW COPD has used automated segmentation software to evaluate many of the same key structures, including pulmonary vasculature, fissures, and regions of emphysema and SAD. Lastly, 4DMedical’s XV Technology relies instead on composite X-ray images to create ventilation maps and thereby requires lower radiation doses than the conventional CT scans used by other companies. Of note, this review is based on published literature and online resources, and a full description of each platform and its capabilities would only be attainable through direct contact with the vendor.

Conclusion

The companies discussed in this review have developed a wide array of novel imaging modalities that have already moved the QCT field forward and will allow researchers and clinicians additional insight into the complex disease process of COPD. These imaging biomarkers have demonstrated the ability to predict disease progression earlier and inform the contributions of airway remodeling, air trapping, and emphysema to airflow obstruction and altered pulmonary biomechanics. The geographic distribution of these changes as visualized by automated software has also been validated and could signal accelerated lung function decline not yet captured by global PFT metrics. Such tools also hold the promise of assisting clinicians in advanced bronchoscopic procedures, such as lung volume reduction via coil treatments and endobronchial valves in the appropriate patient population. More importantly, many of the metrics have been correlated to patient symptoms and clinically meaningful outcomes. Even in subjects without airflow obstruction as measured on PFTs, these imaging markers were sensitive to abnormalities that could predict which subgroups were more likely to progress and become symptomatic. This leaves ample opportunity for research focused on key populations, where early intervention and monitoring could change the course of a potentially irreversible disease process with significant life-limiting morbidity and mortality.

Abbreviations

COPD, chronic obstructive pulmonary disease; FRI, functional respiratory imaging; fSAD, functional small airways disease; PFT, pulmonary function testing; PRM, parametric response mapping; QCT, quantitative computed tomography.

Acknowledgments

The authors thank Lee Olsen for assisting with manuscript preparation and editing.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

The research for this review was funded by the National Heart, Lung, and Blood Institute/NIH (grants R01HL139690 and R01HL150023).

Disclosure

Dr. Galbán is co-inventor and patent holder of Parametric Response Mapping, which the University of Michigan has licensed to Imbio, LLC, and has a financial interest in Imbio, LLC. Dr. Labaki reports personal fees from Konica Minolta and Continuing Education Alliance. Dr. Han reports personal fees from GlaxoSmithKline, AstraZeneca, Boehringer Ingelheim, Cipla, Chiesi, Novartis, Pulmonx, Teva, Verona, Merck, Mylan, Sanofi, DevPro, Aerogen, Polarian, Regeneron, United Therapeutics, UpToDate, Medscape, and Integrity. She has received either in kind research support or funds paid to the institution from the National Institutes of Health (NIH), Novartis, Sunovion, Nuvaira, Sanofi, AstraZeneca, Boehringer Ingelheim, Gala Therapeutics, Biodesix, the COPD Foundation, and the American Lung Association. She has participated in Data Safety Monitoring Boards for Novartis and Medtronic with funds paid to the institution. She has received stock options from Meissa Vaccines. The authors report no other conflicts of interest in this work.

References

1. Quaderi SA, Hurst JR. The unmet global burden of COPD. Glob Health Epidemiol Genom. 2018;3:e4. doi:10.1017/gheg.2018.1

2. Agusti A, Calverley PM, Celli B, et al. Characterisation of COPD heterogeneity in the ECLIPSE cohort. Respir Res. 2010;11:122. doi:10.1186/1465-9921-11-122

3. Lopez-Campos JL, Tan W, Soriano JB. Global burden of COPD. Respirology. 2016;21(1):14–23. doi:10.1111/resp.12660

4. Lynch DA, Austin JH, Hogg JC, et al. CT-definable subtypes of chronic obstructive pulmonary disease: a statement of the Fleischner Society. Radiology. 2015;277(1):192–205. doi:10.1148/radiol.2015141579

5. Park J, Hobbs BD, Crapo JD, et al. Subtyping COPD by using visual and quantitative CT imaging features. Chest. 2020;157(1):47–60. doi:10.1016/j.chest.2019.06.015

6. Herth FJF, Kirby M, Sieren J, et al. The modern art of reading computed tomography images of the lungs: quantitative CT. Respiration. 2018;95(1):8–17. doi:10.1159/000480435

7. Lynch DA, Newell JD. Quantitative imaging of COPD. J Thorac Imaging. 2009;24(3):189–194. doi:10.1097/RTI.0b013e3181b31cf0

8. Coxson HO, Rogers RM, Whittall KP, et al. A quantification of the lung surface area in emphysema using computed tomography. Am J Respir Crit Care Med. 1999;159(3):851–856. doi:10.1164/ajrccm.159.3.9805067

9. Gevenois PA, De Vuyst P, de Maertelaer V, et al. Comparison of computed density and microscopic morphometry in pulmonary emphysema. Am J Respir Crit Care Med. 1996;154(1):187–192. doi:10.1164/ajrccm.154.1.8680679

10. Matsuoka S, Kurihara Y, Yagihashi K, Hoshino M, Watanabe N, Nakajima Y. Quantitative assessment of air trapping in chronic obstructive pulmonary disease using inspiratory and expiratory volumetric MDCT. AJR Am J Roentgenol. 2008;190(3):762–769. doi:10.2214/AJR.07.2820

11. Galban CJ, Han MK, Boes JL, et al. Computed tomography-based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression. Nat Med. 2012;18(11):1711–1715. doi:10.1038/nm.2971

12. Bhatt SP, Bodduluri S, Newell JD, et al. CT-derived biomechanical metrics improve agreement between spirometry and emphysema. Acad Radiol. 2016;23(10):1255–1263. doi:10.1016/j.acra.2016.02.002

13. Vasilescu DM, Martinez FJ, Marchetti N, et al. Noninvasive imaging biomarker identifies small airway damage in severe chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2019;200(5):575–581. doi:10.1164/rccm.201811-2083OC

14. Hammond E, Sloan C, Newell JD Jr, et al. Comparison of low- and ultralow-dose computed tomography protocols for quantitative lung and airway assessment. Med Phys. 2017;44(9):4747–4757. doi:10.1002/mp.12436

15. Gerard SE, Patton TJ, Christensen GE, Bayouth JE, Reinhardt JM. FissureNet: a deep learning approach for pulmonary fissure detection in CT images. IEEE Trans Med Imaging. 2019;38(1):156–166. doi:10.1109/TMI.2018.2858202

16. Estépar RS, Kinney GL, Black-Shinn JL, et al. Computed tomographic measures of pulmonary vascular morphology in smokers and their clinical implications. Am J Respir Crit Care Med. 2013;188(2):231–239. doi:10.1164/rccm.201301-0162OC

17. Martinez CH, Okajima Y, Yen A, et al. Paired CT measures of emphysema and small airways disease and lung function and exercise capacity in smokers with radiographic bronchiectasis. Acad Radiol. 2021;28(3):370–378. doi:10.1016/j.acra.2020.02.013

18. De Backer JW, Vos WG, Vinchurkar SC, et al. Validation of computational fluid dynamics in CT-based airway models with SPECT/CT. Radiology. 2010;257(3):854–862. doi:10.1148/radiol.10100322

19. van Geffen WH, Hajian B, Vos W, et al. Functional respiratory imaging: heterogeneity of acute exacerbations of COPD. Int J Chron Obstruct Pulmon Dis. 2018;13:1783–1792. doi:10.2147/COPD.S152463

20. Hajian B, De Backer J, Vos W, et al. Changes in ventilation-perfusion during and after an COPD exacerbation: an assessment using fluid dynamic modeling. Int J Chron Obstruct Pulmon Dis. 2018;13:833–842. doi:10.2147/COPD.S153295

21. De Backer LA, Vos WG, Salgado R, et al. Functional imaging using computer methods to compare the effect of salbutamol and ipratropium bromide in patient-specific airway models of COPD. Int J Chron Obstruct Pulmon Dis. 2011;6:637–646. doi:10.2147/COPD.S21917

22. De Backer LA, Vos W, De Backer J, Van Holsbeke C, Vinchurkar S, De Backer W. The acute effect of budesonide/formoterol in COPD: a multi-slice computed tomography and lung function study. Eur Respir J. 2012;40(2):298–305. doi:10.1183/09031936.00072511

23. De Backer J, Vos W, Vinchurkar S, et al. The effects of extrafine beclometasone/formoterol (BDP/F) on lung function, dyspnea, hyperinflation, and airway geometry in COPD patients: novel insight using functional respiratory imaging. J Aerosol Med Pulm Drug Deliv. 2015;28(2):88–99. doi:10.1089/jamp.2013.1064

24. De Backer W, De Backer J, Verlinden I, et al. Functional respiratory imaging assessment of glycopyrrolate and formoterol fumarate metered dose inhalers formulated using co-suspension delivery technology in patients with COPD. Ther Adv Respir Dis. 2020;14:1753466620916990. doi:10.1177/1753466620916990

25. De Backer W, De Backer J, Vos W, et al. A randomized study using functional respiratory imaging to characterize bronchodilator effects of glycopyrrolate/formoterol fumarate delivered by a metered dose inhaler using co-suspension delivery technology in patients with COPD. Int J Chron Obstruct Pulmon Dis. 2018;13:2673–2684. doi:10.2147/COPD.S171707

26. Vos W, Hajian B, De Backer J, et al. Functional respiratory imaging to assess the interaction between systemic roflumilast and inhaled ICS/LABA/LAMA. Int J Chron Obstruct Pulmon Dis. 2016;11:263–271. doi:10.2147/COPD.S93830

27. Lanclus M, Clukers J, Van Holsbeke C, et al. Machine learning algorithms utilizing functional respiratory imaging may predict COPD exacerbations. Acad Radiol. 2019;26(9):1191–1199. doi:10.1016/j.acra.2018.10.022

28. Cahn A, Hamblin JN, Robertson J, et al. An inhaled PI3Kdelta inhibitor improves recovery in acutely exacerbating COPD patients: a randomized trial. Int J Chron Obstruct Pulmon Dis. 2021;16:1607–1619. doi:10.2147/COPD.S309129

29. De Backer L, Vos W, Dieriks B, et al. The effects of long-term noninvasive ventilation in hypercapnic COPD patients: a randomized controlled pilot study. Int J Chron Obstruct Pulmon Dis. 2011;6:615–624. doi:10.2147/COPD.S22823

30. Hajian B, De Backer J, Sneyers C, et al. Pathophysiological mechanism of long-term noninvasive ventilation in stable hypercapnic patients with COPD using functional respiratory imaging. Int J Chron Obstruct Pulmon Dis. 2017;12:2197–2205. doi:10.2147/COPD.S136412

31. Hajian B, De Backer J, Vos W, et al. Pulmonary vascular effects of pulsed inhaled nitric oxide in COPD patients with pulmonary hypertension. Int J Chron Obstruct Pulmon Dis. 2016;11:1533–1541. doi:10.2147/COPD.S106480

32. Woodruff PG, Barr RG, Bleecker E, et al. Clinical significance of symptoms in smokers with preserved pulmonary function. N Engl J Med. 2016;374(19):1811–1821. doi:10.1056/NEJMoa1505971

33. Smith BM, Traboulsi H, Austin JHM, et al. Human airway branch variation and chronic obstructive pulmonary disease. Proc Natl Acad Sci USA. 2018;115(5):E974–E981. doi:10.1073/pnas.1715564115

34. Bhatt SP, Terry NL, Nath H, et al. Association between expiratory central airway collapse and respiratory outcomes among smokers. JAMA. 2016;315(5):498–505. doi:10.1001/jama.2015.19431

35. Diaz AA, Han MK, Come CE, et al. Effect of emphysema on CT scan measures of airway dimensions in smokers. Chest. 2013;143(3):687–693. doi:10.1378/chest.12-0039

36. Kim V, Davey A, Comellas AP, et al. Clinical and computed tomographic predictors of chronic bronchitis in COPD: a cross sectional analysis of the COPDGene study. Respir Res. 2014;15:52. doi:10.1186/1465-9921-15-52

37. Kirby M, Yin Y, Tschirren J, et al. A novel method of estimating small airway disease using inspiratory-to-expiratory computed tomography. Respiration. 2017;94(4):336–345. doi:10.1159/000478865

38. Kirby M, Tanabe N, Tan WC, et al. Total airway count on computed tomography and the risk of chronic obstructive pulmonary disease progression. Findings from a population-based study. Am J Respir Crit Care Med. 2018;197(1):56–65. doi:10.1164/rccm.201704-0692OC

39. Choi S, Haghighi B, Choi J, et al. Differentiation of quantitative CT imaging phenotypes in asthma versus COPD. BMJ Open Respir Res. 2017;4(1):e000252. doi:10.1136/bmjresp-2017-000252

40. Gompelmann D, Eberhardt R, Schuhmann M, et al. Lung volume reduction with vapor ablation in the presence of incomplete fissures: 12-month results from the STEP-UP Randomized Controlled Study. Respiration. 2016;92(6):397–403. doi:10.1159/000452424

41. Gompelmann D, Kontogianni K, Schuhmann M, Eberhardt R, Heussel CP, Herth FJ. The minimal important difference for target lobe volume reduction after endoscopic valve therapy. Int J Chron Obstruct Pulmon Dis. 2018;13:465–472. doi:10.2147/COPD.S152029

42. Kontogianni K, Russell K, Eberhardt R, et al. Clinical and quantitative computed tomography predictors of response to endobronchial lung volume reduction therapy using coils. Int J Chronic Obstr. 2018;13:2215–2223. doi:10.2147/Copd.S159355

43. Schuhmann M, Raffy P, Yin Y, et al. Computed tomography predictors of response to endobronchial valve lung reduction treatment. Comparison with chartis. Am J Respir Crit Care Med. 2015;191(7):767–774. doi:10.1164/rccm.201407-1205OC

44. Aaron CP, Hoffman EA, Kawut SM, et al. Ambient air pollution and pulmonary vascular volume on computed tomography: the MESA air pollution and lung cohort studies. Eur Respir J. 2019;53(6):1802116. doi:10.1183/13993003.02116-2018

45. Boueiz A, Chang Y, Cho MH, et al. Lobar emphysema distribution is associated with 5-year radiological disease progression. Chest. 2018;153(1):65–76. doi:10.1016/j.chest.2017.09.022

46. Diaz AA, Strand M, Coxson HO, et al. Disease severity dependence of the longitudinal association between CT lung density and lung function in smokers. Chest. 2018;153(3):638–645. doi:10.1016/j.chest.2017.10.012

47. Pompe E, Strand M, van Rikxoort EM, et al. Five-year progression of emphysema and air trapping at CT in smokers with and those without chronic obstructive pulmonary disease: results from the COPDGene Study. Radiology. 2020;295(1):218–226. doi:10.1148/radiol.2020191429

48. Charbonnier JP, Pompe E, Moore C, et al. Airway wall thickening on CT: relation to smoking status and severity of COPD. Respir Med. 2019;146:36–41. doi:10.1016/j.rmed.2018.11.014

49. Bodduluri S, Kizhakke Puliyakote A, Nakhmani A, Charbonnier JP, Reinhardt JM, Bhatt SP. Computed tomography-based airway surface area-to-volume ratio for phenotyping airway remodeling in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2021;203(2):185–191. doi:10.1164/rccm.202004-0951OC

50. Welling JBA, Hartman JE, van Rikxoort EM, et al. Minimal important difference of target lobar volume reduction after endobronchial valve treatment for emphysema. Respirology. 2018;23(3):306–310. doi:10.1111/resp.13178

51. Welling JBA, Klooster K, Charbonnier JP, et al. A new oxygen uptake measurement supporting target selection for endobronchial valve treatment. Respiration. 2019;98(6):521–526. doi:10.1159/000502310

52. Slebos DJ, Cicenia J, Sciurba FC, et al. Predictors of response to endobronchial coil therapy in patients with advanced emphysema. Chest. 2019;155(5):928–937. doi:10.1016/j.chest.2019.02.012

53. Bhatt SP, Soler X, Wang X, et al. Association between functional small airway disease and FEV1 decline in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2016;194(2):178–184. doi:10.1164/rccm.201511-2219OC

54. Criner RN, Hatt CR, Galban CJ, et al. Relationship between diffusion capacity and small airway abnormality in COPDGene. Respir Res. 2019;20(1):269. doi:10.1186/s12931-019-1237-1

55. Pompe E, Galban CJ, Ross BD, et al. Parametric response mapping on chest computed tomography associates with clinical and functional parameters in chronic obstructive pulmonary disease. Respir Med. 2017;123:48–55. doi:10.1016/j.rmed.2016.11.021

56. Han MK, Quibrera PM, Carretta EE, et al. Frequency of exacerbations in patients with chronic obstructive pulmonary disease: an analysis of the SPIROMICS cohort. Lancet Respir Med. 2017;5(8):619–626. doi:10.1016/S2213-2600(17)30207-2

57. Boes JL, Hoff BA, Bule M, et al. Parametric response mapping monitors temporal changes on lung CT scans in the subpopulations and intermediate outcome measures in COPD Study (SPIROMICS). Acad Radiol. 2015;22(2):186–194. doi:10.1016/j.acra.2014.08.015

58. Martinez CH, Diaz AA, Meldrum C, et al. Age and small airway imaging abnormalities in subjects with and without airflow obstruction in SPIROMICS. Am J Respir Crit Care Med. 2017;195(4):464–472. doi:10.1164/rccm.201604-0871OC

59. Labaki WW, Gu T, Murray S, et al. Reprint of: voxel-wise longitudinal parametric response mapping analysis of chest computed tomography in smokers. Acad Radiol. 2019;26(3):306–312. doi:10.1016/j.acra.2019.02.003

60. Pompe E, Moore CM, Mohamed Hoesein FAA, et al. Progression of emphysema and small airways disease in cigarette smokers. Chronic Obstr Pulm Dis. 2021;8(2):198–212. doi:10.15326/jcopdf.2020.0140

61. Labaki WW, Xia M, Murray S, et al. Quantitative emphysema on low-dose CT imaging of the chest and risk of lung cancer and airflow obstruction: an analysis of the national lung screening trial. Chest. 2021;159(5):1812–1820. doi:10.1016/j.chest.2020.12.004

62. Yun J, Park J, Yu D, et al. Improvement of fully automated airway segmentation on volumetric computed tomographic images using a 2.5 dimensional convolutional neural net. Med Image Anal. 2019;51:13–20. doi:10.1016/j.media.2018.10.006

63. Kwon SO, Hong SH, Han YJ, et al. Long-term exposure to PM10 and NO2 in relation to lung function and imaging phenotypes in a COPD cohort. Respir Res. 2020;21(1):247. doi:10.1186/s12931-020-01514-w

64. Hwang HJ, Seo JB, Lee SM, et al. New method for combined quantitative assessment of air-trapping and emphysema on chest computed tomography in chronic obstructive pulmonary disease: comparison with parametric response mapping. Korean J Radiol. 2021;22(10):1719–1729. doi:10.3348/kjr.2021.0033

65. Park SW, Lim M-N, Kim WJ, Bak SH. Quantitative assessment the longitudinal changes of pulmonary vascular counts in chronic obstructive pulmonary disease. Res Square. 2021;23:1.

66. Dubsky S, Hooper SB, Siu KK, Fouras A. Synchrotron-based dynamic computed tomography of tissue motion for regional lung function measurement. J R Soc Interface. 2012;9(74):2213–2224. doi:10.1098/rsif.2012.0116

67. Fouras A, Allison BJ, Kitchen MJ, et al. Altered lung motion is a sensitive indicator of regional lung disease. Ann Biomed Eng. 2012;40(5):1160–1169. doi:10.1007/s10439-011-0493-0

68. 4DMedical. 4DMedical commences clinical trial studying XV LVAS at Johns Hopkins school of medicine; [updated June 29, 2021]. Available from: company-announcements.afr.com/asx/4dx/5329e76d-d85f-11eb-8781-565d0a66e208.pdf. Accessed December 29, 2021.

Source link

Introduction

The characteristic symptoms of chronic obstructive pulmonary diseases (COPD) are dyspnea, reduced exercise tolerance and impaired health-related quality of life.1 Anxiety and depression are also frequently observed in people with COPD.1 In addition to these symptoms, more recently, there has been an increased awareness that pain is a common symptom in people with COPD.2 An earlier systematic review reported that pain affects between 45% and 96% of the people with COPD.3 According to an interview-based study of pain experiences, people with COPD reported that pain had adverse effects on their breathing, walking ability, daily activities and sleep, and contributed to the development of anxiety and depression.4 Both pain and dyspnea are unpleasant sensory and emotional experiences with physiological and psychological consequences.5 Therefore, dyspnea and pain in people with COPD are likely to interact and increase the total symptom burden, leading to greater challenges in clinical management.

Pain is defined as “An unpleasant sensory and emotional experience associated with, or resembling that associated with, actual or potential tissue damage”.6 In people with low back pain and arthritis, the presence of pain can be associated with an exaggerated negative cognitive and emotional response to actual as well as anticipated pain, referred to as pain catastrophizing.7 People with pain catastrophizing can be worried about how increased pain leads to progressive disability. Therefore, it leads to anxiety and depression. Previous studies have reported that COPD people with chronic pain have psychological disorder such as anxiety or depression.8,9 If individual people perceive by pain be anxiety, they fall into the loop described as a fear-avoidance mode.6 Since people with chronic pain often have a range of signs and symptoms in addition to pain, it is recommended to perform a multifaceted evaluation such as such as physical function, cognition, and emotion in order to comprehensively understand their pain.6 Further, an association between pain and reduced physical activity has been reported in those with COPD10 which is important given that low levels of physical activity are associated with greater mortality in this population.11 Moreover, a previous study reported that pulmonary rehabilitation did not relieve the sensation of pain or the patients’ response such as general health, mood, enjoyment of life, anxiety or fear avoidance behavior to COPD with pain.9 Although there are reports of the cognitive, emotional, and physical aspects associated with chronic pain in people with COPD, few have included a systematically multifaceted assessment.12–14 Specifically, few studies have included assessment of cognitive aspects such as pain catastrophizing and fear of movement.15,16

The primary aim of this study was to describe the characteristic multifaceted impact of chronic pain on cognition, emotional and physical health in people with stable COPD and to compare with an age-matched sample of community-dwelling older participants. The secondary aim was to explore the pain impact upon physical function and psychological aspect in people with COPD.

Materials and Methods

Study Design and Participants

This was a prospective, cross-sectional multicenter study carried out in people with COPD and a group of community-dwelling age-matched participants. The COPD participants (COPD group) comprised those with clinically stable disease17,18 who attended pulmonary rehabilitation at Hozenkai Tagami Hospital (Nagasaki, Japan), Utsunomiya Medical Clinic or Kirigaoka Tsuda Hospital (Fukuoka, Japan). Stable COPD was defined as having symptoms are well managed, pulmonary decline is minimized and no exacerbation in the previous 4 weeks.17,18 The community-dwelling participants (Control group) were recruited from a community-based exercise class. Recruitment took place between November 2017 and May 2018. For the COPD group, inclusion criteria comprised confirmed diagnosis of COPD on spirometry and receiving optimal medical management. Control participants were required to have no history of lung disease such as bronchiectasis, asthma and interstitial lung diseases in addition to COPD, and no smoking history as inclusion criteria. People with COPD were excluded from participation if they had experienced an exacerbation in the previous 4 weeks. Exclusion criteria common to both groups were the presence of concurrent malignancy (including lung cancer) that can cause chronic pain, a diagnosis of cognitive impairment or suspected cognitive dysfunction, and soft tissue or musculoskeletal injury within the last 4 weeks. All participants provided written informed consent prior to data collection. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013) and approved by the Human Ethics Review Committee of Nagasaki University Graduate School of Biomedical Sciences on October 10, 2017 (approval number 17082117).

Procedures

All participants attended a single visit. Firstly, participants were asked a screening question relating to chronic pain, “In the last past month have you experienced any pain-related symptoms? If yes, were these symptoms present for at least the last 6 months?”19 Those who answered yes to the second question were considered to have chronic pain and underwent the following assessments and completed them by themselves in a quiet environment of each room. The remaining participants (ie, those without chronic pain) underwent all assessments other than those related to pain (ie, location, intensity, catastrophizing and fear).

Location of Pain

The participant was asked to circle the location(s) of their pain on a body chart comprising standardized body regions based on 45 anatomical areas.20

Pain Intensity

This was assessed by documenting intensity at the site of maximum pain experienced over the past month using a numerical rating scale from 0 (“no pain”) to 10 (“worst imaginable pain”).21

Pain Catastrophizing

This was assessed using the Japanese version of the Pain Catastrophizing Scale (PCS-J).22 The scale comprises 13 items and can be scored to provide an overall composite measure of catastrophizing (total score) or as three subscales that represent rumination, helplessness and magnification. The PCS-J instructions ask the responder to reflect on past painful experiences and to indicate the degree to which they experienced thoughts or feelings when experiencing pain on a 5-point Likert scale (0–4), with a total score ranging from 0 to 52; higher scores indicate greater catastrophizing. A score above 30 (cut-off) indicates a high level of catastrophizing. The PCS-J has been shown to be reliable and valid in Japanese people with chronic pain.23

Pain-Related Fear

Pain-related fear was assessed using the Japanese version of the Tampa Scale for Kinesiophobia (TSK-J), which is a self-report measure developed to assess fear of movement-related pain. The scale comprises 17 questions which identify fear-related to activities with responses to each question scored on a 4-point Likert scale (0–4) with a maximum total score of 68.24 A score greater than 37 indicates a high degree of kinesiophobia.25 The TSK-J is valid and reliable for detecting kinesiophobia in the Japanese population.26

Anxiety and Depression

This was assessed using the Hospital Anxiety and Depression Scale (HADS) which consists of 14 items, 7 of which assess anxiety and seven assess depression. Response options to each question are assigned a score from 0 to 3 with higher scores representing greater feelings of anxiety and/or depression. Scores of 7 or lower were considered “normal”, scores of 8–10 taken to represent “borderline abnormal” and a score of 11 or higher considered to be suggestive of a “clinical diagnosis of anxiety or depression”.27

Physical Activity

Physical activity was measured with a uniaxial accelerometer (Lifecorder GS; Suzuken Corporation; Nagoya, Japan) over a 7-day period. The accelerometer records vertical acceleration as counts and activity times per day. The accelerometer was worn on a waistband at all times except when showering or sleeping. The step count of adults when walking around an outdoor track with this accelerometer has an intramodel reliability of 0.998.28

Sleep Duration

Participants were asked to report their average daily sleep time over the past month by responding to a question about sleep duration taken from the Japanese version of the Pittsburgh Sleep Quality Index.29

In addition, the following evaluations were also undertaken in the COPD group.

Spirometry

Lung function was measured using a portable automatic calibrated spirometer (Autospiro AS-507®; MINATO MEDICAL SCIENCE, Osaka, Japan). We measured forced expiratory volume in the first second (FEV1) and forced vital capacity (FVC) in accordance with published guidelines.30 Each participant repeated the manoeuvres until three reliable tracings were obtained, and the highest values were retained for analysis.30 Measurements were expressed in absolute values and as a percentage of the predicted value in a healthy Japanese population.31 Based on these results, the severity was classified according to Global Strategy for the Diagnosis Management and prevention of Chronic Obstructive Pulmonary Disease (GOLD).1

Dyspnea

The modified Medical Research Council (mMRC) dyspnea scale was used to assess functional limitation due to dyspnea. This scale is a valid method of categorising people with chronic lung disease in terms of their functional disability. This simple scale comprises five statements, which describe disability ranging from none (Grade 0) to almost complete incapacity (Grade 4). The participant selects the statement, which best reflects their level of limitation in activities of daily life due to breathlessness.32

Activities of Daily Living (ADL)

This was evaluated using the Nagasaki University Respiratory Activities of Daily Living questionnaire.33 This questionnaire consists of 10 ADL items and an additional score for continuous walking distance. Each of the 10 ADL items rates movement speed, dyspnea, and flow rate of supplemental oxygen, if used, from 0 to 3 points, and the continuous walking distance is scored from 0 to 10 points (≤50m, 0 points; 51–200m, 2 points; 201–500m, 4 points; 501m-1km, 8 points; > 1km, 10 points). The maximum total score is 100 points.

Exercise Performance

This was assessed using the 6-minute walk test (6MWT) in accordance with published guidelines.34 The test measures the maximum distance a person can walk in 6 minutes (6-minute walk distance, [6MWD]), on a flat, hard surface. It is a self-paced test with standardized encouragement given each minute. The higher 6MWD from two attempts was recorded.

Sample Size

The sample size was calculated using G*Power and based on a previous study in which the prevalence of pain was 72% in individuals with COPD14 and 34% in healthy control participants,13 for a t-test with an alpha of 0.05 and power of 0 0.80. A sample size of 61 participants in each group was required.

Data Analysis

Data were assessed for normality using the Shapiro–Wilk test. Data are expressed as the mean ± standard deviation or absolute number and percentage. Between-group differences (ie, COPD vs Control group; COPD with pain vs COPD without pain) were assessed using Mann–Whitney U-test, with categorical data analyzed using Chi Square test. Statistical analysis was performed using IBM SPSS statistics version 25.0 for Windows (IBM SPSS Statistics version 25.0, IBM, Chicago, IL, USA). The level of statistical significance was set at <0.05%.

Results

A total of 78 people with COPD and 78 community-dwelling individuals were screened for eligibility. Of these, 74 participants with COPD (COPD group) and 70 community-dwelling individuals (Control group) were recruited to the study. Data from 11 participants were not included in the analyses (6 COPD group, 5 Control group) due to incomplete or missing data (Figure 1). The demographics of the final sample (68 COPD group, 65 Control group) are presented in Table 1. There was a higher percentage of males in the COPD group (76% vs 32% in the Control group, p<0.001). Compared with the Control group, weight and body mass index (BMI) were lower (both p<0.05) and the prevalence of pain was higher in the COPD group (85% vs 51%, p<0.001).

Table 1 Participant Demographics

Figure 1 Flow diagram of study participants.

Abbreviation: COPD, chronic obstructive pulmonary disease.

Figure 2 provides a comparison of the pain locations reported by the two groups. Pain was more commonly experienced in the neck/shoulders, chest, upper back, and upper limbs in the COPD group (p<0.001). A greater proportion of the Control group reported low back pain (p<0.05).

Figure 2 Pain location. COPD group, Control group.*p<0.05 indicates a significant difference between groups.

Abbreviations: COPD, chronic obstructive pulmonary disease; UL, upper limb; LL, lower limb.

Table 2 compares the characteristics of the COPD and control participants who experienced pain. The number of pain locations was higher in the COPD group (p<0.001). The scores for helplessness, magnification and the total score on the PCS-J were higher in the COPD group compared to the Control group (all p<0.05). Sixteen (28%) participants with COPD and three (9%) participants in the Control group reported a high level of pain catastrophizing (p<0.05). Kinesiophobia (TSK-J total score >37) was present in 67% of the COPD group versus 42% in the Control group (p<0.05). There were no between-group differences in the mean scores for anxiety or depression, however, a higher proportion in the COPD group reported scores for anxiety and depression above the cut off for borderline abnormal (both p<0.05). Daily step count was significantly lower in the COPD group, being on average half that measured in the Control group (p<0.05).

Table 2 Comparison of Characteristics Between the COPD and Control Group with Chronic Pain

The clinical impact of pain in people with COPD is shown in Table 3. No differences in gender, BMI, spirometry, anxiety and depression, and duration of sleep were evident in those with chronic pain compared to those without pain. In contrast, those with pain had more severe COPD (GOLD grade) and higher levels of dyspnea (mMRC grade) (both p<0.001) and a higher proportion were using long-term oxygen therapy (LTOT, p<0.05). Further, the COPD group with pain were more restricted in ADL (NRADL, p<0.05), had worse exercise tolerance (6MWD, p<0.01) and a lower daily step count per day (mean difference −848 steps) (p<0.01).

Table 3 Clinical Impact of Pain in Participants with COPD

Discussion

To our knowledge, this is first study to undertake a chronic pain multifaceted evaluation of people with COPD. We found a higher prevalence of chronic pain among people with COPD compared to community-dwelling older people of a similar age with no history of lung disease. The commonly reported pain locations in our COPD sample were neck/shoulder, upper back, thorax and upper limbs, in contrast to low back pain which was the most frequent site of pain experienced by the community-dwelling individuals. Further, in those with COPD, the pain was associated with higher levels of catastrophizing, kinesiophobia, anxiety and depression, and reduced physical activity. The COPD participants who experienced pain were characterized by more severe disease, lower exercise capacity and physical activity, and had greater restrictions in ADL compared to those with COPD without chronic pain.

In the current study, the high (85%) prevalence of chronic pain in people with COPD was within the range (45–96%) reported in a systematic review.3 Our finding was also similar to the 81% prevalence of chronic pain reported by HajGhanbari et al,35 in a COPD sample of a similar age and disease severity (mean age; 72 years, FEV1 predicted; 43%). In addition, our study was similar to previous studies that reported a higher prevalence of pain in COPD patients compared to the general population.12,13,15 As a possible cause, HajGhanbari et al12 described that there might be systemic inflammation, central adaptation associated with dyspnea, and musculoskeletal disorders including restricted chest wall movement due to hyperinflation. Another systematic review36 reported that pain prevalence ranged from 32% to 60% and that the variation in prevalence could be explained by heterogeneity in the participants’ characteristics. Another contributor to the variation in prevalence is the specific definition of pain used in the studies. The same systematic review36 stated that a relatively high prevalence was found in some studies that used a low threshold for reporting pain, for example where pain was considered to be present in all participants who shaded pain on a body chart. We used a low threshold for reporting pain. In contrast, in other studies pain was required to be moderately severe or extremely severe for at least half of the time to meet the definition of pain.36 However, this study was not measured inflammatory markers or indicator of hyperinflation and could not clear about possible etiologies. The further research is required to determine the contributing factors to the etiology of pain in people with COPD.

The commonly reported pain locations such as neck/shoulder, chest and upper back in our COPD sample were consistent with previous studies.4,12,13,35 These were the most frequently reported sites as a result of a meta-analysis.36 In the previous studies, these were speculated that the pain was associated with cough, osteoporosis, gastroesophageal reflux disease or musculoskeletal disorders such as vertical deformity and costotransverse joint arthropathy of the chest wall caused by hyperinflation, excessive use of respiratory assist muscles due to abnormal breathing patterns.12,13,37,38 In the current study, the proportion of upper limbs was higher than the control group. It was different from a previous study. However, the cause of pain locations was not investigated in our study and further research is required to determine the contributing factors to the etiology of pain in people with COPD.

Responses to the questionnaires assessing pain catastrophizing and pain-related fear suggest that pain had a more profound effect on cognitive function in the COPD group compared to the control group. In the current study, the percentage (28%, 16/57) of the COPD group with pain who had a high level of pain catastrophizing (total PCS ≥30), was significantly higher than in the Control group with pain (9%, 3/33). This finding is consistent with a previous study where a greater percentage (24%, 7/29) of people with COPD had a high level of pain catastrophizing when compared to an age and gender-matched sample of healthy controls (11%, 2/19).15 People with pain may have catastrophic thinking that their pain will significantly affect their lives, and such thinking has been associated with disability among with chronic medical condition particularly chronic pain.39 Catastrophizing is an exaggerated negative cognitive toward noxious stimuli and experiences, characterized by rumination about those experiences, magnification of their threat value, and perceived inability to control them.40 In the previous studies with pain, catastrophizing has been found to be more strongly correlated with disability than the pain itself41,42 and has contributed to a fear avoidance model of disability.43 As well as this mechanism, pain in people with COPD could lead to more catastrophic thinking. Our COPD participants experienced pain-related fear as indicated by their scores on the TSK-J, a finding similar to that seen in other studies of people with chronic pain.6 People who experience pain may be concerned that physical activity may elicit or worsen their pain and in turn this may lead to progressive disability.6 Anxiety and depression are also associated with pain-exacerbating activities, such as avoidance of physical activity.36 One study that used the TSK in COPD participants with chronic pain reported that 93% of the sample had a high degree of kinesiophobia which was strongly associated with dyspnea perception and depression.16 A cardinal symptom of COPD is dyspnea, and mood disturbance (ie anxiety and depression) is a common comorbidity.1 Pain-related fear may relate to dyspnea, or to the presence of anxiety or depression in those with COPD.

The level of physical activity in the COPD group was half that of the Control group. Moreover, physical activity in both groups was less than the average step count reported for Japanese people aged over 70 (male; 5016 steps/day, female; 4225 steps/day).44 Contributing to the finding of lower levels of physical activity could be the effect of pain on cognitive function, anxiety and depression associated with pain-exacerbating activities, and the avoidance of physical activity leading to a downward spiral of increased disability.39 However, given that many studies have reported high levels of anxiety and depression and low physical activity in people with COPD,1 our finding may be a function of having COPD.

Our COPD participants with pain had worse respiratory function, higher levels of dyspnea and a higher proportion were using LTOT compared to COPD participants without pain. An earlier study reported that chronic pain in people with COPD was associated with greater dyspnea and that each symptom could amplify perception of the other.45 The lower 6MWD and greater limitation in daily life in the COPD participants with pain could be due to the combined effects of chronic pain and dyspnea on physical function and ADL. The association between pain and reduced physical activity is important, given the detrimental effects of a sedentary lifestyle on survival in COPD.46 However, this finding should be interpreted with caution due to the modest sample size of those without pain and the possibility in the group with pain that worse airflow limitation and a higher percentage receiving LTOT contributed to the lower 6MWD and as a consequence a greater reduction in physical activity.

The management of COPD prioritizes approaches that have been demonstrated to improve symptoms such as dyspnea.1 However, previous studies reported that dyspnea is more prevalent than pain.3,47 Therefore, dyspnea that precedes pain may be overlooked. Moreover, Lee et al9 reported that pulmonary rehabilitation did not relieve the sensation of pain or the patients’ response to COPD with pain. A better understanding of the characteristics of chronic pain and its influence on symptoms and physical activity will help inform approaches for managing chronic pain in people with COPD. Given this observation, it is necessary to assess the symptoms of chronic pain as well as dyspnea in people with COPD, and especially in those with more severe disease and establish chronic pain and dyspnea coping strategies that are essential for optimizing COPD management. There are evidences that the affective dimensions of dyspnea, such as distress and unpleasantness, are processed in the same areas of the brain that process fear, anxiety, and pain.5,48 In addition, the guidelines for chronic pain strongly recommend psychological treatments, specifically psycho-educational relaxation techniques, self-monitoring and other hand-based techniques, and cognitive-behavioral therapy as psychological approaches.49 Based on these results, our finding suggests the need for these interventions also in patients with COPD who have pain.

The strengths of our study include the systematically multifaceted assessment of chronic pain, which has been lacking in previous studies, the inclusion of a group without lung disease and assessment of the clinical impact of pain in those with COPD. This study has some limitations. Firstly, the COPD group had a high proportion of males in contrast to the Control group that was mostly female, consistent with the gender of those who attend community-based exercise classes within Japan. However, in the general population, the prevalence of pain in females is higher than in males.44 Secondly, community-dwelling participants did not undergo spirometry, so we cannot exclude the presence of undiagnosed lung disease. Thirdly, neither group was investigated for comorbidities such as osteoporosis and heart disease. Lastly, this study did not include an assessment of quality of life.

Conclusion

This study demonstrated that compared to community-dwelling participants, chronic pain is more prevalent in people with COPD. The pain was more commonly located in the neck/shoulder, upper back, thorax and upper limbs in those with COPD, whereas pain in the lower back was the commonest site in the community-dwelling sample. The presence of pain in COPD can have a greater impact on cognitive function and, combined with dyspnea, has the potential to lead to a negative spiral that adversely affects mood, exercise tolerance, physical activity, and ADL. Our results indicate that management of people with COPD may need to focus more attention, assessment and intervention such as psychological approaches of chronic pain, in addition to symptoms such as dyspnea, with the aim of implementing strategies to minimize pain impact and thus improve well-being.

Data Sharing Statement

Data used to support the findings of this study are included within the manuscript in Tables 1–3 and Figure 2. Raw data are available from the corresponding author upon reasonable request.

Acknowledgments

The authors were grateful to the study participants, physiotherapists who acquired data for the study from Department of Rehabilitation, Hozenkai Tagami Hospital; Department of Rehabilitation, Kirigaoka Tsuda Hospital; Department of Rehabilitation; Utsunomiya Medical Clinic and Special Elderly Nursing Home Keijyuen.

Author Contributions

TT, MO and RK contributed significantly to conception and design, or analysis and interpretation of data; and have been involved in drafting and reviewing the manuscript. SJ contributed significantly to analysis and interpretation data and has been involved in drafting and revised it critically for important intellectual content. All authors contributed to data analysis, drafting or revising the article, have agreed on the journal to which the article will be submitted, gave final approval of the version to be published, and agree to be accountable for all aspects of the work. All named authors had given final approval of the version to be published.

Funding

The study design, collection and analysis of the data, and preparation of the manuscript were not funded by any public, commercial, or not-for-profit sectors.

Disclosure

The authors report no conflicts of interest in this work.

References

1. GOLD. Global strategy for the diagnosis m, and prevention of chronic obstructive pulmonary disease; 2021. Available from: goldcopd.org/wp-content/uploads/2020/11/GOLD-REPORT-2021-v1.1-25Nov20_WMV.pdf.html. Accessed June 4, 2021.

2. Lewthwaite H, Williams G, Baldock KL, Williams MT. Systematic review of pain in clinical practice guidelines for management of COPD: a case for including chronic pain? Healthcare. 2019;7(1):15. doi:10.3390/healthcare7010015

3. Lee AL, Harrison SL, Goldstein RS, Brooks D. Pain and its clinical associations in individuals with COPD: a systematic review. Chest. 2015;147(5):1246–1258. doi:10.1378/chest.14-2690

4. Lohne V, Heer HC, Andersen M, Miaskowski C, Kongerud J, Rustoen T. Qualitative study of pain of patients with chronic obstructive pulmonary disease. Heart Lung. 2010;39(3):226–234. doi:10.1016/j.hrtlng.2009.08.002

5. von Leupoldt A, Sommer T, Kegat S, et al. Dyspnea and pain share emotion-related brain network. Neuroimage. 2009;48(1):200–206. doi:10.1016/j.neuroimage.2009.06.015

6. Raja SN, Carr DB, Cohen M, et al. The revised International Association for the Study of Pain definition of pain: concepts, challenges, and compromises. Pain. 2020;161(9):1976–1982. doi:10.1097/j.pain.0000000000001939

7. Quartana PJ, Campbell CM, Edwards RR. Pain catastrophizing: a critical review. Expert Rev Neurother. 2009;9(5):745–758. doi:10.1586/ern.09.34

8. Andenaes R, Momyr A, Brekke I. Reporting of pain by people with chronic obstructive pulmonary disease (COPD): comparative results from the HUNT3 population-based survey. BMC Public Health. 2018;18(1):181. doi:10.1186/s12889-018-5094-5

9. Lee AL, Butler SJ, Varadi RG, Goldstein RS, Brooks D. The impact of pulmonary rehabilitation on chronic pain in people with COPD. COPD. 2020;17(2):165–174. doi:10.1080/15412555.2020.1733952

10. HajGhanbari B, Garland SJ, Road JD, Reid WD. Pain and physical performance in people with COPD. Respir Med. 2013;107(11):1692–1699. doi:10.1016/j.rmed.2013.06.010

11. Esteban C, Quintana JM, Aburto M, et al. Impact of changes in physical activity on health-related quality of life among patients with COPD. Eur Respir J. 2010;36(2):292–300. doi:10.1183/09031936.00021409

12. HajGhanbari B, Holsti L, Road JD, Darlene Reid W. Pain in people with chronic obstructive pulmonary disease (COPD). Respir Med. 2012;106(7):998–1005. doi:10.1016/j.rmed.2012.03.004

13. Bentsen SB, Rustoen T, Miaskowski C. Prevalence and characteristics of pain in patients with chronic obstructive pulmonary disease compared to the Norwegian general population. J Pain. 2011;12(5):539–545. doi:10.1016/j.jpain.2010.10.014

14. Borge CR, Wahl AK, Moum T. Pain and quality of life with chronic obstructive pulmonary disease. Heart Lung. 2011;40(3):e90–101. doi:10.1016/j.hrtlng.2010.10.009

15. Lee AL, Goldstein RS, Brooks D. Chronic pain in people with chronic obstructive pulmonary disease: prevalence, clinical and psychological implications. Chronic Obstr Pulm Dis. 2017;4(3):194–203. doi:10.15326/jcopdf.4.3.2016.0172

16. Vardar-Yagli N, Calik-Kutukcu E, Saglam M, Inal-Ince D, Arikan H, Coplu L. The relationship between fear of movement, pain and fatigue severity, dyspnea level and comorbidities in patients with chronic obstructive pulmonary disease. Disabil Rehabil. 2019;41(18):2159–2163. doi:10.1080/09638288.2018.1459886

17. Negewo NA, McDonald VM, Baines KJ, et al. Peripheral blood eosinophils: a surrogate marker for airway eosinophilia in stable COPD. Int J Chron Obstruct Pulmon Dis. 2016;11:1495–1504. doi:10.2147/COPD.S100338

18. Bollmeier SG, Hartmann AP. Management of chronic obstructive pulmonary disease: a review focusing on exacerbations. Am J Health Syst Pharm. 2020;77(4):259–268. doi:10.1093/ajhp/zxz306

19. Nakamura M, Nishiwaki Y, Ushida T, Toyama Y. Prevalence and characteristics of chronic musculoskeletal pain in Japan. J Orthop Sci. 2011;16(4):424–432. doi:10.1007/s00776-011-0102-y

20. Margolis RB, Tait RC, Krause SJ. A rating system for use with patient pain drawings. Pain. 1986;24(1):57–65. doi:10.1016/0304-3959(86)90026-6

21. Haefeli M, Elfering A. Pain assessment. Eur Spine J. 2006;15(Suppl 1):S17–24. doi:10.1007/s00586-005-1044-x

22. Matsuoka H, Sakano Y. Assessment of cognitive aspect of pain: development, reliability, and validation of Japanese version of pain catastrophizing scale. Jpn J Psychosom Med. 2007;47(8):95–102. Japanese. doi:10.15064/jjpm.47.2_95

23. Iwaki R, Arimura T, Jensen MP, et al. Global catastrophizing vs catastrophizing subdomains: assessment and associations with patient functioning. Pain Med. 2012;13(5):677–687. doi:10.1111/j.1526-4637.2012.01353.x

24. Roelofs J, Goubert L, Peters ML, Vlaeyen JW, Crombez G. The Tampa scale for kinesiophobia: further examination of psychometric properties in patients with chronic low back pain and fibromyalgia. Eur J Pain. 2004;8(5):495–502. doi:10.1016/j.ejpain.2003.11.016

25. Vlaeyen JWS, Kole-Snijders AMJ, Boeren RGB, van Eek H. Fear of movement/(re) injury in chronic low back pain and its relation to behavioral performance. Pain. 1995;62(3):363–372. doi:10.1016/0304-3959(94)00279-N

26. Kikuchi N, Matsudaira K, Sawada T, Oka H. Psychometric properties of the Japanese version of the Tampa Scale for Kinesiophobia (TSK-J) in patients with whiplash neck injury pain and/or low back pain. J Orthop Sci. 2015;20(6):985–992. doi:10.1007/s00776-015-0751-3

27. Shima S, Shikano T, Kitamura T, Asai M. A new self-rating scale for depression. Clin Psychiatry. 1985;27:717–723. Japanese.

28. Schneider PL, Crouter SE, Lukajic O, Bassett DR Jr. Accuracy and reliability of 10 pedometers for measuring steps over a 400-m walk. Med Sci Sports Exerc. 2003;35(10):1779–1784. doi:10.1249/01.MSS.0000089342.96098.C4

29. Doi Y, Minowa M, Uchiyama M, et al. Psychometric assessment of subjective sleep quality using the Japanese version of the Pittsburgh Sleep Quality Index (PSQI-J) in psychiatric disordered and control subjects. Psychiatry Res. 2000;97(2–3):165–172. doi:10.1016/s0165-1781(00)00232-8

30. Miller MR, Hankinson J, Brusasco V, et al. Standardisation of spirometry. Eur Respir J. 2005;26(2):319–338. doi:10.1183/09031936.05.00034805

31. Japanese Respiratory Society. Guidelines for the Diagnosis and Treatment of COPD. 3rd ed. Japanese Respiratory Society; 2009.

32. Mahler DA, Wells CK. Evaluation of clinical methods for rating dyspnea. Chest. 1988;93(3):580–586. doi:10.1378/chest.93.3.580

33. Takigawa N, Tada A, Soda R, et al. Comprehensive pulmonary rehabilitation according to severity of COPD. Respir Med. 2007;101(2):326–332. doi:10.1016/j.rmed.2006.03.044

34. Holland AE, Spruit MA, Troosters T, et al. An official European Respiratory Society/American Thoracic Society technical standard: field walking tests in chronic respiratory disease. Eur Respir J. 2014;44(6):1428–1446. doi:10.1183/09031936.00150314

35. HajGhanbari CY, Garland SJ, Garland RJ, et al. The relationship between pain and comorbid health conditions in people with chronic obstructive pulmonary disease. Cardiopulm Phys Ther J. 2014;25(1):29–35. doi:10.1097/01823246-201403000-00007

36. van Dam van Isselt EF, Groenewegen-Sipkema KH, Spruit-van Eijk M, et al. Pain in patients with COPD: a systematic review and meta-analysis. BMJ Open. 2014;4(9):e005898. doi:10.1136/bmjopen-2014-005898

37. Bordoni B, Marelli F, Morabito B, Castagna R. Chest pain in patients with COPD: the fascia’s subtle silence. Int J Chron Obstruct Pulmon Dis. 2018;13:1157–1165. doi:10.2147/COPD.S156729

38. Chen YW, Coxson HO, Coupal TM, et al. The contribution of thoracic vertebral deformity and arthropathy to trunk pain in patients with chronic obstructive pulmonary disease (COPD). Respir Med. 2018;137:115–122. doi:10.1016/j.rmed.2018.03.007

39. Boersma K, Linton SJ. Psychological processes underlying the development of a chronic pain problem: a prospective study of the relationship between profiles of psychological variables in the fear-avoidance model and disability. Clin J Pain. 2006;22(2):160–166. doi:10.1097/01.ajp.0000159582.37750.39

40. Sullivan MJL, Bishop SR, Pivik J. The pain catastrophizing scale: development and validation. Psychol Assess. 1995;7(4):524–532. doi:10.1037/1040-3590.7.4.524

41. Crombez G, Vlaeyen JW, Heuts PHTG, Lysens R. Pain-related fear is more disabling than pain itself: evidence on the role of pain-related fear in chronic back pain disability. Pain. 1999;80(1–2):329–339. doi:10.1016/S0304-3959(98)00229-2

42. Helsen K, Goubert L, Peters ML, Vlaeyen JW. Observational learning and pain-related fear: an experimental study with colored cold pressor tasks. J Pain. 2011;12(12):1230–1239. doi:10.1016/j.jpain.2011.07.002

43. Vlaeyen JWS, Linton SJ. Fear-avoidance and its consequences in chronic musculoskeletal pain: a state of the art. Pain. 2000;85(3):317–332. doi:10.1016/S0304-3959(99)00242-0

44. Ministry of Health Law. National health and nutrition survey. Available from: www.mhlw.go.jp/content/10900000/000687163.pdf. Accessed September 1, 2021.

45. Cai B, Oderda GM. The association between pain and depression and some determinants of depression for the general population of the United States. J Pain Palliat Care Pharmacother. 2012;26(3):257–265. doi:10.3109/15360288.2012.703292

46. Garcia-Aymerich J, Lange P, Benet M, Schnohr P, Anto JM. Regular physical activity modifies smoking-related lung function decline and reduces risk of chronic obstructive pulmonary disease: a population-based cohort study. Am J Respir Crit Care Med. 2007;175(5):458–463. doi:10.1164/rccm.200607-896OC

47. Janssen DJ, Spruit MA, Uszko-Lencer NH, Schols JM, Wouters EF. Symptoms, comorbidities, and health care in advanced chronic obstructive pulmonary disease or chronic heart failure. J Palliat Med. 2011;14(6):735–743. doi:10.1089/jpm.2010.0479

48. Carrieri-Kohlman V, Donesky-Cuenco D, Park SK, Mackin L, Nguyen HQ, Paul SM. Additional evidence for the affective dimension of dyspnea in patients with COPD. Res Nurs Health. 2010;33(1):4–19. doi:10.1002/nur.20359

49. Williams AC, Eccleston C, Morley S. Psychological therapies for the management of chronic pain (excluding headache) in adults. Cochrane Database Syst Rev. 2020;11:CD007407. doi:10.1002/14651858.CD007407.pub4

Source link

A chest X-ray can be an important screening tool for people with asthma, but an X-ray alone is not enough to diagnose the condition.

Approximately 25 million adults in the United States (U.S.) are living with asthma. It is the most common chronic disease in children.

The first step in asthma treatment is an accurate diagnosis. Doctors use a range of tests to check for asthma symptoms, including blood tests and lung function tests.

A chest X-ray can also be helpful for people who have severe asthma or live with another condition alongside asthma.

Keep reading to learn more about the diagnosis and treatment of asthma and how and when an asthma X-ray can help.

About 5 million children in the U.S. currently have asthma. In adults, the condition is more prevalent among females than males, but more boys than girls receive a diagnosis during childhood.

Many people with asthma may not know that they have the condition, especially if they experience only mild symptoms. They may mistake these symptoms for those of another condition.

Some of the most common asthma symptoms include:

Cause

There is no known cause of asthma. However, a person’s genetic background and environment can play a role in the development of the condition.

Some people experience asthma as part of an allergic reaction. Others may notice that their symptoms occur in response to triggers such as temperature changes or certain household cleaners.

People with asthma may also have a slightly increased risk of illness from COVID-19, so they should receive the COVID-19 vaccination.

Asthma exacerbations can also result from respiratory conditions, such as the flu and pneumonia. A person should receive seasonal vaccinations to help prevent any complications.

Learn more about the flu shot.

Diagnosis

Healthcare professionals use various diagnostic tests to detect asthma. These may include a physical exam and tests that measure lung function. Doctors may also test for allergies through skin tests or blood analysis.

Early diagnosis and treatment are essential for managing asthma symptoms. Some asthma attacks may be brief and mild. Other attacks can last for a long time and cause serious harm if a person does not get treatment.

Learn more by visiting our asthma and allergies hub.

Generally, doctors do not use chest X-rays to diagnose asthma. Instead, they may use this test to rule out conditions other than asthma.

Other conditions may cause similar symptoms to asthma. For example, an individual may have something stuck in their airways. An X-ray can identify any foreign objects, and it can also detect the signs of certain heart and lung conditions.

People experiencing severe asthma may also have an X-ray to test for additional diseases or damage to the lungs.

A chest X-ray can detect many potential health concerns, including:

A chest X-ray uses a special type of low dose radiation to create pictures of the chest’s interior. This type of X-ray allows a healthcare professional to see various parts of the body, including the:

  • heart
  • lungs
  • aorta
  • bones in the chest

No specific preparation is necessary for a chest X-ray. Before the exam, an individual will exchange their clothes for a medical gown and take off any metallic jewelry or other accessories.

During the exam, the individual will stand near the X-ray machine and hold still. A technician will then carry out the X-ray, which generally does not take more than a few minutes.

Finally, a radiologist will examine the results and pass them along to other doctors or specialists as necessary.

If a person undergoes an X-ray when pregnant, there is a risk that it will affect the fetus, leading to congenital abnormalities. Anyone who is pregnant, or suspects that they might be, should tell the doctor before undergoing an X-ray.

There is also a slight possibility that the radiation that the imaging tests use will increase a person’s cancer risk. However, this is not overly likely given that the tests use low doses.

Anyone concerned about radiation should speak with a doctor to learn more about the specific risks they may face.

There are many different diagnostic tests for asthma, ranging from standard physical exams to imaging tests.

Blood tests

Most asthma specialists carry out blood tests as part of a routine asthma screening. These tests can detect environmental allergies, elevated levels of white blood cells called eosinophils, and lowered vitamin D levels. They can also determine whether any organs are not functioning correctly.

Identifying one of these health issues could rule out an asthma diagnosis.

Sputum induction tests

Sputum is a combination of saliva and mucus that is present in the lungs. During a sputum induction test, a clinician will take a sample of sputum from the person’s lungs.

They will then send the sample to a laboratory for further testing to determine the cause of lung inflammation and the best treatments to pursue.

Chest CT scan

A chest CT scan takes 2D pictures of different chest cross-sections. A computer arranges these images for a healthcare professional to review.

Doctors often use this test as part of an asthma diagnosis, as it can provide evidence of the presence of asthma. It can also rule out asthma by identifying other conditions, such as bronchiectasis, which causes symptoms that may mimic asthma symptoms.

Beta-agonist and anticholinergic inhalers are useful for the immediate relief of asthma symptoms. These treatments enlarge the passageways to the lungs.

Both treatments are examples of bronchodilators that can control immediate distress by making it easier for air to come into and out of the lungs.

People may take long-term medications daily that work to keep the symptoms of asthma in check over time. These include:

People who experience asthma as part of an allergic response may also go through immunotherapy.

This treatment includes receiving regular allergy shots or taking immunotherapy pills to help reduce the frequency and severity of asthma attacks.

Learn more about asthma medications.

Most people with asthma can manage their symptoms with a combination of medication and lifestyle adjustments.

Making lifestyle adjustments can involve taking steps to avoid known triggers, which may include:

  • emotional irritability or stress
  • aspirin
  • exercise
  • breathing cold air
  • cigarette smoke
  • certain foods that trigger an allergic response

People who are unsure what may be triggering their symptoms can seek advice from a doctor on making lifestyle modifications to help them determine their triggers.

There is no single cure for asthma, but a range of treatments and techniques can keep the condition in check. The best way to determine what works is for each individual to work with a doctor to create an asthma action plan.

Learn more about controlling asthma symptoms.

Asthma is a common condition that affects many adults and children. Doctors use a variety of physical and imaging tests to diagnose asthma.

A chest X-ray may be helpful for identifying additional conditions that might be causing or exacerbating an individual’s symptoms. However, doctors cannot make an asthma diagnosis based on an X-ray alone.

People with asthma can manage the condition with medication and certain lifestyle adjustments, which can improve their quality of life.

Source link

Clinical Fellow (ST 1-2) in Respiratory Medicine

We are currently looking to recruit an enthusiastic Clinical Fellow (ST 1-2 Level equivalent) to a post in the Respiratory Department at St Bartholomew's Hospital, part of Barts Health NHS Trust. There are 2 posts available, starting in April or August October 2022. for either a 4 or 12 month period.

The Respiratory Department at St Bartholomew's Hospital is a specialist unit looking after patients with a range of problems including cystic fibrosis, home ventilation, and severe asthma. The post-holder will join a team of 4 SHO level doctors including two Core Medical trainees and 1 FY2 covering the Respiratory wards.

This post would provide an excellent opportunity both for a junior doctor having completed Foundation training and considering Core Medical Training, or a doctor having completed Core Medical Training considering a career in Respiratory Medicine

Main duties of the job

Duties of the Post

Typical Junior team ward timetable:

Mon

Tues

Wed

Thurs

Fri

Am

Radiology Teaching

Consultant Ward round

SpR Ward round

Consultant Ward round

SpR Ward Round

Lung Cancer clinic

(1 in 4)

Lung Cancer MDT

Consultant Ward Round

Pm

Departmental Teaching

Ward work & New admissions

Core Medical Trainee Teaching

Ward work & New admissions

Clinical Governance

(1 in 4)

Ward work & New admissions

Grand Round

Ward work & New admissions

Ward work & New admissions

There is a daily board round to plan discharges

There is also opportunity to experience other specialist Respiratory Clinics such as.

The post-holders will do 1 in 4 weekends (9am to 5pm) covering the Respiratory patients. They will also have weeks of weekday late shifts every 4 weeks.

The post-holder will have a supervised lung cancer outpatient clinic list every month and there is the opportunity to attend specialist respiratory outpatient clinics (for example Sleep and Ventilation and CF/Bronchiectasis clinics) and attend bronchoscopy/Endobronchial Ultrasound lists. There will be opportunities to gain experience in thoracic ultrasound and Sleep Study Reporting

About us

Barts Health is one of the largest NHS trusts in the country, and one of Britain's leading healthcare providers.

The Barts Health group of NHS hospitals is entering an exciting new era on our improvement journey to becoming an outstanding organisation with a world-class clinical reputation. Having lifted ourselves out of special measures, we now have the impetus and breathing space to chart a fresh course in which we are continually striving to improve all our services for patients.

Our vision is to be a high-performing group of NHS hospitals, renowned for excellence and innovation, and providing safe and compassionate care to our patients in east London and beyond. That means being a provider of excellent patient safety, known for delivering consistently high standards of harm-free care and always caring for patients in the right place at the right time. It also means being an outstanding place to work, in which our WeCare values and behaviours are visible to all and guide us in how we work together.

We strive to live by our WeCare values and are committed to promoting inclusion, where every staff member has a sense of belonging. We value our differences and fully advocate, cultivate and support an inclusive working environment where staff treat one another with dignity and respect. We aim to create an equitable working environment where every individual can fulfil their potential.

Job description

Job responsibilities

Clinical Responsibilities

To provide clinical care to patients attending the Barts Respiratory service based at St Bartholomews Hospital, Barts Health NHS Trust.

General Responsibilities

To provide evidence based care.

To have regard at all times to the clinical and quality standards set out within the Clinical Academic Group (CAG) & Trust guidelines

To liaise effectively and on a timely basis with colleagues within the Respiratory multidisciplinary team, other clinical specialities within the Trust particularly Diabetes, Gastroenterology & Hepatology, Rheumatology, Interventional radiology, Anaesthetics and ITU, Obstetrics, Paediatric CF (based at the Royal London Hospital), General Practitioners, community services, and all healthcare agencies.

To maintain and promote team and multi-disciplinary work, working closely with the consultant medical staff and the members of the MDT.

To provide cover for annual and study leave for colleagues so far as is practicable.

Continuing responsibility for the care of patients in his/her charge, including all administrative duties associated with patient care.

To adhere to all procedures and protocols and update them as and when necessary through a consultative process

To adhere to Trust policy and infection control principles and standards to minimise patient risk and ensure high quality patient care.

Undertake appropriate training and practice to ensure you (and your teams) have the right skills and are competent.

To be responsible for keeping the environment clutter free, clean and raising issues of concern in the interest of staff and patient safety.

Person Specification

Qualifications

Essential

  • MB BS or equivalent.
  • Full Registration with the GMC

Desirable

Clinical Knowledge & Skills

Essential

  • The applicant must be familiar with and be able to demonstrate an understanding of the major principles of the GMC's Good Medical Practice
  • The applicant must have Foundation Competencies or equivalent
  • The applicant must have experience of Audit, Research and/or Quality Improvement

Desirable

  • Current ALS certification
  • Experience of Teaching
  • At least 6 months experience of working in the NHS

Language & Communication Skills

Essential

  • The applicant must have demonstrable skills in listening, reading, writing and speaking in English that enable effective communication about medical topics with patients and colleagues, as set out in paragraph 22 of the GMC's Good Medical Practice.

Attributes

Essential

  • The applicant must demonstrate:

    • Good organisational skills
    • An understanding of the importance of the patient as the central focus of care;
    • The ability to prioritise appropriately,
    • An understanding of the importance of working effectively in a team,
    • The ability to communicate effectively with both colleagues and patients

Desirable

  • Desire to pursue a career in Respiratory Medicine

Probity

Essential

  • The applicant must have criminal records clearance at the appropriate level subject to prevailing UK legislation

 

Source link

Plain Language Summary

Chronic Obstructive Pulmonary Disease (COPD) is more common among Indigenous Australians compared to non-Indigenous Australians, with a high morbidity and mortality associated. Previous studies have established generally lower lung function parameters in the Indigenous Australian population, however there is lack of published data showing how the lung function parameters would display in the presence of underlying COPD. In this study, we reported the differences in various lung function parameters between Indigenous and non-Indigenous patients who meet criteria for COPD diagnosis in the Top End of the Northern Territory of Australia. Our study showed that Indigenous patients with COPD are generally younger, with a higher proportion of females, and a lower body mass index. They also displayed significantly lower lung function values in comparison to their non-Indigenous counterparts, even after matching against age, sex, height and smoking status with both Forced Vital Capacity (FVC) and Forced Expiratory volume in one second (FEV1) approximately 16% lower. There was no significant difference in the lower limit of normal (LLN) values between the groups. Moreover, most Indigenous patients who demonstrate airflow obstruction, defined by a fixed FEV1/FVC ratio of <0.7, also demonstrated a FEV1/FVC ratio below LLN. The current approach to COPD diagnosis, treatment and management is based on recommendations drawn from non-Indigenous populations. There could be a role for correctional factors or use of LLN values when assessing the lung function in our Indigenous patients, so that health practitioners will be able to provide more appropriate care to this unique population.

Introduction

Chronic obstructive pulmonary disease (COPD) is a debilitating condition that is characterised by chronic airway inflammation, leading to obstruction of the airway and airflow limitation.1 In the Australian context, the prevalence of COPD is estimated to be around 7.5% for individuals aged 40 years and above. Amongst those aged 75 and above, the prevalence is increased to 30%.2 Clinical manifestations of COPD include shortness of breath, chronic cough and mucous production resulting in considerable impact on quality of life.1,3,4 Furthermore, COPD is noted to be the fifth leading cause of death in Australia.5 The risk factors for COPD include; genetic predisposition, tobacco smoking, and environmental or occupational exposure to dust, gas or fumes.1,3

The Australian Institute of Health and Welfare estimates that COPD is 2.3 times more prevalent among Australians of Aboriginal or Torres Strait Islanders descent (hereafter respectfully referred to as Indigenous Australians/patients), compared to non-Indigenous Australians.4 In the Northern Territory (NT) of Australia, approximately 30% of the population self-identify as Indigenous Australian, the highest proportion compared to all other Australian states and territories.6 Several recent studies from the Top End Health Service (TEHS) region of the NT of Australia have reported a significantly higher prevalence of chronic respiratory disorders among the Indigenous population, more specifically a high prevalence of COPD.7–11

Measurement of lung function tests including spirometry, static lung volumes using plethysmography and diffusing capacity for carbon monoxide (DLCO) aid in the clinical assessment and diagnosis of COPD.12 Spirometry, when appropriately performed, is a reproducible and objective measure of airway function and is essential in the diagnosis of COPD, and grading COPD airflow obstruction severity in terms of GOLD 1 to 4 (GOLD for Global Initiative for Chronic Obstructive Lung Disease).1 Lung function parameters (LFPs) measured on spirometry include: forced vital capacity (FVC), forced expiratory volume in one second (FEV1) and FEV1/FVC ratio.1,12 FEV1 and FVC are measured in litres (L), and subsequently calculated as a percentage of predicted values (% predicted) adjusted for age, sex, height, and race/ethnicity.13 According to the GOLD criteria, a diagnosis of COPD could be considered when post-bronchodilator (BD) spirometry results demonstrate a FEV1/FVC ratio < 0.7.

Multiple studies have demonstrated significantly lower LFPs, in particular lower FVC and FEV1 values, amongst Indigenous people.14–17 These studies have consistently demonstrated that spirometric values could be up to 20 to 30% lower among Indigenous people compared to Caucasian counterparts, even among “apparently healthy” Indigenous adults.18–21 However, normative spirometric reference values for adult Indigenous Australians are yet to be established.

Currently there is sparse evidence in the literature demonstrating how spirometry parameters display in the presence of underlying COPD amongst the Indigenous Australian population in comparison to their non-Indigenous peers. Moreover, in the absence of normative spirometry reference values for adult Indigenous Australians, and in the presence of a high burden of COPD, this information is sorely needed. Hence, it is imperative to investigate the spirometric parameters among an Indigenous cohort with a diagnosis of COPD against non-Indigenous Australians. Thus, the aim of this study was to ascertain the differences in the spirometric parameters between these two diverse ethnic populations among those who had undergone spirometry testing in the TEHS region of the NT of Australia.

Patients and Methods

Setting and Study Participants

This study was undertaken at the Respiratory and Sleep service based at the Royal Darwin Hospital and Darwin Respiratory and Sleep service/Darwin Private Hospital in the TEHS region of the NT of Australia. The study participants included all patients that performed acceptable and repeatable spirometry between 2012 and 2020. Also included were datasets from previously published studies of cohorts of participants from the TEHS region NT which included Indigenous and non-Indigenous populations.15,16,21,22 This study was conducted and reported according to strengthening and reporting of health research involving Indigenous people, including consultation with local institute Indigenous Australian representatives.23 Individual consent from the study participants was not obtained, as the study was retrospective in nature and no active interventions were investigated in this study. More details regarding the setting and study participants are available from our previous reports, including details regarding lung function testing protocol and clinical data collection.14–16,21 This study was approved by the Human Research Ethics Committee of the NT, TEHS and Menzies School of Health Research (Reference no: HREC 2019–3445) and was conducted according to the Declaration of Helsinki.

Spirometry Measurements

All spirometry tests were performed according to the American Thoracic Society and the European Respiratory Society guidelines and recommendations, including calibration of equipment and quality assurance.13 Spirometry was measured using the EasyOne Pro® (ndd Medical Technologies Inc. Zurich, Switzerland). Each individual volume-time and flow-volume graph was inspected for acceptability to assess session quality. Only spirometric tests that were graded as acceptable for session quality were included in this study.

When appropriate, all patients undergoing elective spirometry testing were instructed to refrain from smoking for at least two to four hours prior to spirometry testing. Patients using airway-directed inhaled pharmacotherapy were asked to withhold their medication for 12–24 hours prior to testing. Spirometry was repeated 15–20 minutes after inhalation of 400 µg of salbutamol via a spacer to assess BD responsiveness.1,2 Throughout this study the National Health And Nutrition Examination Survey (others/Caucasian) reference values were utilised, as is standard in clinical practice in this setting.24 If the study participants were identified to have undergone multiple LFTs during the study period, the earliest acceptable test for session quality was utilised for analysis. Spirometry parameters included were pre- and post-BD FEV1, FVC absolute values, % predicted and lower limits of normal (LLN), and FEV1/FVC (absolute and LLN).

Inclusion Criteria for Presence of COPD

To ascertain the presence of COPD, post-BD FEV1/FVC ratio < 0.7 was used according to GOLD and the Australian concise tool for COPD (COPD-X) recommendations.1,3

Matching of Indigenous and Non-Indigenous Patients with COPD

To assess differences in spirometry parameters, a sub-set analysis was conducted in a cohort of matched Indigenous and non-Indigenous patients. Matching of Indigenous and non-Indigenous patients was conducted based on four categories: sex (male/female); smoking status (never/former/current smoker); age (18–35, 36–50, 51–65, 66–75, 76–85, >85 years); height (cm) (females: < 150, 150–170, > 170, males: < 160, 160–180, > 180). These categorisation criteria were first applied to the Indigenous patient cohort of 240 patients with COPD, and then to the non-Indigenous cohort to identify patients with matched categories. There was an excess of Indigenous patients in multiple categories as compared to the non-Indigenous cohort. There was a greater proportion of Indigenous patients in the younger, shorter and current smoking categories. As such, further refinement was conducted to give an equal number of Indigenous and non-Indigenous patients for each category. In the case of an inadequate number of non-Indigenous patients fitting to an Indigenous patient category or vice versa, the excess patients were dropped from the dataset, resulting in a total of 222 matched patients (111 Indigenous, 111 non-Indigenous).

Statistical Analysis

Continuous parameters were tested for normality via the Shapiro Wilks distribution test, with body mass index (BMI) and smoking pack years displaying a non-parametric distribution, and other continuous variables approximating normal distribution. Non-parametric parameters were presented as medians (interquartile ranges (IQR)), normally distributed parameters as means (95% confidence intervals (CIs)), and categorical parameters as numbers (%). Clinical characteristics were compared between Indigenous and non-Indigenous patients by 2-tailed Student's t-test for normally distributed parameters. Equality of medians test was used for non-parametrically distributed parameters and 2-tailed proportions z-test for categorical parameters. LFPs were compared between Indigenous and non-Indigenous patients by 2-tailed Student's t-test. For both matched and unmatched cohorts Hedges G effect size was calculated for FVC % predicted, FEV1% predicted and FEV1/FVC ratio and reported as G (95% CI).25 Hedges G effect size was classified as small (0.2 < 0.5), moderate (0.5 < 0.8) or large (>0.8). All data were analysed in STATA IC 15 (StataCorp, Texas) and alpha set to 0.05 throughout.

Results

A total of 5321 patients were referred for at least one spirometry test between 2012 and 2020. From the total cohort, 240/742 (32%) Indigenous and 873/4579 (19%) non-Indigenous patients were identified as meeting diagnostic criteria for COPD (Figure 1). There was a significantly larger proportion of Indigenous patients with COPD compared to non-Indigenous patients (32% vs 19%, p <0.0001). The demographic/clinical factors for these patients differed significantly by Indigenous status (Table 1).

Table 1 Clinical Characteristics of Indigenous and Non-Indigenous Patients with COPD

Figure 1 Flow chart of Indigenous and non-Indigenous study participants’ inclusion criteria.

Abbreviation: COPD, chronic obstructive pulmonary disease.

Indigenous patients had significantly lower values for all spirometry parameters, aside from FEV1 LLN values (Table 2). The post-BD FVC % predicted, FEV1% predicted and FEV1/FVC ratio were a mean 17%, 17%, and 2 points lower respectively among Indigenous patients with COPD in comparison to non-Indigenous patients. In addition to having FEV1/FVC ratio < 0.7, a significantly greater proportion of the Indigenous cohort also displayed FEV1/FVC ratio < LLN (92% vs 71%, p<0.001). Hedges G effect size for differences in percent predicted values was large for FVC (0.91, 95% CI 0.77, 1.06), FEV1 (0.87, 95% CI 0.72, 1.01), and small for FEV1/FVC (absolute) (0.25, 95% CI 0.11, 0.39).

Table 2 Lung Function Parameters (LFPs) of Indigenous and Non-Indigenous Patients with COPD

Matched Data for Indigenous and Non-Indigenous with COPD

A total of 222 patients were matched (111 Indigenous, 111 non-Indigenous) with no statistically significant differences in age, sex, height or smoking status. Despite matching, among the Indigenous cohort a greater proportion of patients were in the underweight BMI category, and among the non-Indigenous patients a greater median pack years of smoking was reported (Table 3). LFPs between the Indigenous and non-Indigenous patients with COPD in each sub-divided aged groups (18–35, 36–50, 51–65, 66–75, 76–85, >85 years) are represented in Table 1S.

Table 3 Clinical Characteristics of Patients Included in Matched Cohorts (n=222)

Among the matched cohort significant differences were noted in all LFPs aside for LLN values, once again with Indigenous patients displaying lower values than non-Indigenous patients with COPD (Table 4). Post-BD percent predicted values for FVC, FEV1, and FEV1/FVC ratio again displayed a mean 16%, 16%, and 4 points lower respectively among Indigenous patients with COPD in comparison to non-Indigenous patients (Figure 2). Hedges G effect size was large for FVC % predicted (0.94, 95% CI 0.66, 1.22), FEV1% predicted (0.92, 95% CI 0.64, 1.19) and small for FEV1/FVC ratio (0.41, 95% CI 0.15, 0.68).

Table 4 Lung Function Parameters (LFPs) for Matched Indigenous and Non-Indigenous Patients with COPD

Figure 2 Percentage predicted values of FVC, FEV1 and FEV1/FVC ratio (mean (95% CI)) post-BD for unmatched and matched cohorts of Indigenous and non-Indigenous patients with post-BD FEV1/FVC < 0.7.

Abbreviations: BD, bronchodilator; FEV1, forced expiratory volume in one second; FVC, forced vital capacity.

Sub-Set Analysis

Within the cohort of COPD patients 220/240 (92%) Indigenous and 622/873 (71%) non-Indigenous patients were recorded to have FEV1/FVC < LLN, of which 150 (75 Indigenous and non-Indigenous) were able to be matched (Tables 2S and 5). Pre- and post-BD parameters for FVC and FEV1 were significantly lower among the Indigenous cohort in both matched and unmatched subgroups (mean difference in post-BD FVC % predicted and FEV1% predicted; matched: 15% and 12% and unmatched 15% and 13%). In the unmatched subgroup, LLN significantly differed for FVC and FEV1/FVC, while in the matched subgroup there were no significant differences. FEV1/FVC values did not significantly differ between Indigenous and non-Indigenous patients in the matched subgroup.

Table 5 Lung Function Parameters for Matched Indigenous and Non-Indigenous Patients with FEV1/FVC < LLN

Discussion

To the best of the authors’ knowledge, this is the first study to match and compare spirometry parameters between Indigenous and non-Indigenous Australian patients diagnosed with COPD, specifically from the NT of Australia. This study has demonstrated several key findings:

  1. In patients with COPD, Indigenous patients are significantly younger, and have lower BMI compared to non- Indigenous patients.
  2. Indigenous patients with COPD display significantly lower spirometry results, both as absolute values and as a percent of predicted in comparison to their non-Indigenous counterparts, even after matching against age, sex, height and smoking status.
  3. The FVC and FEV1 parameters are significantly lower for Indigenous patients, despite no significant difference in the LLN values between the groups, especially among the matched cohort.
  4. Most Indigenous patients who demonstrate airflow obstruction, defined by a fixed FEV1/FVC ratio of < 0.7, also have a FEV1/FVC ratio below the LLN.

The burden of chronic respiratory diseases is reported to be highly prevalent among various Indigenous populations globally.7–11,26–29 Existing literature has demonstrated that LFPs generally tend to be lower among Indigenous people.14–21 There is, however, sparse published quantitative data directly comparing LFPs for patients with COPD among diverse ethnic populations, including the Indigenous Australian population. Hence, the results represented in this study are of significant relevance in bridging our gap in knowledge. Moreover, this study further strengthens the notion that indeed, Indigenous Australians have lower LFPs even in the presence of underlying respiratory conditions such as COPD.

In this study, the absolute FEV1 and percent predicted values in Indigenous patients with COPD was observed to be significantly lower in comparison to non-Indigenous patients. It may be reasonable to speculate this truly reflects the severity of underlying disease among Indigenous patients with COPD, hence, the lower FEV1 values noted. However, even after controlling for age, sex, height and smoking status, these differences still persisted. Hence, an alternative explanation is plausible for this observation.

In line with observed lower FEV1, the FVC value was also similarly lower among Indigenous patients. The lower FVC values have consistently been observed in previous reports among Indigenous Australians.14,16,19,21,30,31 This may be suggestive of a restrictive ventilatory impairment or a combination of obstructive and restrictive impairments (ie; mixed impairment). The authors do acknowledge that static lung volumes as measured by plethysmography are required to accurately identify restrictive ventilatory impairment. However, it was beyond the scope of this study to assess plethysmography data. Nonetheless, in the real-world scenario, irrespective of the presence of any form or type of underlying respiratory condition, a restrictive ventilatory impairment is one of the most common spirometry patterns observed in this Indigenous population, including among a significant proportion of patients with COPD.16 The lower FVC (a restrictive ventilatory impairment) may indicate the presence of underlying conditions such as interstitial lung disease (ILD), chest wall deformities or neuromuscular disorders.32 However, the aforementioned respiratory conditions are very unlikely, as previous reports on chest radiological data have shown that respiratory conditions such as ILD are very rarely observed among this Indigenous Australian population.8,9 Similar to this study, the lower FVC has been reported in a subset of African ethnic population, indicating certain ethnic populations inherently demonstrate lower FVC values.33 Hence, it may be reasonable to speculate that a myriad of intrinsic or extrinsic factors34 may be responsible for the observed lower FVC values in this population, such as: genetics,35 ethnicity,36 birth weight,37 nutrition,38 childhood infections,39 and smoking.40

Although controversy exists if FEV1/FVC fixed ratio < 0.7 or the FEV1/FVC < LLN should be utilised in accurately diagnosing patients with COPD,41–43 it appears that the FEV1/FVC< LLN criteria is gaining popularity.44 However, due to lack of established spirometry reference norms for adult Indigenous Australians, currently there is sparse published data in the literature to demonstrate how the LLN parameters would display among Indigenous patients in comparison to non-Indigenous patients.45 In this study, we observed that the LLN values did not significantly differ between the Indigenous and non-Indigenous patients. Moreover, among Indigenous patients who demonstrated airflow obstruction by FEV1/FVC fixed ratio < 0.7 criteria, 92% of them also demonstrated to fit the FEV1/FVC < LLN criteria, whereas only 71% of the non-Indigenous patients did so. In light of this study’s findings and in the absence of spirometry reference norms for adult Indigenous Australians, in the authors’ opinion, the LLN parameters may be a better marker in the clinical decision making for the adult Indigenous Australians.

This study has highlighted that health practitioners caring for Indigenous patients should be aware of the implications in utilising spirometry criteria in COPD diagnosis and severity classification when adopting reference values drawn from non-Indigenous populations. A recent study has highlighted, irrespective of which of the recommended classifications used, COPD-X46 or GOLD,1 most Indigenous patients with COPD will likely fall into either the “severe” or “very severe” category.22 Indigenous patients in this study demonstrated 16–17% lower values for FVC and FEV1, compared to non-Indigenous patients. It is inevitable that an Indigenous patient with COPD will likely be assessed to have a higher disease burden by adopting spirometry reference values for the non-Indigenous population. In the aforementioned report on spirometry data in Indigenous population, creating a model by arbitrarily inflating FVC and FEV1 values by 15% (arbitrarily applying a correction factor for Indigenous patients) resulted in a re-categorization of 80% of patients to either “mild” or “moderate” airflow obstruction severity categories - A much closer approximation to the distribution found in non-Indigenous populations.22

Understandably, lack of normative lung function reference values may impose unprecedented diagnostic and management challenges among adult Indigenous patients with respiratory conditions, including COPD. Nonetheless, this study has highlighted that there are significant differences along with lower spirometry parameters among the adult Indigenous patients with COPD, in comparison to non-Indigenous patients. Furthermore, despite no significant differences in the LLN FEV1/FVC ratio values, the FEV1 values were noted to be substantially lower for Indigenous patients. Once again, as mentioned above, it is unclear at this stage if this truly reflects greater COPD disease burden due to higher smoking prevalence9,10 amongst Indigenous people or if it is due to inherent lower LFPs values in this Indigenous population.14,16,19,21,30,31 Indeed, future research should be directed toward elucidating the lived experience of Indigenous Australian patients with respiratory conditions in order to better correlate LFPs with the subjective experience on quality of life and the appropriateness of varied management and treatment plans.

We presume that the results of this study may be of interest for Indigenous health practitioners and primary care physicians. It is not clear at this stage if a correction factor for spirometry could be utilised in day-to-day diagnostic and clinical decision making for Indigenous patients presenting with COPD. There has been significant progress in the recent past in addressing respiratory health issues among the adult Indigenous Australian population, especially, from the Top End NT of Australia.7–11,14–17,21,22,47–59 Further studies, however, may be useful to better understand if our study findings are comparable to other Indigenous population both in other parts of Australian and globally, that may change the diagnostic and management of COPD paradigm among Indigenous populations.

Limitations

Caution must be exercised in relation to the applicability of the study outcomes in other Indigenous populations, as such, our data is only representative of patients residing in the TEHS region of the NT of Australia. Moreover, the Indigenous population in Australia is a diverse group of people. There exists a proportion of individuals who are descendants of inter-racial relationships, many of whom identify as Indigenous. Furthermore, although we could anticipate and extrapolate a similar trend to other Indigenous population, there may be other variables such as climate (desert vs tropical) or living environments (remote vs metropolitan) that could affect the development of lung and LFPs. The authors also acknowledge that the number of study participants in the matched group may be less than desired and radiological data was not assessed for the severity of underlying COPD between the two groups. It may be reasonable to accept this limitation, as such significant proportion of Indigenous people reside in remote and regional communities, hence imposing substantial challenges in conducting prospective studies. Furthermore, even among the matched cohort, the BMI significantly differed, indicating much reduced weight among the Indigenous cohort, which is reflective of the socioeconomic situation of many Indigenous patients. This study also did not include static lung volumes and DLCO data. Nonetheless, this is the first study to demonstrate differences in the spirometry parameters among Indigenous patients with COPD against non-Indigenous patients and there may be avenues in the future for further prospective research.

Conclusion

Our current approach to all patients with COPD is often based on guidelines that provide recommendations drawn from non-Indigenous general population. In this study we have demonstrated that Indigenous patients with COPD have substantially lower spirometry values for FVC and FEV1, even after matching for age, sex, height and smoking status. In light of this study’s findings, a more tailored approach to the diagnosis and management of this unique Indigenous population with COPD is required, enabling us to improve quality of life and close the health gap.60

Abbreviations

BD, bronchodilator; BMI, body mass index; CI, confidence interval; COPD, chronic obstructive pulmonary disease; COPD-X, concise tool for chronic obstructive pulmonary disease; DLCO, diffusing capacity for carbon monoxide; FEV1, forced expiratory volume in one second; FVC, forced vital capacity; GOLD, global initiative for chronic obstructive lung disease; ILD, interstitial lung disease; IQR, interquartile range; LFPs, lung function parameters; LLN, lower limit of normal; NT, Northern territory; TEHS, top end health service.

Acknowledgments

We sincerely thank all the respiratory technologists and Respiratory Clinical Nurse Consultants from Darwin Respiratory and Sleep Health and Royal Darwin Hospital, Darwin Private Hospital, Darwin, Australia during this study. We also thank Ms Amelia Skaczkowskit, Flinders University, Northern Territory Medical Program medical student for helping with data collection for this research. We also extend our sincere appreciation to our remote community Indigenous health workers and also Mr Izaak Thomas (Australian Indigenous Luritja descendent) from the respiratory chronic respiratory disease co-ordination division in approving this research addressing much needed data in the diagnosis and management of adult Indigenous patients with respiratory disorders and for the appropriateness and respect in relation to the Indigenous context represented in this study.

Author Contributions

All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; agreed to submit to the current journal; gave final approval of the version to be published; and agree to be accountable for all aspects of the work.

Funding

Nil to declare.

Disclosure

All authors declare no conflicts of interest for this study.

References

1. Global initiative for chronic obstructive lung disease. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease; 2021. Available from: www.goldcopd.org. Accessed March 27, 2022.

2. Toelle BG, Xuan W, Bird TE, et al. Respiratory symptoms and illness in older Australians: the Burden of Obstructive Lung Disease (BOLD) study. Med J Aust. 2013;198(3):144–148. doi:10.5694/mja11.11640

3. Yang IA, George J, Jenkins S, et al. The COPD-X plan: Australian and New Zealand guidelines for the management of Chronic Obstructive Pulmonary Disease 2021. Version 2.63; February, 2021. Available from: copdx.org.au. Accessed March 27, 2022.

4. Australian Institute of Health and Welfare. Chronic obstructive pulmonary disease (COPD). Cat. no. ACM 35. Canberra: AIHW; 2020. Available from: www.aihw.gov.au/reports/chronic-respiratory-conditions/copd. Accessed March 27, 2022.

5. Australian Institute of Health and Welfare. Deaths in Australia. Cat. no. PHE 229. Canberra: AIHW; 2021. Available from: www.aihw.gov.au/reports/life-expectancy-death/deaths-in-australia. Accessed March 27, 2022.

6. Australian Bureau of Statistics. Estimates of Aboriginal and Torres Strait Islander Australians. Canberra, Australia: ABS; 2016.

7. Mehra S, Chang AB, Lam CK, et al. Bronchiectasis among Australian aboriginal and non-aboriginal patients in the regional and remote population of the Northern Territory of Australia. Rural Remote Health. 2021;21(2):6390. doi:10.22605/RRH6390

8. Heraganahally SS, Timothy TP, Sorger L. Chest computed tomography findings among adult Indigenous Australians in the Northern Territory of Australia. J Med Imaging Radiat Oncol. 2021;66(3):337–344. doi:10.1111/1754-9485.13295

9. Kruavit A, Fox M, Pearson R, Heraganahally S. Chronic respiratory disease in the regional and remote population of the Northern Territory Top End: a perspective from the specialist respiratory outreach service. Aust J Rural Health. 2017;25(5):275–284. doi:10.1111/ajr.12349

10. Heraganahally S, Wasgewatta SL, McNamara K, et al. Chronic obstructive pulmonary disease in aboriginal patients of The Northern Territory of Australia: a landscape perspective. Int J Chron Obstruct Pulmon Dis. 2019;14:2205–2217. doi:10.2147/COPD.S213947

11. Heraganahally SS, Wasgewatta SL, McNamara K, et al. Chronic obstructive pulmonary disease with and without bronchiectasis in aboriginal Australians – a comparative study. Int Med J. 2020;50(12):1505–1513. doi:10.1111/imj.14718

12. Ranu H, Wilde M, Madden B. Pulmonary function tests. Ulster Med J. 2011;80(2):84–90.

13. Miller MR, Hankinson J, Brusasco V, et al. Standardisation of spirometry. “ATS/ERS task force: standardisation of lung function testing”. Eur Respir J. 2005;26(2):319–338. doi:10.1183/09031936.05.00034805

14. Schubert J, Kruavit A, Mehra S, Wasgewatta S, Chang AB, Heraganahally S. Prevalence and nature of lung function abnormalities among Indigenous Australians referred to specialist respiratory outreach clinics in the Northern Territory. Int Med J. 2019;49(2):217–224. doi:10.1111/imj.14112

15. Heraganahally SS, Ponneri TR, Howarth TP, Saad HB. The effects of inhaled airway directed pharmacotherapy on decline in lung function parameters among indigenous Australian adults with and without underlying airway disease. Int J Chron Obstruct Pulmon Dis. 2021;16:2707–2720. doi:10.2147/COPD.S328137

16. Heraganahally SS, Howarth T, Mo L, Sorger L, Saad HB. Critical analysis of spirometric patterns in correlation to chest computed tomography among adult Indigenous Australians with chronic airway diseases. Expert Rev Respir Med. 2021;15(9):1229–1238. doi:10.1080/17476348.2021.1928496

17. Howarth TP, Saad HB, Perez AJ, Atos CB, White E, Heraganahally SS. Comparison of diffusing capacity of carbon monoxide (DLCO) and total lung capacity (TLC) between Indigenous Australians and Australian Caucasian adults. PLoS One. 2021;16(4):e0248900. doi:10.1371/journal.pone.0248900

18. de Hamel FA, Glass WJ. Observations on Maori-European lung function differences. Aust N Z J Med. 1975;5(1):44–48. doi:10.1111/j.1445-5994.1975.tb03254.x

19. Cooksley NA, Atkinson D, Marks GB, et al. Prevalence of airflow obstruction and reduced forced vital capacity in an Aboriginal Australian population: the cross-sectional BOLD study. Respirology. 2015;20(5):766–774. doi:10.1111/resp.12482

20. Veale AJ, Peat JK, Salome CM, Woolcock AJ, Thompson JE. ‘Normal’ lung function in rural Australian aborigines. Aust N Z J Med. 1997;27(5):543–549. doi:10.1111/j.1445-5994.1997.tb00962.x

21. Heraganahally SS, Howarth T, White E, Sorger L, Biancardi E, Ben Saad H. Lung function parameters among Australian Aboriginal “apparently healthy” adults: an Australian Caucasian and global lung function initiative (GLI-2012) various ethnic norms comparative study. Expert Rev Respir Med. 2020;23:1–11. doi:10.1080/17476348.2021.1847649

22. Heraganahally S, Howarth TP, White E, Ben Saad H. Implications of using the GLI-2012, GOLD and Australian COPD-X recommendations in assessing the severity of airflow limitation on spirometry among an Indigenous population with COPD: an Indigenous Australians perspective study. BMJ Open Respir Res. 2021;8(1):e001135. doi:10.1136/bmjresp-2021-001135

23. National Health and Medical Research Council. Ethical conduct in research with Aboriginal and Torres Strait Islander Peoples and communities: guidelines for researchers and stakeholders. Canberra: Commonwealth of Australia; 2018.

24. Hankinson JL, Odencrantz JR, Fedan KB. Spirometric reference values from a sample of the general US population. Am J Respir Crit Care Med. 1999;159(1):179–187. doi:10.1164/ajrccm.159.1.9712108

25. Olejnik S, Algina J. Measures of effect size for comparative studies: applications, interpretations, and limitations. Contemp Educ Psychol. 2000;25(3):241–286. doi:10.1006/ceps.2000.1040

26. State of the world’s Indigenous peoples. Indigenous peoples’ access to health services. United nations; 2018. Available from: www.un.org/development/desa/indigenouspeoples/wp-content/uploads/sites/19/2018/03/The-State-of-The-Worlds-Indigenous-PeoplesWEB.pdf. Accessed March 27, 2022.

27. Bird Y, Moraros J, Mahmood R, et al. Prevalence and associated factors of COPD among Aboriginal peoples in Canada: a cross-sectional study. Int J Chron Obstruct Pulmon Dis. 2017;30(12):1915–1922. doi:10.2147/COPD.S138304

28. Australian institute of health and welfare. Indigenous Australians more likely to die from CHD and COPD, but some positive trends emerging - Australian Institute of Health and Welfare; 2014. Available from: www.aihw.gov.au/news-media/media-releases/2014/june/indigenous-australians-more-likely-to-die-from-chd. Accessed March 27, 2022.

29. Ospina MB, Voaklander DC, Stickland MK. Prevalence of asthma and chronic obstructive pulmonary disease in Aboriginal and non-Aboriginal populations: a systematic review and meta-analysis of epidemiological studies. Can Respir J. 2012;19(6):355–360. doi:10.1155/2012/825107

30. White E, James A, de Klerk N, Musk A, Hall G. Selection of appropriate spirometry reference values in Aboriginal Australians. Australian Indigenous Health Bulletin; 2019;19.

31. Hall GL, Pearson G. Reduced forced vital capacity in Aboriginal Australians: biology or missing evidence? Respirology. 2015;20(5):693–694. doi:10.1111/resp.12552

32. Martinez-Pitre PJ, Sabbula BR, Cascella M. Restrictive lung disease. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2021 [Updated July 31, 2021]. Available from www.ncbi.nlm.nih.gov/books/NBK560880/. Accessed March 27, 2022.

33. Obaseki DO, Erhabor GE, Awopeju OF, et al. Reduced forced vital capacity in an African population. Prevalence and risk factors. Ann Am Thorac Soc. 2017;14(5):714–721. doi:10.1513/AnnalsATS.201608-598OC

34. Stanojevic S, Wade A, Stocks J. Reference values for lung function: past, present and future. Eur Respir J. 2010;36(1):12–19. doi:10.1183/09031936.00143209

35. Kumar R, Seibold MA, Aldrich MC, et al. Genetic ancestry in lung-function predictions. N Engl J Med. 2010;363(4):321–330. doi:10.1056/NEJMoa0907897

36. Braun L, Wolfgang M, Dickersin K. Defining race/ethnicity and explaining difference in research studies on lung function. Eur Respir J. 2013;41(6):1362–1370. doi:10.1183/09031936.00091612

37. Westrupp EM, D’Esposito F, Freemantle J, Mensah FK, Nicholson JM. Health outcomes for Australian Aboriginal and Torres Strait Islander children born preterm, low birthweight or small for gestational age: a nationwide cohort study. PLoS One. 2019;14(2):e0212130. doi:10.1371/journal.pone.0212130

38. Gaultier C, Crapo R. Effects of nutrition, growth hormone disturbances, training, altitude and sleep on lung volumes. Eur Respir J. 1997;10(12):2913–2919. doi:10.1183/09031936.97.10122913

39. Howarth T, Brunette R, Davies T, et al. Antibiotic use for Australian Aboriginal children in three remote Northern Territory communities. PLoS One. 2020;15(4):e0231798. doi:10.1371/journal.pone.0231798

40. Fenton ME, Graham BL, Stanojevic S, Whitford L, Ironstand L. Interpretation of Spirometry in Saskatchewan First Nations Adults. Ann ATS. 2018;15(10):1237–1239. doi:10.1513/AnnalsATS.201711-909RL

41. Swanney MP, Ruppel G, Enright PL, et al. Using the lower limit of normal for the FEV1/FVC ratio reduces the misclassification of airway obstruction. Thorax. 2008;63(12):1046–1051. doi:10.1136/thx.2008.098483

42. Hoesein FAAM, Zanen P, Lammers JJ. Lower limit of normal or FEV1/FVC <0.70 in diagnosing COPD: an evidence-based review. Respir Med. 2011;105(6):907–915. doi:10.1016/j.rmed.2011.01.008

43. Dijk WV, Tan W, Li P, et al. Clinical relevance of fixed ratio vs lower limit of normal of FEV1/FVC in COPD: patient-reported outcomes from the CanCOLD cohort. Ann Fam Med. 2015;13(1):41–48. doi:10.1370/afm.1714

44. Stanojevic S, Kaminsky DA, Miller M, et al. ERS/ATS technical standard on interpretive strategies for routine lung function tests. Eur Respir J. 2021:2101499. doi:10.1183/13993003.01499-2021.

45. Blake TL, Chang AB, Petsky HL, et al. Spirometry reference values in Indigenous Australians: a systematic review. Med J Aust. 2016;205(1):35–40. doi:10.5694/mja16.00226

46. Stepwise management of stable COPD. Lung Foundation Australia; 2021. Available from lungfoundation.com.au/resources/stepwise-management-of-stable-copd/. Accessed March 27, 2022.

47. Heraganahally SS, Silva SAMS, Howarth TM, Kangaharan N, Majoni SW. Comparison of clinical manifestation among Australian Indigenous and non‐ Indigenous patients presenting with pleural effusion. Int Med J. 2021. doi:10.1111/imj.15310

48. Heraganahally SS, Mortimer N, Howarth T, et al. Utility and outcomes among Indigenous and non-Indigenous patients requiring domiciliary oxygen therapy in the regional and rural Australian population. Aust J Rural Health. 2021;29(6):918–926. doi:10.1111/ajr.12782

49. Heraganahally SS, Kruavit A, Oguoma VM, et al. Sleep apnoea among Australian Aboriginal and Non- Aboriginal patients in the Northern Territory of Australia– a comparative study. Sleep. 2020;43(3):zsz248. doi:10.1093/sleep/zsz248

50. Mehra S, Ghimire RH, Mingi JJ, et al. Gender differences in the clinical and polysomnographic characteristics among Australian aboriginal patients with obstructive sleep apnea. Nat Sci Sleep. 2020;12:593–602. doi:10.2147/NSS.S258330

51. Garg H, Er XY, Howarth T, Heraganahally SS. positional sleep apnea among regional and remote Australian population and simulated positional treatment effects. Nat Sci Sleep. 2020;12:1123–1135. doi:10.2147/NSS.S286403

52. Heraganahally SS, Kerslake C, Issac S, et al. Outcome of public hospital-funded continuous positive airway therapy device for patients with obstructive sleep apnoea: an Australian perspective study. Sleep Vigilance. 2020;4(2):195–204. doi:10.1007/s41782-020-00114-4

53. Heraganahally SS, Zaw KK, Tip S, et al. Obstructive sleep apnoea and adherence to continuous positive airway therapy among Australian women. Int Med J. 2022;52(3):440–450. doi:10.1111/imj.15076

54. Heraganahally SS, Rajaratnam B, Silva SAAS, et al. Obstructive sleep apnoea and cardiac disease among aboriginal patients in the Northern Territory of Australia. Heart Lung Circ. 2021;30(8):1184–1192. doi:10.1016/j.hlc.2021.01.007

55. Benn E, Wirth H, Short T, Howarth T, Heraganahally SS. The Top End Sleepiness Scale (TESS): a new tool to assess subjective daytime sleepiness among indigenous Australian adults. Nat Sci Sleep. 2021;13:315–328. doi:10.2147/NSS.S298409

56. Heraganahally SS, Howarth TP, Wirth H, Short T, Benn E. Validity of the New “Top End Sleepiness Scale” (TESS) against the STOP-bang tool in predicting obstructive sleep apnoea among Indigenous Australian Adults. Intern Med J. 2021. doi:10.1111/imj.15633

57. Heraganahally SS, White S. A cost-effective novel innovative box (C-box) to prevent cockroach infestation of continuous positive airway pressure equipment: a unique problem in Northern Tropical Australia. Am J Trop Med Hyg. 2019;101(4):937–940. doi:10.4269/ajtmh.19-0434

58. Heraganahally SS, Ghimire RH, Howarth T, Kankanamalage OM, Palmer D, Falhammar H. Comparison and outcomes of emergency department presentations with respiratory disorders among Australian Indigenous and non-Indigenous Patients. BMC Emerg Med. 2022;22(1):11. doi:10.1186/s12873-022-00570-3

59. Heraganahally SS, Howarth T, Sorger L, Ben Saad H. Sex differences in pulmonary function parameters among Indigenous Australians with and without chronic airway disease. PLoS One. 2022;17(2):e0263744. doi:10.1371/journal.pone.0263744

60. Closing the Gap: Indigenous Health Campaign. Closing the gap in partnership: everyone enjoys long and healthy lives. Canberra: Australian Human Rights Commission; March 18, 2021. [cited May 4, 2021]. Available from: www.closingthegap.gov.au/everyone-enjoys-long-and-healthy-lives.

Source link

Global Lung (Pulmonary) Airway Disease Treatment Market: Dynamics 

The key driver to the Lung (Pulmonary) Airway Disease treatment market is the rising prevalence of respiratory diseases such as asthma, cystic fibrosis, and COPD is increasing significantly across the world and. This is primarily because older adults are highly susceptible to respiratory diseases (COPD, asthma, emphysema) due to reduced immunity and blood flow. Furthermore, introduction of advanced imaging technologies such as digital radiography and computed radiography are other major drivers of the market; this is because these technologies render high-quality digital images. Digital imaging systems have witnessed low adoption rate as these are more expensive than analog imaging systems, this can restrain the Lung (Pulmonary) Airway Disease treatment market.

Lung (Pulmonary) Airway Disease affects the airways that transport oxygen and other gases in and out of the lungs. The Lung (Pulmonary) Airway Disease narrow down or block the airways. Such breathing problems caused due to Lung (Pulmonary) Airway Disease may inhibit the body receiving sufficient oxygen. Lung (Pulmonary) Airway Disease include bronchiectasis, asthma, and COPD. People affected with Lung (Pulmonary) Airway Disease often feel as if they are breathing out through a straw. In Respiratory subdivision of NHLI, the Lung (Pulmonary) Airway Disease shares a prominent share, and it affects significant population in Europe. Chronic obstructive pulmonary disease (COPD) is a kind of obstructive lung disease that affects the airflow for a longer period with shortness of breath and sputum production.

To remain ahead of your competitors, request for a sample – www.futuremarketinsights.com/reports/sample/rep-gb-4359

Global Lung (Pulmonary) Airway Disease Treatment Market: Region-wise Outlook

Geographically, North America was the largest market for Lung (Pulmonary) Airway Disease Treatment and the region is anticipated to maintain its dominance during the forecast period. The dominance of North America was due to increasing prevalence of respiratory diseases, rising popularity of portable devices, and growing demand for home health care devices and services. The American Lung Association states that COPD is the third leading cause of death in the U.S. Moreover, rising demand for technologically advanced and innovative products in hospitals, diagnostic laboratories, and outpatient ambulatory surgery centers would fuel market growth in the region. Europe accounted for the second largest share of the global Lung (Pulmonary) Airway Disease Treatment market. However, Asia-Pacific is expected to expand at the highest CAGR during 2016-2024. This growth is mainly attributed to factors such as untapped opportunities, improving health care infrastructure, and increasing awareness about available diagnostic procedures. Rapidly increasing number of hospitals is leading to higher demand for imaging technologies in hospital and laboratory settings, thereby driving the Lung (Pulmonary) Airway Disease Treatment market. Furthermore, the Lung (Pulmonary) Airway Disease Treatment market in Latin America is likely to expand at a significant CAGR. Brazil and Mexico are driving the Lung (Pulmonary) Airway Disease Treatment market in the region due to favorable initiatives were taken by the respective governments. Moreover, rising health care infrastructure would fuel market growth in Latin America.

Global Lung (Pulmonary) Airway Disease Treatment Market: Key Players

Currently, the global Lung (Pulmonary) Airway Disease Treatment market is highly competitive owing to the involvement of many established players. Some of the key players in the global Lung (Pulmonary) Airway Disease Treatment market are Holaira, Inc., VIDA Diagnostics, Boehringer Ingelheim International GmBH, AstraZeneca, Teva Pharmaceuticals,  GlaxoSmithKline, and Novartis.

Get a Tailored Made Report to Match Your requirements, Ask from Market Research Expert – www.futuremarketinsights.com/ask-question/rep-gb-4359

The report covers exhaustive analysis on:

  • Market Segments
  • Market Dynamics
  • Historical Actual Market Size, 2012 – 2016
  • Market Size & Forecast 2017 to 2027
  • Supply & Demand Value Chain
  • Market Current Trends/Issues/Challenges
  • Competition & Companies involved
  • Technology
  • Value Chain
  • Aircraft Refurbishing Market Drivers and Restraints

Regional analysis includes

  • North America
  • Latin America
  • Europe
  • Asia Pacific
  • Middle East & Africa

The report is a compilation of first-hand information, qualitative and quantitative assessment by industry analysts, inputs from industry experts and industry participants across the value chain. The report provides in-depth analysis of parent market trends, macro-economic indicators and governing factors along with market attractiveness as per segments. The report also maps the qualitative impact of various market factors on market segments and geographies.

Global Lung (Pulmonary) Airway Disease Treatment Market: Segmentation 

On the basis of Type, the Global Lung (Pulmonary) Airway Disease Treatment market can be segmented into,

  • Asthma
  • Chronic Obstructive Pulmonary Disorder
  • Bronchiectasis

On the basis of treatment, the Global Lung (Pulmonary) Airway Disease Treatment market can be segmented into

  • Bronchodilators
  • Corticosteroids
  • Cytotoxic Drugs
  • Oxygen Therapy
  • Antibiotics
  • Others

On the basis of end-user, the Global Lung (Pulmonary) Airway Disease Treatment market can be segmented into,

  • Hospitals
  • Clinics
  • ASC’s
  • Rehabilitation Centers
  • Others

On the basis of Region, the Global Lung (Pulmonary) Airway Disease Treatment market can be segmented into,

  • North America
  • Western Europe
  • Eastern Europe
  • Asia Pacific Excluding Japan
  • Japan
  • Latin America
  • Middle East & Africa

For in-depth insights, Download a PDF Brochure – www.futuremarketinsights.com/reports/brochure/rep-gb-4359

Report Highlights:

  • Detailed overview of parent market
  • Changing market dynamics in the industry
  • In-depth market segmentation
  • Historical, current and projected market size in terms of volume and value
  • Recent industry trends and developments
  • Competitive landscape
  • Strategies of key players and products offered
  • Potential and niche segments, geographical regions exhibiting promising growth
  • A neutral perspective on market performance
  • Must-have information for market players to sustain and enhance their market footprint.

Source link

Introduction

Chronic respiratory disease (CRD) (chronic diseases of the airways and the other structures of the lungs) is associated with cigarette smoking, fumes from cooking on open stoves, air pollution,1 and pulmonary tuberculosis,2 and is highly prevalent across low- and middle-income countries (LMIC).3 Physical behaviours include all activities over a 24-h period across the movement spectrum, from no/little movement (sleep, sedentary behaviour) to movement of greater intensities (light and moderate-to-vigorous physical activity), which are important modifiable risk factors for CRD morbidity and mortality. Physical activity (PA) is defined as any bodily movement produced by skeletal muscles that results in energy expenditure. Optimising the 24-hour behavioural profile of people remains a global challenge.4

Physical inactivity is as a key risk factor for non-communicable disease, has been estimated to cause 9% of premature deaths worldwide5 and global trends are not on track to meet the global physical activity target, which is 10% relative reduction in the prevalence of insufficient physical activity by 2025.6,7 People living with CRD are not only less physically active than healthy adults,8 but also those living with other chronic diseases.9 Symptoms of CRD, including breathlessness, deconditioning and declining lung function, contribute to a physically inactive lifestyle; linked to an increased risk of hospitalisation10 and premature death.11 Sedentary behaviour (SB) (any waking behaviour characterized by an energy expenditure ≤1.5 metabolic equivalents, while in a sitting, reclining or lying posture)12 comprises the majority of people’s waking day. People living with CRD spend more time sedentary13 which has been associated with premature mortality.14 Poor sleep quality,15 sleep dissatisfaction,16 and inadequate sleep17 contributes significantly to societies’ health burden.18 Sleep disturbances are a common feature of CRD, leading to more severe respiratory symptoms and putting people at a greater risk of an acute exacerbation.19

Whilst distinct behaviours, PA, SB and sleep are intrinsically interrelated components of the 24-hour day, demonstrating the need to examine them in the context of each other rather than in isolation. Recent WHO guidelines for PA and SB20 and Canadian 24-hour movement guidelines21 have started to reflect this notion, but research is almost exclusively from high-income countries (HIC).22 Not least, PA in LMIC is mainly accumulated through transportation and occupation-related activities, contrasting HIC where most PA is undertaken recreationally.23 Economical, societal and cultural diversities in LMIC must be considered in the evaluation and monitoring of physical behaviours.24 A range of measurement options are available to quantify free-living PA, SB and sleep.

By compiling the current evidence on free-living PA, SB and sleep of adults living with CRD in LMIC, this review seeks to highlight research priorities, enabling researchers and clinicians to address important lifestyle health challenges in a more time- and cost-effective manner. Our scoping exercise identified no similar published reviews in LMIC. Similar reviews have been conducted in HIC25,26 which have aided comparisons between our findings from LMIC with findings from HIC. This systematic review addressed the following questions: Where have free-living PA, SB and sleep data been collected for adults living with CRD in LMIC? How have the data been collected? What are the free-living PA, SB and sleep levels of adults living with CRD?

Materials and Methods

This systematic review is reported in accordance with the Preferred Reporting Items for Systematic Review and Meta‐Analysis (PRISMA) statement.27 The protocol was prospectively registered on the International Prospective Register of Systematic Reviews (PROSPERO; CRD42020176196).

The search strategy was developed by an experienced clinical librarian [PD] using appropriate subject headings for each database. Search terms included chronic respiratory disease, “physical* activ*”, “sedent*” and sleep*alongside filters on LMIC (a full search strategy is provided in Appendix A). The strategy was adapted for each database.

Relevant articles were identified through systematic searches in five databases (Ovid-MEDLINE, Cochrane library, Ovid-EMBASE, Ovid-EMCARE and CINAHL). Searches for articles published in peer-reviewed journals were from the inception of each database until 30th January 2020. Global Index Medicus was searched, which included African Index Medicus, Index Medicus for Eastern Mediterranean Region, Index Medicus for South-East Asia Region, Latin Am. and Carib. Center on Health Sci Info, and Western Pacific Region Index Medicus. Reference lists of included articles were searched by hand for additional eligible articles. Studies on adults living with CRD in LMIC (population) which related to PA, SB and sleep (intervention), irrespective of the presence or absence of comparator (control group) which report what, where and how PA, SB and sleep data have been collected (outcome) were included in the review. Eligible studies included cross-sectional, longitudinal and case-control studies. Intervention studies were included if baseline data were presented. No restrictions were placed on sample size or language. The full text of the conference abstracts were requested from the corresponding author and if the authors did not respond, conference abstracts were also considered due to the paucity of the empirical evidence in LMICs.

Search results were screened using Rayyan software.28 Two reviewers independently screened for eligibility by title and abstract [MO, AVJ]. Interrater agreement was 99.6%. Discrepancies between the two reviewers were resolved through discussion and with final decision provided by ZY. The full texts of these potentially eligible articles were then retrieved and independently assessed by two reviewers [MO, ZY]. Discrepancies between the two reviewers were resolved through discussion and final decision provided by SS in the event of no consensus. Interrater agreement was 88.1%. Study authors were asked for full text copies if these could not be retrieved.

Data from included studies were extracted using a bespoke Microsoft Excel spreadsheet which included details on study location,29 study design, participant characteristics, free-living 24-hour physical behaviour measurements and methods, income classification,30 climate classification,31 air quality32 (see Appendix B for full data extraction form/table). Authors of included studies were not contacted in the event of missing data. Data extraction was performed by five authors [AA, ARJ, BG, MO, WK], with double-data extraction for each study. Any conflicts in data extraction were resolved through discussion with final decision provided by MO/SS.

The main summary measures were objectively measured overall free-living PA, SB and sleep. Given the nature of the review, no formal risk of bias assessment was conducted. Instead, indicators of quality of reporting and questionnaire/device deployment were examined. Reporting quality of the included studies was conducted by considering whether key information was provided. The presence or absence of information relating to each of the above items was used to describe the quality of reporting. Reporting quality of the included studies was presented through narrative and table summaries. In accordance with our PROSPERO registration, a meta-analysis was not planned due to anticipated heterogeneity in measurement approaches (Table 1).

Table 1 Summary of the Variables Used to Describe Physical Activity, Sedentary Behaviour and Sleep

Extracted data and summary measures were stratified into subgroups: Region29 (African, Region of the Americas, South-East Asia, European, Eastern Mediterranean, Western Pacific); income classification (Countries with low-income economies (Gross national income per capita $1045 or less in 2020); lower middle-income economies (GNI per capita between $1046 and $4095) and upper middle-income economies (GNI per capita between $4096 and $12,695) constitute low-and middle-income countries (LMIC)),33 Köppen-Geiger climate classification (Equatorial, Arid, Warm temperature, Snow, Polar),31 air quality levels (PM10: above or below 20µg/m3 annual mean and PM2.5: above or below 10µg/m3 annual mean),34 and measurement approach.

Results

Study Selection and Characteristics

Figure 1 shows the selection process according to the PRISMA statement.27 The initial search retrieved 18,121 records, 153 full texts were screened for eligibility and 89 articles (0.6%) met the criteria for inclusion (Appendix C). Of the 89 articles identified, 78 were articles and 11 were conference abstracts. The full text of the selected conference abstracts were requested from the corresponding authors but none responded. Most studies recruited exclusively COPD (65 studies [73%]) and asthma patients (13 studies [15%]). Four studies (4%) recruited a combined sample of COPD and asthma and two studies (2%) recruited bronchiectasis patients. Excluding studies covering all regions, sample sizes totalled 8986 participants. Most studies were cross-sectional in design (65 studies [73%]), comprised male majority samples (55 studies [62%]) and included data collected in warm temperature climates (45 studies [51%]) (Table 2). Fifteen studies reported having a control group and eight studies compare the patients with healthy control (Appendix D).


Figure 1 PRISMA flow diagram of study selection.

Income Classification

Of the 89 articles identified, none were conducted in low-income countries, 12 (13%) were conducted in lower-middle-income countries, 71 (80%) were conducted in upper-middle-income countries (UMIC) and six (7%) spanned income classifications.

WHO Regions

The most prevalent region for included studies was the Region of the Americas (45 studies [51%]; 43 in Brazil) followed by European (11 studies [12%], eight in Turkey) and South-East Asian (10 studies [11%], six in India). Three studies were conducted solely in Africa (all in Nigeria).

The Methods of Physical Behaviour Data Collection

PA (66 studies) was the most commonly measured behaviour; measured solely in 47 studies, in combination with SB in 16 studies, with sleep in one study and with SB and sleep in two studies. Sleep (24 studies) was measured exclusively in 21 studies and SB (20 studies) was measured solely in two studies.

Majority of studies assessed physical behaviours using questionnaires only (52 studies [58%]). Fourteen different questionnaires were used across included studies, with an additional 12 studies reporting specific questions. For sleep, the Pittsburgh Sleep Quality Index (PSQI) was the most common questionnaire (18 studies). For PA/SB, the International Physical Activity Questionnaire (IPAQ) was most common (11 studies). Questions or questionnaires were not provided in four studies. Most studies (58%) did not report the recall period of the questionnaires used and only one study (2%) reported how missing data were handled (Appendix E).

Of the 36 studies using device-based measurement of physical behaviours, twenty-six studies (72%) used accelerometers and 10 studies (28%) used pedometers. Dynaport activity monitors were used in 15 studies (58% of accelerometer studies) and the SenseWear Armband in seven studies. Five studies (19%) deployed a Dynaport and SenseWear simultaneously. Four studies (15%) did not provide the model of activity monitor used. Most studies (32, 89%) did not report whether participants had access to feedback on their behaviour during the measurement period. Four studies (11%) did not report the number of days participants were asked to wear the device. Most studies (18, 50%) monitored behaviours for two days and six studies (17%) had a seven-day wear protocol. Fifteen studies (42%) did not report how long each day participants were asked to wear a device. Of the 21 studies reporting this information, the majority (18 studies, 86%) asked for 12 hours of wear and during a fixed time period (19 studies, 90%) to be included in analysis, 14 studies (39%) did not report the minimum number of hours each day and minimum number of days the device needed to be worn. Most studies (35, 97%) did not report average wear-time and 34 studies (94%) did not report if or how periods of non-wear were calculated (Appendix F).

Variables to Assess 24-Hour Physical Behaviours

A summary of the variables used to describe physical behaviours is given in Table 1 (full extraction of the variables in Appendix B). The most common PA categories of variables used were steps per day (n=21), energy expenditure (n=21), binary classification of active/inactive (n=19), time spent walking (n=16) and classification of active/inactive using at least three groups (n=16). For SB, the common categories of variables used were sedentary time (n=16), standing time (n=13), sitting time (n=11) and lying time (n=10). Overall sleep quality (n=32), sleep duration (n=14), use of sleep medication (n=11) and sleep disturbances (n=11) were the most commonly used categories of sleep variables.

Levels of Free-Living PA, SB and Sleep

Time spent walking (12 studies; range 36–91min/day) and movement intensity (eight studies; range 0.18–1.9m/s2) measured by Dynaport activity monitor were most common, followed by the number of steps per day measured by Dynaport (six studies; range 2669–6557steps/day) and Yamax (five studies; range 4227–7490 steps/day) and MVPA measured by SenseWear Armband (five studies; range 29–89min/day). Three SB variables, all measured using Dynaport, were directly comparable between studies; comprising sitting time (12 studies; range 283–418min/day); standing time (12 studies; range 139–270min/day); lying time (11 studies; range 76–119min/day). For sleep, PSQI score was provided in 12 studies (range 4–11) and PSQI classification of poor sleep quality in 11 studies (range 33–100%) (Table 3).

Sub-Group Analysis of 24-Hour Physical Behaviours

Four studies measuring PA and two studies measuring SB spanned all regions. The Region of the Americas accounted for the majority of studies measuring PA (45 studies [68%]) (Figure 2A) and SB (19 studies [95%]) (Figure 2B). Studies measuring sleep were distributed across regions in small numbers with South-East Asia as the most common (six studies [25%]) (Figure 2C). One study measuring PA included data from all low- and middle- income classifications. No studies solely took place in low-income countries. Most took place in upper-middle income countries (60 [91%] measured PA, 19 [95%] measured SB and 16 [67%] measured sleep). We were unable to classify climate for seven studies measuring PA, two studies measuring SB and one study measuring sleep. Most studies were conducted in warm temperature climates (41 studies [69%] of PA, 17 studies [94%] of SB and six studies of sleep [26%]). There were 52 studies where we were unable to classify air quality (61% measuring PA, 85% measuring SB and 46% measuring sleep). All other studies exceeded the annual 10µg/m3 PM2.5 threshold. Using the annual 20µg/m3 PM10 threshold, 15 studies (58%) measured PA, one study (33%) measured SB and ten studies (77%) measured sleep in areas exceeding the threshold (Table 4).

Table 4 Summary of the Number of Studies Measuring Each Physical Behaviour, Stratified by Region, Income Classification, Measurement Approach, Climate Classification and Air Quality


Figure 2 (A) Location of studies measuring physical activity. (B) Location of studies measuring sedentary behaviour. (C) Locations of studies measuring sleep.

Figure 2 Continued.

Figure 2 Continued

Discussion

This systematic review provided a comprehensive assessment of the prevalence, levels and measurement approaches of studies assessing 24-hour physical behaviours in people living with CRD in LMIC. The majority of studies examining PA and SB in CRD in LMIC are limited to Brazil (UMIC), whilst studies measuring sleep were more evenly distributed across regions. Questionnaires were more commonly used to measure physical behaviours than device-based methods, with data supporting low physical activity levels, sedentary lifestyles and poor sleep quality for people in LMIC living with CRDs.

Most studies were conducted in upper-middle-income countries with Brazil accounting for almost half. In other regions, Turkey (European), India (South-East Asian) and Nigeria (Africa) had the monopoly of identified articles within their respective regions. No studies were conducted exclusively in low-income countries. Therefore, more studies are needed to compare physical behaviours within and between regions and income classifications. For large cohort studies of general adult populations, such as those included in The Lancet physical inactivity series,5 data were from predominantly HIC in Europe and North America; with data from Africa and Central Asia particularly scarce.35 Efforts to increase the available data on PA, SB and sleep in other LMIC are needed before any reliable prevalence estimates and worthwhile comparisons can be made.

PA was most commonly investigated. There was a relatively equal split of device- and questionnaire-based assessment of PA, with almost exclusively device-based assessment of SB and questionnaire-based assessment of sleep. Compared with a previous systematic review of 76 studies measuring free-living PA in COPD (almost exclusively studies in HIC), the Dynaport activity monitor was more commonly used (42% versus 14%). The disproportionate use of the Dynaport in the included studies from Brazil explains this disparity, whereas the previous review contained a large proportion of studies from HIC. For questionnaire-based PA and SB, we found the IPAQ to be most commonly used; consistent with the evidence base for large epidemiological studies of healthy adult populations.5 IPAQ is commonly used in LMIC for population surveillance, with more than 50 counties adopting it as of 2016,7 but it was developed for individuals aged 15–69 years of age which may not be appropriate for many CRD populations.36 The PSQI was the most common questionnaire in our review with relatively consistent use of the overall PSQI score and classification of poor sleep quality. The PSQI was also more consistently used across regions and currently offers the greatest opportunity for pooled analysis and standardisation. The popularity of the PSQI is supported by a previous review of sleep disorders in COPD.26 Although it is also possible to assess free-living sleep in CRD populations using devices,37,38 the PSQI offers a simple tool to examine sleep quality and duration in LMIC.

Articles included in our review relied predominantly on summary variables of physical behaviours, such as steps per day, sedentary time and overall sleep quality. The high prevalence of summary variables supports previous work exploring device-based PA, which found 70% of studies used a marker for total activity, with half using step count. Postural assessments such as sitting, standing or lying down were more commonly reported in the included studies using more device-based assessments than previously found (44% versus 20%); due to the exclusive measurement of postures in Brazilian studies.

The commonly reported variables of PA were mostly related to walking (time spent walking and step count) which is easily interpretable and at the heart of clinical practice guidelines for COPD.39 It is estimated that 7000–10,000 steps per day is approximately equivalent to taking part in 30 minutes of daily MVPA9 and data from the included articles support people living with CRD in LMIC to be physically inactive. Indicating a global nature of the problem, prevalence of physical inactivity was found more than twice as high in high-income countries compared to low-income countries.6 However, the considerable variation between studies and locations, such as their measurement and data processing protocols, make it difficult to generalise.

Only the range of PA, SB and sleep levels were presented in the study. The reported study variables were not consistent across the included articles which caused considerable heterogeneity, and it may lead to false conclusions. Pooling the data on various study designs increase the methodological heterogeneity. Studies included in our review suggest people living with CRD in LMIC have worse sleep quality than healthy counterparts across income classifications. We found PSQI classification of poor sleep quality ranged 33–100%; higher than the estimated prevalence of 32.8% (95% confidence interval: 25.9–39.7%) based on a meta-analysis of 19 studies of working-age adults in LMIC40 and from adults residing in high-income European countries.41

The strengths of our systematic review include a pre-defined rigorous search methodology, registered on PROSPERO before study screening, bias assessment, including the use of validated search terms, the comprehensive array of databases searched, the absence of language restrictions and screening included reference lists to reduce selection bias. The data extraction phase was also duplicated to minimise assessor bias and error. However, there may be databases unknown to the authors where additional studies may be published in this area. We were unable to translate two non-English publications (Persian). The climate and air quality classifications were not always possible to identify from information reported in the included studies, limiting subgroup analyses.

Conclusion

Based on 89 articles included in our systematic review of the PA, SB and sleep of people living with CRD in LMIC, we showed considerable inequity between behaviours, geographical locations and income classifications. The majority of studies examining PA and SB in CRD in LMIC were limited to Brazil, whilst studies measuring sleep were more limited and spread across regions. Questionnaires were more commonly used to measure physical behaviours than device-based methods, with summary data supporting low physical activity levels, sedentary lifestyles and poor sleep quality for people in LMIC living with CRDs. There was large variation in measurement approaches and poor quality methodological reporting for all physical behaviours which limited direct comparisons. The lack of standardisation and poor reporting reduces the strength of evidence needed to strengthen the scientific integrity of data and resulting clinical and policy implications to support patients to lead healthier lifestyles. More studies of PA, SB and sleep are needed in a broader range of LMICs. Future work would benefit from a harmonised approach to data collection to ensure international comparisons.

Abbreviations

CRD, Chronic respiratory disease; HIC, High-income countries; LMIC, Low- and middle-income; countries; PA, Physical activity; PRISMA, Preferred reporting items for systematic review and meta‐analysis; PROSPERO, International prospective register of systematic reviews; SB, Sedentary behaviour; UMIC, Upper-middle-income countries; WHO, World health organisation.

Acknowledgments

The authors thank the University Hospitals of Leicester NHS Trust Libraries, British Library and authors of included studies for providing full-text articles. The authors send a special thank you to Dr Xiaorong Ding (Institute of Biomedical Engineering, University of Oxford, UK), Carlos Morgado Areia (Nuffield Department of Clinical Neurosciences, University of Oxford, UK), Daniel Filipe Cunha Taveira (NIHR Leicester Biomedical Research Centre-Respiratory, University of Leicester, UK) and Joao Sousa (NIHR Leicester Biomedical Research Centre-Respiratory, University of Leicester, UK) for extracting data from non-English full-text publications.

Funding

This research was funded by the National Institute for Health Research (NIHR) (17/63/20) using UK aid from the UK Government to support global health research. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK Department of Health and Social Care.

Disclosure

The authors report no conflicts of interest in this work.

References

1. Hu G, Zhou Y, Tian J, et al. Risk of COPD from exposure to biomass smoke: a metaanalysis. Chest. 2010;138(1):20–31. doi:10.1378/chest.08-2114

2. Amaral AFS, Coton S, Kato B, et al. Tuberculosis associates with both airflow obstruction and low lung function: BOLD results. Eur Respir J. 2015;46(4):1104–1112. doi:10.1183/13993003.02325-2014

3. Salvi SS, Manap R, Beasley R. Understanding the true burden of COPD: the epidemiological challenges. Prim Care Respir J. 2012;21(3):249–251. doi:10.4104/pcrj.2012.00082

4. Rollo S, Antsygina O, Tremblay MS. The whole day matters: understanding 24-hour movement guideline adherence and relationships with health indicators across the lifespan. J Sport Health Sci. 2020;9:493–510. doi:10.1016/j.jshs.2020.07.004

5. Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT. Impact of physical inactivity on the world’s major non- communicable diseases. Lancet. 2012;380(9838):219–229. doi:10.1016/S0140-6736(12)61031-9

6. World Health Organization. Global action plan for the prevention and control of noncommunicable diseases 2013–2020. World Health Organization; 2013. Available from: apps.who.int/iris/bitstream/handle/10665/94384/?sequence=1. Accessed March 7, 2022.

7. Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants. Lancet Glob Health. 2018;6(10):e1077–e1086. doi:10.1016/S2214-109X(18)30357-7

8. Vorrink SNW, Kort HSM, Troosters T, Lammers J-WJ. Level of daily physical activity in individuals with COPD compared with healthy controls. Respir Res. 2011;12(1):1–8. doi:10.1186/1465-9921-12-33

9. Tudor-Locke C, Craig CL, Aoyagi Y, et al. How many steps/day are enough? For older adults and special populations. Int J Behav Nutr Phys Act. 2011;8(1):1–19. doi:10.1186/1479-5868-8-1

10. Pitta F, Troosters T, Probst VS, Spruit MA, Decramer M, Gosselink R. Physical activity and hospitalization for exacerbation of COPD. Chest. 2006;129(3):536–544. doi:10.1378/chest.129.3.536

11. Waschki B, Kirsten A, Holz O, et al. Physical activity is the strongest predictor of all-cause mortality in patients with COPD: a prospective cohort study. Chest. 2011;140(2):331–342. doi:10.1378/chest.10-2521

12. Tremblay MS, Aubert S, Barnes JD, et al. Sedentary Behavior Research Network (SBRN) - Terminology Consensus Project process and outcome. Int J Behav Nutr Phys Act. 2017;14(1):1–17. doi:10.1186/s12966-017-0525-8

13. Pitta F, Troosters T, Spruit MA, Probst VS, Decramer M, Gosselink R. Characteristics of physical activities in daily life in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2005;171(9):972–977. doi:10.1164/rccm.200407-855OC

14. Furlanetto KC, Donária L, Schneider LP, et al. Sedentary behavior is an independent predictor of mortality in subjects with COPD. Respir Care. 2017;62(5):579–587. doi:10.4187/respcare.05306

15. Ohayon M, Wickwire EM, Hirshkowitz M, et al. National Sleep Foundation’s sleep quality recommendations: first report. Sleep Health. 2017;3(1):6–19. doi:10.1016/j.sleh.2016.11.006

16. Ohayon MM, Paskow M, Roach A, et al. The National Sleep Foundation’s Sleep Satisfaction Tool. Sleep Health. 2019;5(1):5–11. doi:10.1016/j.sleh.2018.10.003

17. Hirshkowitz M, Whiton K, Albert SM, et al. National Sleep Foundation’s updated sleep duration recommendations: final report. Sleep Health. 2015;1(4):233–243. doi:10.1016/j.sleh.2015.10.004

18. Chattu V, Manzar MD, Kumary S, Burman D, Spence DW, Pandi-Perumal SR. The global problem of insufficient sleep and its serious public health implications. Healthcare. 2019;7(1):1–16. doi:10.3390/healthcare7010001

19. Shorofsky M, Bourbeau J, Kimoff J, et al. Impaired sleep quality in COPD is associated with exacerbations: the CanCOLD cohort study. Chest. 2019;156(5):852–863. doi:10.1016/j.chest.2019.04.132

20. Bull FC, Al-Ansari SS, Biddle S, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54(24):1451–1462. doi:10.1136/bjsports-2020-102955

21. Ross R, Chaput JP, Giangregorio LM, et al. Canadian 24-Hour Movement Guidelines for Adults aged 18–64 years and Adults aged 65 years or older: an integration of physical activity, sedentary behaviour, and sleep. Appl Physiol Nutr Metab. 2020;45(10):S57–S102. doi:10.1139/apnm-2020-0467

22. Sallis JF, Bull F, Guthold R, et al. Progress in physical activity over the Olympic quadrennium. Lancet. 2016;388(10051):1325–1336. doi:10.1016/S0140-6736(16)30581-5

23. Lambert EV, Kolbe-Alexander T, Adlakha D, et al. Making the case for ‘physical activity security’: the 2020 WHO guidelines on physical activity and sedentary behaviour from a Global South perspective. Br J Sports Med. 2020;54(24):1447–1448. doi:10.1136/bjsports-2020-103524

24. Strain T, Wijndaele K, Garcia L, et al. Levels of domain-specific physical activity at work, in the household, for travel and for leisure among 327 789 adults from 104 countries. Br J Sports Med. 2020;54(24):1488–1497. doi:10.1136/bjsports-2020-102601

25. Byrom B, Rowe DA. Measuring free-living physical activity in COPD patients: deriving methodology standards for clinical trials through a review of research studies. Contemp Clin Trials. 2016;47:172–184. doi:10.1016/j.cct.2016.01.006

26. Garrow AP, Yorke J, Khan N, Vestbo J, Singh D, Tyson S. Systematic literature review of patient-reported outcome measures used in assessment and measurement of sleep disorders in chronic obstructive pulmonary disease. Int J COPD. 2015;10:293–307. doi:10.2147/COPD.S68093

27. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;88:105906. doi:10.1136/bmj.n71

28. Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev. 2016;5(1):1–10. doi:10.1186/s13643-016-0384-4

29. World Health Organisation. Definition of regional groupings. Available from: www.who.int/healthinfo/global_burden_disease/definition_regions/en/. Accessed March 7, 2022.

30. The World Bank. World Bank Country and Lending Groups. Country Classification. Available from: datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups. Accessed March 7, 2022.

31. Kottek M, Grieser J, Beck C, Rudolf B, Rubel F. World map of the Köppen-Geiger climate classification updated. Meteorol Zeitschrift. 2006;15(3):259–263. doi:10.1127/0941-2948/2006/0130

32. Peel MC, Finlayson BL, McMahon TA. Updated world map of the Koppen-Geiger climate classification. Hydrol Earth Syst Sci. 2007;11(5):1633–1644. doi:10.1002/ppp.421

33. World Bank. New World Bank Country Classifications by Income Level: 2020–2021; Washington, DC, USA: World Bank; 2020. Available from: blogs.worldbank.org/opendata/new-world-bank-country-classifications-income-level-2020-2021. Accessed March 7, 2022.

34. World Health Organisation. WHO Air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide. Global update 2005. Summary of risk assessment; 2005. Available from: apps.who.int/iris/handle/10665/69477. Accessed March 7, 2022.

35. Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U. Physical activity levels of the world’s population Surveillance progress, gaps and prospects. Lancet. 2012;380:247–257. doi:10.1016/S0140-6736(12)60646-1

36. Ainsworth BE, Pratt M, Ekelund U, Yngve A, Sallis JF, Oja P. International physical activity questionnaire: 12-Country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–1395. doi:10.1249/01.MSS.0000078924.61453.FB

37. Orme MW, Steiner MC, Morgan MD, et al. 24-hour accelerometry in COPD: exploring physical activity, sedentary behavior, sleep and clinical characteristics. Int J COPD. 2019;14:419–430. doi:10.2147/COPD.S183029

38. Spina G, Spruit MA, Alison J, et al. Analysis of nocturnal actigraphic sleep measures in patients with COPD and their association with daytime physical activity. Thorax. 2017;72(8):694–701. doi:10.1136/thoraxjnl-2016-208900

39. Lewthwaite H, Effing TW, Olds T, Williams MT. Physical activity, sedentary behaviour and sleep in COPD guidelines: a systematic review. Chron Respir Dis. 2017;14(3):231–244. doi:10.1177/1479972316687224

40. Simonelli G, Marshall NS, Grillakis A, Miller CB, Hoyos CM, Glozier N. Sleep health epidemiology in low and middle-income countries: a systematic review and meta-analysis of the prevalence of poor sleep quality and sleep duration. Sleep Health. 2018;4(3):239–250. doi:10.1016/j.sleh.2018.03.001

41. Ohayon MM, Reynolds III CF. Epidemiological and clinical relevance of insomnia diagnosis algorithms according to the DSM-IV and the International Classification of Sleep Disorders (ICSD). Sleep Med. 2009;10(9):952–960. doi:10.1016/j.sleep.2009.07.008

42. Adetiloye AO, Erhabor GE, Adewole OO, Awopeju O. The determinants of sleep quality and its impact on exercise capacity of patients with COPD. Afr J Respir Med. 2018;13(2):10–14.

43. Adewole O, Erhabor G, Oguntola O. Sleep pattern, quality and sleepiness among patients with chronic pulmonary parenchyma disease in Nigeria. Sleep Med. 2011;12:S47. doi:10.1016/S1389-9457(11)70173-3

44. Desalu OO, Onyedum CC, Makusidi MA, et al. Physical and socioeconomic impact of asthma in Nigeria: experience of patients attending three tertiary hospitals. Niger J Clin Pract. 2019;22(6):855–861. doi:10.4103/njcp.njcp_294_18

45. Ali Zohal M, Yazdi Z, Kazemifar AM. Daytime sleepiness and quality of sleep in patients with COPD compared to control group. Glob J Health Sci. 2013;5(3):150–155. doi:10.5539/gjhs.v5n3p150

46. Borji M, Salimi E. Comparing the effect of home care nursing and health promotion model on quality of life, sleep quality and life style of patients with asthma: a randomized clinical trial. Iran J Allergy Asthma Immunol. 2018;17(1):150.

47. Chegeni SP, Gholami M, Azargoon A, Hossein Pour AH, Birjandi M, Norollahi H. The effect of progressive muscle relaxation on the management of fatigue and quality of sleep in patients with chronic obstructive pulmonary disease: a randomized controlled clinical trial. Complement Ther Clin Pract. 2018;31:64–70. doi:10.1016/j.ctcp.2018.01.010

48. Eslaminejad A, Safa M, Ghassem BF, Hajizadeh F, Pashm FM. Relationship between sleep quality and mental health according to demographics of 850 patients with chronic obstructive pulmonary disease. J Health Psychol. 2017;22(12):1603–1613. doi:10.1177/1359105316684937

49. Mahmood ZA, Malghooth ZT. Relationship of hips and knees osteoarthritis with bronchial asthma. Res J Pharm Biol Chem Sci. 2019;10(2):64–70.

50. Moussa SB, Sfaxi I, Tabka Z, Saad HB, Rouatbi S. Oxidative stress and lung function profiles of male smokers free from COPD compared to those with COPD: a case-control study. Libyan J Med. 2014;9(1):23873. doi:10.3402/ljm.v9.23873

51. Moussa SB, Rouatbi S, Saad HB. Incapacity, handicap, and oxidative stress markers of male smokers with and without COPD. Respir Care. 2016;61(5):668–679. doi:10.4187/respcare.04420

52. Mihaltan F, Adir Y, Antczak A, et al. Importance of the relationship between symptoms and self-reported physical activity level in stable COPD based on the results from the SPACE study. Respir Res. 2019;20(1). doi:10.1186/s12931-019-1053-7

53. Mihaltan F, Antczak A, Radulovic V, Chen Y, Alecu S. Physical activity level in stable copd patients from central eastern europe: results from a large observational study. Chest. 2019;156(4):A903. doi:10.1016/j.chest.2019.08.848

54. Vukoja M, Kopitovic I, Milicic D, Maksimovic O, Pavlovic-Popovic Z, Ilic M. Sleep quality and daytime sleepiness in patients with COPD and asthma. Clin Respir J. 2018;12(2):398–403. doi:10.1111/crj.12528

55. Akinci AC, Yildirim E. Factors affecting health status in patients with chronic obstructive pulmonary disease. Int J Nurs Pract. 2013;19(1):31–38. doi:10.1111/ijn.12034

56. Genc A, Ucok K, Gunay E, et al. Effects of long-acting beta-2 agonist treatment on daily energy balance and body composition in patients with chronic obstructive pulmonary disease. Turk J Med Sci. 2012;42:1414–1422.

57. Genc A, Ucok K, Sener U, et al. Association analyses of oxidative stress, aerobic capacity, daily physical activity, and body composition parameters in patients with mild to moderate COPD. Turk J Med Sci. 2014;44(6):972–979. doi:10.3906/sag-1308-65

58. Pehlivan E, Niksarlioglu EY, Balci A, Kilic L. The effect of pulmonary rehabilitation on the physical activity level and general clinical status of patients with bronchiectasis. Turk Thorac J. 2019;20(1):30–35. doi:10.5152/TurkThoracJ.2018.18093

59. Şahin AZ, Dayapoğlu N. Effect of progressive relaxation exercises on fatigue and sleep quality in patients with chronic obstructive lung disease (COPD). Complement Ther Clin Pract. 2015;21(4):277–281. doi:10.1016/j.ctcp.2015.10.002

60. Saglam M, Savci S, Vardar Yagli N, et al. Relationship between obesity and respiratory muscle strength, functional capacity, and physical activity level in patients with chronic obstructive pulmonary disease. Fiz Rehabil. 2013;24(3):157–162.

61. Turan O, Ure I, Turan PA. Erectile dysfunction in COPD patients. Chronic Respir Dis. 2016;13(1):5–12. doi:10.1177/1479972315619382

62. Yilmaz FT, Aydin HT. The effect of a regular walking program on dyspnoea severity and quality of life in normal weight, overweight, and obese patients with chronic obstructive pulmonary disease. Int J Nurs Pract. 2018;24(3):1. doi:10.1111/ijn.12636

63. Lopez Jove OR, Galdames A, Barrionuevo V, et al. Depression in patients with chronic obstructive pulmonary disease (COPD): relationship to dyspnea degrees and impact on quality of life (QOL). Am J Respir Crit Care Med. 2013;187:A2505.

64. Molinas JA, Torrent C, Ardusso LR, Crisci CD, Barayazarra S, Crisci S. Relationship between body mass index and severity of bronchial asthma in adults. Arch Allergy Immunol Clin. 2007;38(1):19–28.

65. Amorim PB, Stelmach R, Carvalho CRF, Fernandes FLA, Carvalho-Pinto R, Cukier A. Barriers associated with reduced physical activity in COPD patients. J Bras Pneumol. 2014;40(5):504–512. doi:10.1590/S1806-37132014000500006

66. Athayde FTS, Vieira DSR, Britto RR, Parreira VF. Functional outcomes in patients with chronic obstructive pulmonary disease: a multivariate analysis. Rev Brasil Fisioter. 2014;18(1):63–71. doi:10.1590/S1413-35552012005000142

67. Borges RC, Carvalho CRF. Physical activity in daily life in Brazilian COPD patients during and after exacerbation. COPD. 2012;9(6):596–602. doi:10.3109/15412555.2012.705364

68. Campos FL, de Bruin PFC, Pinto TF, da Silva FGC, Pereira EDB, de Bruin VMS. Depressive symptoms, quality of sleep, and disease control in women with asthma. Sleep Breathing. 2017;21(2):361–367. doi:10.1007/s11325-016-1422-0

69. Cani KC, Matte DL, Silva IJCS, Gulart AA, Karloh M, Mayer AF. Impact of home oxygen therapy on the level of physical activities in daily life in subjects with COPD. Respir Care. 2019;64(11):1392–1400. doi:10.4187/respcare.06206

70. Cavalcante AM, De Bruin VMS, Pereira EDB, et al. Restless leg syndrome, fatigue and quality of sleep in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2012;185:A4533.

71. Cetlin AA, Gutierrez MR, Bettiol H, Barbieri MA, Vianna EO. Influence of asthma definition on the asthma-obesity relationship. BMC Public Health. 2012;12:844. doi:10.1186/1471-2458-12-844

72. Coelho CM, Campos LA, Pereira FO, et al. Objectively measured daily-life physical activity of moderate-to-severe Brazilian asthmatic women in comparison to healthy controls: a cross-sectional study. J Asthma. 2018;55(1):73–78. doi:10.1080/02770903.2017.1306547

73. Coelho CM, Reboredo MM, Valle FM, et al. Effects of an unsupervised pedometer-based physical activity program on daily steps of adults with moderate to severe asthma: a randomized controlled trial. J Sports Sci. 2018;36(10):1186–1193. doi:10.1080/02640414.2017.1364402

74. Da Costa CC, Bordin CD, Roberto CJP, Lermen C, Colombo C, Machado DSR. The repercussions of a pulmonary rehabilitation program on the level of physical activity in patients with chronic obstructive pulmonary disease. Rev Inspirar Movimento Saude. 2012;4(5):34–37.

75. Cukier A, Godoy I, Costa CHD, et al. Symptom variability over the course of the day in patients with stable COPD in Brazil: a real-world observational study. J Bras Pneumol. 2020;46(3):e20190223. doi:10.36416/1806-3756/e20190223

76. Felcar JM, Probst VS, De Carvalho DR, et al. Effects of exercise training in water and on land in patients with COPD: a randomised clinical trial. Physiotherapy. 2018;104(4):408–416. doi:10.1016/j.physio.2017.10.009

77. Filippin TR, Ariza D, Beloto A-CNDO. Correlation of the bod mortality predictor index in physically active and sedentary chronic obstructive pulmonary diseased patients. Rev Inspirar Movimento Saude. 2011;3(4):30–35.

78. Fonseca FR, Karloh M, De araujo CLP, Dos santos K, Mayer AF. Nutritional status and its relationship with different dimensions of functional status in patients with chronic obstructive pulmonary disease. Rev Nutr. 2016;29(5):635–644. doi:10.1590/1678-98652016000500002

79. Freitas PD, Silva AG, Ferreira PG, et al. Exercise improves physical activity and comorbidities in obese adults with asthma. Med Sci Sports Exerc. 2018;50(7):1367–1376. doi:10.1249/MSS.0000000000001574

80. Furlanetto KC, Pinto IFS, Sant’Anna T, Hernandes NA, Pitta F. Profile of patients with chronic obstructive pulmonary disease classified as physically active and inactive according to different thresholds of physical activity in daily life. Braz J Phys Ther. 2016;20(6):517–524. doi:10.1590/bjpt-rbf.2014.0185

81. Furlanetto KC, Demeyer H, Sant’anna T, et al. Physical activity of patients with COPD from regions with different climatic variations. COPD. 2017;14(3):276–283. doi:10.1080/15412555.2017.1303039

82. Gulart AA, Munari AB, Klein SR, Venancio RS, Alexandre HF, Mayer AF. The London Chest Activity of Daily Living scale cut-off point to discriminate functional status in patients with chronic obstructive pulmonary disease. Braz J Phys Ther. 2019;24(3):264–272. doi:10.1016/j.bjpt.2019.03.002

83. Hernandes NA, Teixeira DC, Probst VS, Brunetto AF, Ramos EMC, Pitta F. Profile of the level of physical activity in the daily lives of patients with COPD in Brazil. J Bras Pneumol. 2009;35(10):949–956. doi:10.1590/S1806-37132009001000002

84. Jardim JR, Piazza M, Carvalho AK, Ivanaga IT, Nascimento OA. Assessment of physical activity by pedometer in patients with chronic obstructive pulmonary disease (COPD) In an emerging country. Am J Respir Crit Care Med. 2011;183(1):A2034.

85. Karloh M, Araujo CLP, Gulart AA, Reis CM, Steidle LJM, Mayer AF. The Glittre-ADL test reflects functional performance measured by physical activities of daily living in patients with chronic obstructive pulmonary disease. Braz J Phys Ther. 2016;20(3):223–230. doi:10.1590/bjpt-rbf.2014.0155

86. Landal AC, Monteiro F, Hevely BC, Kanesawa LM, Hernandes N, Pitta F. Factors associated with improved body composition in individuals with COPD after physical training. Fisioter mov. 2014;27(4):633–641. doi:10.1590/0103-5150.027.004.AO15

87. Lanza FC, Castro RAS, de Camargo A, et al. COPD assessment test (CAT) is a valid and simple tool to measure the impact of Bronchiectasis on affected patients. COPD. 2018;15(5):512–519. doi:10.1080/15412555.2018.1540034

88. Lopes AC, Xavier RF, Pereira AC AC, et al. Identifying COPD patients at risk for worse symptoms, HRQoL, and self-efficacy: a cluster analysis. Chronic Illn. 2019;15(2):138–148. doi:10.1177/1742395317753883

89. Mantoani LC, Hernandes N, Guimarães M, Vitorasso RL, Probst VS, Pitta F. Does the BODE index reflect the level of physical activity in daily life in patients with COPD? Braz j Phys Ther (Impr). 2011;15(2):131–137. doi:10.1590/S1413-35552011000200008

90. Monteiro F, Camillo CA, Vitorasso R, et al. Obesity and physical activity in the daily life of patients with COPD. Lung. 2012;190(4):403–410. doi:10.1007/s00408-012-9381-0

91. Morita AA, Silva LKO, Bisca GW, et al. Heart rate recovery, physical activity level, and functional status in subjects with COPD. Respir Care. 2018;63(8):1002–1008. doi:10.4187/respcare.05918

92. Munari AB, Gulart AA, Dos Santos K, Venâncio RS, Karloh M, Mayer AF. Modified Medical Research Council Dyspnea Scale in GOLD classification better reflects physical activities of daily living. Respir Care. 2018;63(1):77–85. doi:10.4187/respcare.05636

93. Nobeschi L, Zangirolami-Raimundo J, Cordoni PK, et al. Evaluation of sleep quality and daytime somnolence in patients with chronic obstructive pulmonary disease in pulmonary rehabilitation. BMC Pulm Med. 2020;20(1):1–7. doi:10.1186/s12890-020-1046-9

94. Nunes DM, Mota RMS, Machado MO, Pereira EDB, de Bruin VMS, de Bruin PFC. Effect of melatonin administration on subjective sleep quality in chronic obstructive pulmonary disease. Braz J Med Biol Res. 2008;41(10):926–931. doi:10.1590/S0100-879X2008001000016

95. Nyssen SM, Santos J, Barusso MS, Delfino de OA Jr, Lorenzo VA, Jamami M. Levels of physical activity and predictors of mortality in COPD. J Bras Pneumol. 2013;39(6):659–666. doi:10.1590/S1806-37132013000600004

96. Pitta F, Breyer M, Hernandes NA, et al. Comparison of daily physical activity between COPD patients from Central Europe and South America. Respir Med. 2009;103(3):421–426. doi:10.1016/j.rmed.2008.09.019

97. Probst VS, Kovelis D, Hernandes N, Camillo CA, Cavalheri V, Pitta F. Effects of 2 exercise training programs on physical activity in daily life in patients with COPD. Respir Care. 2011;56(11):1799–1807. doi:10.4187/respcare.01110

98. Reboredo MDM, Lucinda LM, Yecker GD, et al. Barriers to physical activity in patients with chronic respiratory diseases. Am J Respir Crit Care Med. 2018;197:A2174.

99. Rocha FR, Brüggemann A, Karla V, et al. Diaphragmatic mobility: relationship with lung function, respiratory muscle strength, dyspnea, and physical activity in daily life in patients with COPD. J Bras Pneumol. 2017;43(1):32–37. doi:10.1590/s1806-37562016000000097

100. Rodrigues ESR, Moreira RDF, Rezende AAB, Costa LD. Sedentarism and smoking in patients with cardiovascular, respiratory and orthopedic diseases. Rev Enfermagem Ufpe. 2014;8(3):591–599.

101. Schneider PL, Couto FK, Aparecida HN, Pitta F. Does the wearing time of motion sensor interfere with the choice of physical activity in daily life outcomes of COPD patients? Fisioter Pesquisa. 2018;25(1):43–48. doi:10.1590/1809-2950/16768425012018

102. Silva DR, Coelho AC, Dumke A, et al. Osteoporosis prevalence and associated factors in patients with COPD: a cross-sectional study. Respir Care. 2011;56(7):961–968. doi:10.4187/respcare.01056

103. Simon KM, Hass AP, Zimmermman JL, Carpes MF. BODE mortality prognostic index and physical activity in chronic obstructive pulmonary patients. Rev bras med esporte. 2009;15(1):19–22. doi:10.1590/S1517-86922009000100004

104. Tavares MG, Nascimento ACSD, Ferraz MCCN, Medeiros RABD, Cabral PC, Burgos MGPDA. Overweight and obesity in patients with chronic obstructive pulmonary disease. Braspen J. 2017;32(1):58–62.

105. Vitorasso R, Camillo CA, Cavalheri V, et al. Is walking in daily life a moderate intensity activity in patients with chronic obstructive pulmonary disease? Eur J Phys Rehabil Med. 2012;48(4):587–592.

106. Xavier R, Lopes AC, Pereira ACAC, et al. Factors associated with daily life physical activity in Brazilian COPD patients. Eur Respir J. 2016;48:PA1889.

107. Yunus FM, Khan S, Mitra DK, Mistry SK, Afsana K, Rahman M. Relationship of sleep pattern and snoring with chronic disease: findings from a nationwide population-based survey. Sleep Health. 2018;4(1):40–48. doi:10.1016/j.sleh.2017.10.003

108. Bhuker M, Kapoor S. Environmental and occupational respiratory diseases-1043. Effects of socioeconomic status, life style patterns and demographic factors. World Allergy Organ J. 2013;6:P42. doi:10.1186/1939-4551-6-S1-P42

109. De S. Subjective assessment of quality of sleep in chronic obstructive pulmonary disease patient and its relationship with associated depression. Lung India. 2012;29(4):332–335. doi:10.4103/0970-2113.102808

110. Gupta R, Ulfberg J, Allen RP, Goel D. Comparison of subjective sleep quality of long-term residents at low and high altitudes: SARAHA study. J Clin Sleep Med. 2018;14(1):15–21. doi:10.5664/jcsm.6870

111. Panigrahi MK, Padhan M, Mohapatra PR. Relationship between sleep quality, asthma control and quality of life in adults with asthma. Am J Respir Crit Care Med. 2018;197:A1089. doi:10.1164/rccm.201708-1728OC

112. Poongadan MN, Gupta N, Kumar R. Lifestyle factors and asthma in India - a case-control study. Pneumonol Alergol Pol. 2016;84(2):104–108. doi:10.5603/PiAP.2016.0008

113. Sharma K, Jain A, Goyal V, et al. Depression and physical activity impairment in COPD subjects. J Clin Diagn Res. 2019;13(7):OC16–22.

114. Ulfathinah A, Rachmi SF, Indracahyani A. Characteristics affecting sleep quality of COPD patients. Enferm Clin. 2019;29:30–35. doi:10.1016/j.enfcli.2019.04.005

115. Widyastuti K, Makhabah DN, Rima A, Sutanto YS, Suradi S, Ambrosino N. Home based pulmonary rehabilitation with pedometers in Indonesian COPD patients. Eur Respir J. 2017;50:PA777.

116. Chanthitivech N, Sirichana W. Cardiopulmonary exercise testing parameters and level of daily physical activity correlation in stable COPD patients. Eur Respir J. 2017;50:PA2250.

117. Chunrong J, Rongchang C. Factors associated with impairment of quadriceps muscle function in Chinese patients with chronic obstructive pulmonary disease. PLoS One. 2014;9(2):e84167. doi:10.1371/journal.pone.0084167

118. Ding B, Small M, Bergstrom G, Holmgren U. A cross-sectional survey of night-time symptoms and impact of sleep disturbance on symptoms and health status in patients with COPD. Int J COPD. 2017;12:589–599. doi:10.2147/COPD.S122485

119. Tao Y, Wang L, Dong X, et al. Psychometric properties of the physical activity scale for the elderly in Chinese patients with COPD. Int J COPD. 2017;12:105–114. doi:10.2147/COPD.S120700

120. Wang L, Nygardh A, Zhao Y, Martensson J. Self-management among patients with chronic obstructive pulmonary disease in China and its association with sociodemographic and clinical variables. Appl Nurs Res. 2016;32:61–66. doi:10.1016/j.apnr.2016.05.001

121. Wang L, Tao Y, Dong X, et al. Demographic, health behavioral, and selfmanagement abilities associated with disease severity among patients with chronic obstructive pulmonary disease: an exploratory study. Int J Nurs Pract. 2017;23(1):e12509. doi:10.1111/ijn.12509

122. Wang S, Wu Y, Ungvari GS, et al. Sleep duration and its association with demographics, lifestyle factors, poor mental health and chronic diseases in older Chinese adults. Psychiatry Res. 2017;257:212–218. doi:10.1016/j.psychres.2017.07.036

123. Zheng L, Wang A. Application effect of rational emotional behavior therapy for home chronic obstructive pulmonary disease patients with depression. Chin Nurs J. 2019;33:1135–1140.

124. Vancampfort D, Koyanagi A, Ward PB, et al. Chronic physical conditions, multimorbidity and physical activity across 46 low and middle income countries. Int J Behav Nutr Phys Act. 2017;14(1):6. doi:10.1186/s12966-017-0463-5

125. Koyanagi A, Stubbs B, Smith L, Gardner B, Vancampfort D. Correlates of physical activity among community-dwelling adults aged 50 or over in six low- and middle-income countries. PLoS One. 2017;12(10):e0186992. doi:10.1371/journal.pone.0186992

126. Vancampfort D, Stubbs B, Koyanagi A. Physical chronic conditions, multimorbidity and sedentary behavior amongst middle-aged and older adults in six low- and middle-income countries. Int J Behav Nutr Phys Act. 2017;14(1):147. doi:10.1186/s12966-017-0602-z

127. Vancampfort D, Lara E, Stubbs B, Swinnen N, Probst M, Koyanagi A. Physical activity correlates in people with mild cognitive impairment: findings from six low- and middle-income countries. Public Health. 2018;156:15–25. doi:10.1016/j.puhe.2017.12.002

128. Vancampfort D, Stubbs B, Veronese N, Mugisha J, Swinnen N, Koyanagi A. Correlates of physical activity among depressed older people in six low-income and middle-income countries: a community-based cross-sectional study. Int J Geriatr Psychiatry. 2018;33(2):e314–22. doi:10.1002/gps.4796

129. Vancampfort D, Stubbs B, Mugisha J, Firth J, Schuch FB, Koyanagi A. Correlates of sedentary behavior in 2375 people with depression from 6 low- and middle-income countries. J Affect Disord. 2018;234:97–104. doi:10.1016/j.jad.2018.02.088

Source link

4169 1650022040.4169 1598955570

Spirometer Market size is estimated to reach $1.4 billion by 2027, growing at a CAGR of 3.4% during the forecast period 2022-2027. Spirometer is a device for determining the air capacity of the lungs. Spirometry is a typical, and comparatively effortless, test utilized to assess pulmonary function — to evaluate how well the lungs are performing. One of the most typical symptoms of cystic fibrosis (CF) is a downturn in lung health. Spirometry is frequently utilized to assist in diagnosing and supervising patients with CF and to discover issues like infections that can worsen the ailment. It is also frequently utilized in clinical trials of possible CF therapies to decide how efficient the treatment might be for a patient. A peak flow meter only assesses the rate at which air is compelled out of the lungs. This is termed the peak expiratory flow. Investigations have concluded that assessment of everyday home spirometry in patients with Idiopathic Pulmonary Fibrosis is greatly clinically informative and is possible to carry out for nearly all of the patients. The role of spirometry in the diagnosis of respiratory diseases is vital in the initial screening and evaluation of “severity and type of respiratory disease”.

The consistent innovations attributed to technological developments brought about by the comprehensive research and development of new and progressive diagnostics and monitoring technologies are set to drive the Spirometer Market. The expanding population of the elderly and the application of smartphone-based handheld wireless spirometer for home care of respiratory diseases are set to propel the growth of the Spirometer Market during the forecast period 2022-2027. This represents the Spirometer Industry Outlook

Spirometer Market Segment Analysis – By Type:

The Spirometer Market based on type can be further segmented into Hand-Held and Table-Top. The Table-Top Segment held the largest market share in 2021. This growth is owing to the surging technological progress in this segment. Spirometer is extensively applied to treat respiratory diseases. The soaring accuracy offered by desktop spirometers in comparison with other product types and the greater facilities available along with table top spirometers like in-built printers is further propelling the growth of the Table-Top segment.
Furthermore, the Hand-Held segment is estimated to grow with the fastest CAGR of 4.2% during the forecast period 2022-2027 owing to the increasing predominance of incessant respiratory diseases requiring proliferating application of compact and portable spirometers in households in conjunction with the increasing application of these devices by smokers and asthma patients and the consistent technological innovations happening in these devices.

Request for Sample Report @ www.industryarc.com/pdfdownload.php?id=16839

Report Price: $ 4500 (Single User License) 

Spirometer Market Segment Analysis – By Technology:

The Spirometer Market based on technology can be further segmented into Volume Measurement and Flow Measurement. The Volume Measurement Segment held the largest market share in 2021. This growth is owing to the beneficial feature of generating precise outcomes with effortless management of the device. Spirometer is applied for the diagnosis of respiratory diseases. The proliferating application of Volume Measurement for the treatment of respiratory diseases like asthma and cystic fibrosis is further propelling the growth of this segment.

Furthermore, the Flow Measurement segment is estimated to grow with the fastest CAGR of 4.9% during the forecast period 2022-2027 owing to the boost in the count of key players involved in launching progressive devices like wireless spirometers for treatment of respiratory diseases.

Spirometer Market Segment Analysis – By Geography:

The Spirometer Market based on geography can be further segmented into North America, Europe, Asia-Pacific, South America, and Rest of the World. North America (Spirometer Market) held the largest share with 38% of the overall market in 2021. The growth of this region is owing to the increasing predominance of respiratory diseases in the North American region. As per Centers For Disease Control And Prevention (CDC), the Weighted Number With Crrent Asthma as per Current Asthma Prevalence (2019) is 25,131,132. The existence of key players like 1.MGC Diagnostics Corporation in U.S. is further propelling the growth of the Spirometer Market in the North American region.

Furthermore, the Asia-Pacific region is estimated to be the region with the fastest CAGR rate over the forecast period 2022-2027. This growth is owing to factors like surging count of firms entering into the untapped economies in the Asia-Pacific region. The soaring investments of key players propelling the demand for progressive products and the heightening application of spirometers for diagnosis of respiratory diseases are further fuelling the progress of the Spirometer Market in the Asia-Pacific region.

Spirometer Market Drivers

Surging Applications Of Spirometer For Diagnosis Of Respiratory Diseases Are Projected To Drive The Growth Of Spirometer Market:

Spirometry is a convenient tool for diagnosing the cause of unknown respiratory symptoms and also supervising patients with recognized respiratory diseases. It stays the gold standard test for the diagnosis of obstructive airway diseases, inclusive of asthma and Chronic Obstructive Pulmonary Disease (COPD). As per World Health Organization (WHO), Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of demise globally, bringing about 3.23 million demises in 2019. Spirometry, assessment of the diffusion factor, bronchial incitement tests, and compelled oscillation methods have been utilized in different clinical uses in the diagnosis and supervising of respiratory diseases, like chronic obstructive pulmonary disease, interstitial lung diseases, and asthma. Certain other diseases where spirometry may assist in diagnosis as part of a panel of pulmonary function tests (PFTs) include emphysema (a kind of COPD), bronchiectasis (a kind of COPD), chronic bronchitis (a kind of COPD), pulmonary fibrosis inclusive of idiopathic pulmonary fibrosis) and cystic fibrosis. Spirometry testing serves a vital role in the diagnosis and handling of lung disease in the primary care setting. The surging applications of spirometer for diagnosis of respiratory diseases are therefore fuelling the growth of the Spirometer Market during the forecast period 2022-2027.

Novel Product Launches Involving Spirometer Are Expected To Boost Product Demand:

Spirometers are utilized in the diagnosis of respiratory diseases. In November 2021, Cipla Limited (referred to as Cipla, here afterwards) declared the introduction of Spirofy®, India’s earliest pneumotach based portable, wireless Spirometer, on World COPD Day. With this introduction, the firm determines to transform Obstructive Airway Disease (OAD) diagnosis, in line with its initiative to reinforce its position as the lung leader in India. This is a portion of the firm’s #LungAttack campaign that attempts to fuel awareness regarding COPD and inspire early diagnosis. Cipla’s Spirofy® is an outcome of 5 years of detailed in-house research by the Integrated Product Development (IPD) team, and it targets to revolutionize Obstructive Airway Disease (OAD) diagnosis in India. This progressive device guarantees soaring outcome precision and individual patient security utilizing Bacterial Viral Filters. Spirofy® is completely wireless with good battery support, making it appropriate for application in outdoor camps, remote areas with power deficiencies, or clearly offering adaptability to specialists and ease of application. The device produces reports in real-time, which can be printed utilizing a portable wireless thermal printer instantaneously, or a pdf version can be shared on the phone. Cipla will engage in the coaching of doctors in the clarification of spirometry outcomes. The novel product launches involving spirometer are therefore driving the growth of the Spirometer Market during the forecast period 2022-2027.

Inquiry Before Buying @ www.industryarc.com/reports/request-quote?id=16839

Spirometer Market Challenges

Lack of Skilled Workforce Is Hampering The Growth Of The Spirometer Market:

As per World Health Organization (WHO), some statistics pertaining to health workers are as follows:
“The density of nursing and midwifery personnel in Americas and European regions are the highest, over 82 and 77 per 10,000 population respectively. The density of medical doctors is highest in the European region around 43 per 10, 000 population. Conversely, the African region remains with the lowest density of medical doctors (around 2 per 10,000 population), and, nursing and midwifery personnel (around 10 per 10,000 population).”
From the above, there is a dearth of medical professionals in the developing regions of the world as observed in the case of Africa above.

In spite of the soaring pervasiveness of asthma and COPD worldwide, some determinants restrict the acceptance of spirometers. The lack of a skilled workforce for performing diagnostic tests restrains the growth of the market. Pulmonary function tests are a range of distinct breathing tests, which trained pulmonary function technologists can guide. However, there is a dearth of skilled healthcare experts in developing nations. The immigration of proficient healthcare workers to developed nations is the principal cause of this deficiency. The inclination to migrate to developed economies is chiefly owing to the pursuit of superior wages and working conditions. These issues are thus hampering the growth of the Spirometer Market.

Spirometer Industry Outlook:

Product launches, mergers and acquisitions, joint ventures, and geographical expansions are key strategies adopted by players in the Spirometer Market. Key companies of this market are:

  1. MGC Diagnostics Corporation
  2. NDD Medical Technologies
  3. nSpire Health Inc.
  4. Philips Healthcare
  5. Smiths Medical
  6. Schiller
  7. Vyaire Medical
  8. SDI Diagnostics
  9. Fukuda Sangyo
  10. Sibelmed

Acquisitions/Product Launches:

In November 2021, Royal Philips declared novel AI-enabled innovations in MR imaging introducing at the Radiological Society of North America (RSNA) annual meeting (November 23 – December 2, Chicago, USA). Philips’ novel MR portfolio of intelligent combined solutions is planned to speed up MR exams, streamline workflows, make perfect diagnostic quality, and assist in guaranteeing the effectiveness and sustainability of radiology operations. As MR imaging plays an ever-surging role in accuracy diagnosis, radiology departments encounter numerous challenges like workload demands owing to the accumulation of cases issuing from the COVID-19 pandemic, an increasing population of clinically challenging patients, and a global deficiency of skilled technicians.

In January 2021, Royal Philips declared its presence in the first-ever, all-digital CES 2021, running through Jan. 14. Since the introductory onset of COVID-19 a year ago, healthcare has accomplished an accelerated revolution and expedition of digital health technologies. At the time of CES 2021, Philips will accentuate a rare hybrid view of progressive technology solutions fast-tracking healthcare, moving external to the four walls of the hospital and into the home.

In September 2020, Royal Philips declared novel techniques to health and healthcare, and the switch to health-at-home, at its virtual consumer health press event. Against the backdrop of COVID-19, the requirement for consumers to remain as healthy as achievable is more appropriate than ever. At the contemporary virtual event, coinciding with IFA 2020, Philips declared extension of its consumer health portfolio with a series of innovations to assist people to keep up health-improving lifestyle practices, and digital opportunities that permit them to remain in touch with their healthcare professionals virtually now that physical doctors’ visits are restricted.

Direct Purchase @ www.industryarc.com/purchasereport.php?id=16839

Key Takeaways

Geographically, North America Spirometer Market accounted for the highest revenue share in 2021 and it is poised to dominate the market over the period 2022-2027 owing to the increasing predominance of Chronic Obstructive Pulmonary Disease (COPD), Asthma, and additional respiratory diseases in the North American region.

Spirometer Market growth is being driven by the rising pervasiveness of respiratory diseases like asthma and Chronic Obstructive Pulmonary Disease (COPD) across the world and the surging inclination towards home healthcare. However, the limited awareness of Chronic Obstructive Pulmonary Disease (COPD) amidst the common population resulting in the restricted application of spirometers for detection is one of the major factors hampering the growth of Spirometer Market.

Spirometer Market Detailed Analysis on the Strength, Weakness, and Opportunities of the prominent players operating in the market will be provided in the Spirometer Market report.

Related Reports-

A. Respiratory Devices Market

www.industryarc.com/Report/16227/respiratory-devices-market.html

B. Healthcare Equipment Market

www.industryarc.com/Report/15720/healthcare-equipment-market.html

For more Lifesciences and Healthcare related reports, please click here

About IndustryARC: IndustryARC primarily focuses on Cutting Edge Technologies and Newer Applications market research. Our Custom Research Services are designed to provide insights on the constant flux in the global supply-demand gap of markets. Our strong team of analysts enables us to meet the client research needs at a rapid speed, with a variety of options for your business. Any other custom requirements can be discussed with our team, drop an e-mail to [email protected] to discuss more about our consulting services.

Tags: Spirometer Market Trends, Spirometer Market, Spirometer Market Revenue, Spirometer market forecast, Spirometer market analysis, Spirometer Market Share, Spirometer Market Size, Spirometer Market Growth, Spirometer Market Research, Spirometer Market Outlook

Contact Information:

Venkat Reddy
Sales Director
Email: [email protected]
Website: www.industryarc.com
Phone: (+1) 970-236-3677

Source link

Introduction

Lungs, the principal organs of the human respiratory system, can be affected by a variety of disorders. Chronic obstructive pulmonary disease (COPD), asthma, pneumonia, upper respiratory tract infection (URTI), lower respiratory tract infection (LRTI), bronchiectasis, bronchiolitis, and other disorders are examples. COPD is a common chronic inflammatory lung disease that is primarily caused by smoking.1–3 It causes breathing difficulty, cough, production of mucus (sputum), and wheezing.2,3 These symptoms are also common in asthma disease,4 which is another most common and widespread lung diseases that is associated with airway obstruction in the lungs.4–6 It causes recurrent episodes of wheezing, breathlessness, chest tightness, and coughing, particularly at night or early in the morning.5 Pneumonia is an infection that can be caused by bacteria, viruses, and fungi and inflames the air sacs called alveoli in one or both lungs.7 It causes coughing that may cause mucus production, fever, chest pain, and shortness of breath. An upper respiratory tract infection (URTI) is a respiratory illness that occurs commonly in both children and adults and is a major cause of mild morbidity.8 It is caused by several families of viruses, such as rhinovirus, coronavirus, parainfluenza, respiratory syncytial virus (RSV), adenovirus, human metapneumovirus, influenza, enterovirus, and the recently discovered bocavirus.8 URTIs affect the upper respiratory tract, including the nose, sinuses, pharynx, or larynx.9,10 LRTI symptoms, on the other hand, vary and depend on the severity of the infection, and it is the leading cause of pediatric mortality and morbidity in low and middle-income countries.10 The less severe infections can have the same symptoms as bronchiolitis or bronchiectasis. Bronchiectasis is a long-term condition where the airways of the lungs become abnormally widened.11 It can make the lungs more vulnerable to infection, resulting in a build-up of excess mucus. It causes a persistent cough which usually brings up phlegm (sputum), and breathlessness. Bronchiolitis is also a common lung infection among infants that occurs when small breathing tubes (bronchioles) become infected.

There are several clinical options for diagnosing pulmonary diseases. For the diagnosis of pulmonary problems, imaging modalities such as chest x-rays, CT scans, and magnetic resonance imaging (MRI) are employed. In contrast, the risk of repeated doses of harmful radiation, the cost of machines, and the inconvenience of deploying them in remote regions are only a few of the challenges of adopting imaging modalities, particularly for many third-world patients. Spirometers are also commonly used to measure the volume of inspired and expired air by the lungs, as well as to detect abnormal ventilation patterns. However, a spirometer requires a skilled operator, is expensive, ineffective at detecting obstructive-restrictive defects, and necessitates a large number of patient breathing motions and forceful breathing.12 An arterial blood gas analyzer (ABG) can also be used to assess lung problems using arterial blood gas exchange (pO2 and pCO2).13 However, the ABG test is expensive and requires an invasive procedure.

Despite rapid and continual technological advancements in the field of chest disease detection, auscultation remains the most widely used and indispensable lung disease diagnostic tool.14 Lung sounds, which are collected using a stethoscope, are key indicators of respiratory health and diseases. They provide vital information regarding the health of the lungs.15,16 A stethoscope is a handy, easy, low-cost and completely non-invasive diagnostic tool used to identify lung disorders based on lung sounds. On the other hand, auscultation with a stethoscope gives a limited and subjective perception of respiratory sounds. Subjectivity leads to discrepancies in the interpretation of lung sounds by various medical experts. Subjectivity and inconsistency are caused by the physician’s hearing capability, experience, and ability to distinguish and define various sound patterns. Furthermore, the stethoscope recording is highly susceptible to noise. Noise signals mask crucial characteristics of lung sound signals, thus leading to incorrect diagnosis of lung diseases.

To overcome the constraints of the manual diagnosis technique, many machine learning and deep learning approaches for automatic classification of lung disease based on lung sound signals have been developed in various literatures.12,17–22 For instance, Islam et al,12 proposed a Welch spectral method for estimation of power spectral density (PSD) of lung sounds and a support vector machine (SVM) for classification of lung diseases as normal, asthma, and COPD claiming an accuracy of 93.3%. Similarly, Haider et al,20 proposed a respiratory sound-based classification of pulmonary diseases as normal or COPD using SVM and an accuracy of 83.6% was reported. Convolutional neural network (CNN) and deep neural network (DNN) techniques have been also proposed in the literature for the classification of lung abnormalities.15,23 Demir et al,15 employed a CNN-based approach for the classification of lung diseases (crackles, crackles combined with wheezes, wheezes, and normal) and an accuracy of 63.09% was reported using spectrogram images. Likewise, Quan et al,23 performed classification of asthma severity using DNN to classify asthma severity, achieving a maximum classification accuracy of 91.1%.

However, the majority of these and other approaches proposed for lung disease classification are either designed for specific or limited lung disease classification, are less accurate, or use the exclusive time or frequency domain analysis technique, which is inefficient for analysis of non-stationary lung sound signals. This study presents the design and construction of an electronic stethoscope for effective lung sound signal acquisition, as well as a multiresolution signal analysis technique for multi-classification of the most common lung disorders using a machine learning approach.

Materials and Methods

The general procedure used in this study for the classification of pulmonary diseases using lung sound signals is demonstrated in Figure 1. These methods involve the design and construction of an electronic stethoscope, data acquisition, data pre-processing, feature extraction, feature selection, feature visualization and normalization, data splitting, model training and evaluation, model optimization, and multi-class classification.

Figure 1 A Proposed procedure for analysis of lungs sounds and classification of lung diseases.

Design and Construction of Electronic Stethoscope

Constructing an electronic stethoscope by modifying an acoustic stethoscope necessitates the use of a transduction device to transform acoustic waves into comparable electrical signals. A dependable, cost-effective, and lightweight condenser microphone with a consistent frequency response (working frequency range of 20 Hz to 16 KHz and a high (> 62 dB) signal to noise ratio (SNR) was chosen and installed onto the stethoscope head for this purpose. The sensor (microphone) was chosen in accordance with the recommendations of the computerized respiratory sound analysis (CORSA) specification.24 The materials used for the construction of the electronic stethoscope include head of traditional stethoscope, medical grade tubing, condenser microphone, audio cable and computer installed with audacity software (audacity 2.4.2) (Figure 2)

Figure 2 An electronic stethoscope for acquisition of lung sounds.

Data Acquisition

Lungs’ sound data was collected using the constructed electronic stethoscope from the Pulmonology Unit at Jimma University Medical Center (JUMC). Each record was obtained from a clinically hospitalized patient with a different chest location, such as the left and right anterior, left and right posterior, and left and right lateral areas of the chest wall. The data was collected from nine asthmatic patients, six bronchiectasis patients, four pneumonia patients, three URTI patients, six bronchiolitis patients, seven LRTI patients, six COPD patients, and six healthy participants. A total of 60 asthma, 49 bronchiectasis, 23 pneumonia, 15 URTI, 47 bronchiolitis, 48 LRTI, 30 healthy, and 15 COPD lung sound signals were gathered from these patients. In addition, we incorporated annotated lung sound recordings from the International Conference on Biomedical and Health Informatics (ICBHI) respiratory sound dataset (10 asthma, 16 bronchiectasis, 37 pneumonia, 45 URTI, 13 bronchiolitis, 2 LRTI, 45 healthy, and 45 COPD). In total, 500 lung sound signals (287 from JUMC and 213 from the online dataset), including 75 healthy, 70 asthmatic, 60 pneumonia, 60 URTI, 50 LRTI, 60 COPD, 65 bronchiectasis, and 60 bronchiolitis lung sound recordings, were evaluated and used for machine learning model training. For data uniformity, only lung sound signals that were acquired using a single channel stethoscope were selected from the ICBHI database. In addition, all the local lung sound records were acquired (local records) or resampled (public datasets) with the sampling rate of 44.1KHz and 16 bit-depths of analog to digital (A/D) conversion. Table 1 demonstrates the demographic information of self-collected (local) and online lung sounds.

Table 1 Demographic Information of Self-Collected and Online Lung Sound Data

Data Pre-Processing

Wavelet-based denoising techniques were employed to denoise the signals without affecting essential aspects of the lung sounds. The discrete wavelet transform (DWT) is a time-frequency signal analysis approach that provides good localization in both the time and frequency domains. DWT was used to denoise lung sound signals in three steps: decomposition, detail coefficient thresholding, and reconstruction. Denoising the lung sound signals was accomplished using several wavelet functions and threshold selection criteria. For the final denoising procedure, a wavelet function and level of decomposition with a higher SNR value were used. The first (decomposition) phase involved selecting a suitable wavelet function and determining the level of wavelet decomposition (sym13 at 6th level of decomposition). In the second step, for each wavelet decomposition level 1 to , soft thresholding was applied to the detail (high frequency) coefficients of the signal. The final step, reconstruction, was done using the original approximation coefficients of level and the modified detail coefficients of levels from 1 to . Furthermore, the performance of the two thresholding methods (soft and hard thresholding) for the denoising of lung sounds was investigated using four well-known standard threshold selection rules: heursure, rigrsure, minimaxi, and sqtwolog.25,26

Feature Extraction

The DWT-based feature extraction algorithm was used for the extraction of features of the lung sound signals. Feature extraction was conducted using MATLAB (MATLAB R2019b). The extraction was performed in 7 steps.

Step 1: The lung sound signals were decomposed into six detail sub bands using DWT at level . The sub bands are the details (high-frequency band coefficients) and the approximation (low-frequency band coefficients).

Step 2: The approximation coefficients were further decomposed to extract localized information from the sub bands of detail coefficients. A six-level decomposition was performed using the Sym13 wavelet.

Step 3: All the six level detail band coefficients were then selected for further analysis and processing.

Step 4: For six detail sub bands, the frequency vector (in radians/sample) was extracted using the periodogram function in MATLAB.

Step 5: The decomposed and modified signals were reconstructed using inverse-DWT.

Step 6: Then different time and frequency domain features were computed from the reconstructed signals.

Step 7: Finally, the extracted features for all the lung sound classes were tabulated in the feature table for classification. The extracted features and their mathematical descriptions are illustrated in Table 2.

Table 2 Mathematical Description of Time Domain and Frequency Domain Features

Feature Selection and Normalization

Prior to training machine learning models, feature selection was undertaken to choose the most important features and remove undesired or redundant features. We chose and used one of the statistical filter feature selection approaches, One-way ANOVA, with a minmax normalization scheme to choose the most important features in this study. ANOVA is the process of ranking or ordering retrieved features based on the value of a scoring function. It typically assesses the usefulness of features.27 Moreover, for numerical input and categorical output classification issues, ANOVA is the preferred method.27 After feature ranking and selection of the most relevant features, feature normalization was performed to avoid bias during data training and classification.

Data Splitting

Prior to model training and classification, the data was divided into training and testing. Initially, the dataset was split using the hold-out method. Then, an eightfold cross-validation was applied. This allows the training and validation sets to cross-over in successive rounds so that each data point will have a chance of being validated against 8 separate times. Accordingly, 80% of the data (400) were used for training and the rest 20% (100) were used for testing.

Model Training and Evaluation

A total of 8 models from the 4 machine learning models including Naive Bayes, K-nearest neighbor (KNN), Ensemble and support vector machine (SVM) models were trained and compared for classification of lung diseases. Naive Bayes models are a group of classification algorithms based on Bayes’ theorem. Naive Bayes algorithms classify the data in two steps. In the first step, using the training data, the method estimates the probability distribution of the parameters by assuming each predictor is conditionally independent given the class. Then, in the prediction step, for any new data, the method computes the posterior probability distribution of that sample belonging to each class. KNN classifiers, on the other hand, classify the object based on the distance between the new object and the defined objects. The new object is assigned to the class K that has the shortest distance to class K which is defined as the nearest neighbor.28 Ensemble classifiers mix results from many weak learners into one high-quality ensemble model. The SVM algorithm represents the training data as points in a flat separated space by an apparent gap. Then, the new objects are mapped into space with the forecast category based on which side of the gap they fall. It classifies the data by finding the best hyperplane which separates data points of one class from those of the other classes.

After training of the selected machine learning models using the extracted and selected features, the best model with higher classification accuracy was selected. Then, the classification performance of the selected algorithm was further evaluated using different performance metrics.

Model Optimization and Evaluation

Bayesian optimization techniques were used to optimize our model for improving classification performance. There are different ways to perform Bayesian optimization technique including and , classification learner and regression learner apps, fit function, and .29 In this study, we used a classification learner app to automatically build an optimized model using Bayesian optimization scheme. Finally, different performance metrics such as accuracy, sensitivity, and specificity of the final optimized model were determined from the true positives (TP), false negatives (FN), true negatives (TN), and false positives (FP) of the model classification result (Equations 13).

(1)


(2)


(3)

Results

Data Pre-Processing

A DWT-based denoising of the lung sound signals which involves three steps including decomposition, detail coefficient thresholding, and reconstruction was applied for all recorded signals prior to feature selection and model training. Figure 3 demonstrates sample signal decomposition using the DWT technique. Following the decomposition, performance of soft and hard thresholding techniques using different wavelet functions at different levels of decomposition was evaluated as demonstrated in Table 3. The highest SNR values were obtained for Symlet 13 (Sym13) wavelet function at the 6th level of decomposition (SNR values of 22.50 dB and 22.48 dB using soft and hard thresholding methods, respectively). Hence, the Sym13 wavelet function at 6th level decomposition with soft thresholding method was selected for decomposition and denoising of the lung sound signals. The effect of the four threshold selection rules including Rigrsure, Sqtwolog, Heursure, and Minmax, were also compared during denoising of lung sound signals using the selected wavelet function (demonstrated in Table 4). As indicated in Table 3, the highest SNR values of the Sym13 wavelet function were observed when using the Sqtwolog threshold selection rule. Hence, the sym13 wavelet function with soft thresholding method and Sqtwolog threshold selection rule was selected and applied to denoise all the lung sound signals. Sample results of DWT decomposition on healthy and COPD patient lung sound signals are demonstrated in Figure 3. Figure 4 shows samples of the denoised and reconstructed lung sound signals of healthy and COPD subjects.

Table 3 SNR Results Obtained When Denoising the Lung Sound Signals Using Different Wavelet Functions at Different Levels of Decomposition with Soft and Hard Thresholding Methods and Sqtwolog Threshold Selection Rule. (Db4=Daubechies 4, Db10= Daubechies 10, Sym5= Symlet 5, and Sym13= Symlet 13.)

Table 4 SNR Values of the Four Threshold Selection Rules for Denoising Lung Sound Signals Using Sym13 Wavelet Function at 6th Level Decomposition with Soft Thresholding Method

Figure 3 Wavelet decomposition of lung sound signals using Sym13 at 6th level from (A) healthy patient (B) COPD subjects.

Figure 4 Denoising of lung sound signals using Sym13 wavelet function at 6th level with soft thresholding method. (A) healthy, (B) COPD.

Feature Extraction, Selection and Normalization

Following denoising, important features were extracted from all of the lung sound signals. Then, feature ranking was utilized to choose the greatest predictors or characteristics for discriminating between distinct classes. For the selection of the most discriminative features, a one-way ANOVA feature ranking approach was used. Table 5 displays the results of feature ranking using one-way ANOVA. The final three time-domain features with the lowest score values (skewness, peak value, and total harmonic distortion (THD)) were removed from the feature list, and the remaining features were chosen for model training. Following feature selection, features were normalized by removing each mean from the value of the feature and dividing it by the standard deviation.

Table 5 Results of Features Rank Using One-Way ANOVA Feature Ranking Method

Model Training, Evaluation and Optimization

The classification accuracy of 8 machine learning models trained using the same data and extracted features are demonstrated in Figure 5. Among the 8 models, Fine Gaussian SVM outperformed the rest with training classification accuracy of 97.8%, and was selected for our purpose. For further improvement of classification accuracy, the selected model was optimized using Bayesian optimization technique and a final accuracy of 98.8% was achieved. The performance of the Fine Gaussian SVM classifier after optimization is demonstrated in the confusion matrix of Figure 6.

Figure 5 Accuracy achieved on different machine learning models.

Figure 6 Confusion matrix demonstrating the performance of optimized Fine Gaussian SVM classifier per each true class.

Model Testing

As a final evaluation, testing was performed on the final optimized model with a new dataset. A total of 100 lung sound signals were used for testing. The classification performance of the final optimized model is demonstrated in the confusion matrix presented in Figure 7. Accordingly, an average classification accuracy of 99%, sensitivity of 99.04% and specificity of 99.2% were achieved using the new dataset.

Figure 7 Test result of the final optimized model using unseen dataset.

Discussion

Auscultation of lung sounds provide vital information about the physiology and pathology of lungs and airways obstruction.19 Accurate lung diagnosis of lung sounds requires the ability to distinguish normal breathing sounds from various abnormal adventitious sounds. Many spectral analysis techniques including Fourier transform (FT), fast Fourier transform (FFT)-Welch, autoregressive (AR), AR-Burg, autoregressive moving average (ARMA), and Mel-frequency cepstral coefficients (MFCC) have been used in the literature for lung sound signal analysis.19,20,30,31 Time domain analysis techniques such as empirical mode decomposition and multiscale entropy techniques have been also used for analysis of lung sounds.32–34

However, frequency-domain or time-domain analysis techniques alone cannot provide enough information of the non-stationary lung sound signals. Due to this, time-frequency domain analysis techniques have been also employed in literature for analysis of the non-stationary lung sound signals.15,19,35 Short-time Fourier transform (STFT)-based analysis techniques provide both time and frequency information of signals but unable to extract multiple features due to single and fixed window regions.

In this study, acquisition and wavelet multiresolution analysis of lung sounds using DWT and classification of most common lung diseases using machine learning techniques has been proposed. Lung sounds of 7 top lung diseases and health subjects were recorded using a custom designed and constructed electronic stethoscope. In addition, online annotated lung sound signals were also included in our dataset. All the lung sound records were normalized at the same sampling rate of 44.1KHz and bit-depth of 16 bits. Furthermore, DWT-based denoising of the lung sound signals involving decomposition, detail coefficient thresholding, and reconstruction was performed on all signals. Four wavelet functions (Db4, Db10, Sym5, and Sym13) and four different threshold selection rules (Rigrsure, Sqtwolog, Heursure, and Minmax) were compared in denoising the lung sound signals. The SNR results for the different wavelet functions were calculated at different levels of decomposition (3rd to 7th) for comparison. After evaluation, the Sym13 wavelet function at 6th level decomposition with soft thresholding method and Sqtwolog threshold selection rule outperformed the others and was selected for denoising operation.

A DWT-based feature extraction technique was applied to extract important features from the lung sound signals. A total of 16 features were extracted from the lung sound signals. After pertinent feature extractions, feature selection by a means of variable or feature ranking was done to select the most discriminative features. A one-way ANOVA feature ranking method using normalization scheme was implemented and 13 out of 16 most discriminative features were selected to train different machine learning models. Then, feature normalization was performed to all extracted feature values to avoid bias to the classifier during model training and classification. After feature normalization, the whole data was split into training (80%) and testing (20%). Then, an eightfold cross-validation technique was applied to the training dataset. 8 machine learning models from 4 families were trained and validated to select the best performing model.

Better results were achieved by Gaussian Naive Bayes and Kernel Naive Bayes algorithms with an accuracy of 94.8% and 95.8%, respectively. KNN classifiers typically have best predictive accuracy in low dimensions but might not be good in high dimensions.22 Additionally, KNN classifiers have high memory usage and are not easy to interpret. Naive Bayes classifiers are easy to interpret and useful for multiclass classification.22 However, the Naive Bayes algorithms need a small amount of training data for estimating the vital parameters which made the algorithms extremely fast compared to more sophisticated methods.36 In addition, ensemble algorithms, RUS Boosted trees and Bagged trees were trained, and an accuracy of 96.5% and 97.3%, respectively, were achieved. Ensemble classifiers tend to be slow to fit because they often need many weak learners.36 SVM algorithms including Cubic SVM and Fine Gaussian SVM were also trained and a relatively higher accuracy of 97.5% and 97.8%, respectively, were achieved. Accordingly, the model with the highest classification accuracy, Fine Gaussian SVM was selected and further optimized using Bayesian optimization technique for accuracy improvement. After model optimization, the accuracy of our model was improved to 98.8%. Furthermore, the optimal model parameter values of box constraint level and kernel scale were found to be 417.88 and 24.82, respectively. Finally, the optimized model was tested using unseen (new) dataset and an overall accuracy of 99%, sensitivity of 99.04% and specificity of 99.2% were achieved.

The proposed method delivered considerably improved results compared to similar lung sound signal processing, analysis and classification methods proposed in the literature. A summary of comparison between related works and the current work is illustrated in Table 6.

Table 6 Comparison of the Current Work with Related Literature

The results found in the current work for the classification of most common lung disease using lung sound signals demonstrate the proposed methods’ potential in helping physicians in the diagnosis of lung disease. We acknowledge that the lung sound signals used for model training were acquired using a single channel data acquisition system. Multichannel data acquisition systems could be more efficient to gather more adequate information about the lungs pathology.

Conclusion

In this study, a cost-effective electronic stethoscope for acquisition of lung sounds, DWT for the time-frequency multiresolution analysis of lung sound signals, and different machine learning classifiers were employed for multi-class classification of most common lung diseases.

The Sym13 wavelet function with soft thresholding method and sqtwolog threshold selection rule was used for decomposition and denoising of the lung sound signal. DWT-based feature extraction was performed for the extraction of different features from the lung sound signals. A single way ANOVA was used to select the most discriminative features used to train different machine learning models. After model selection, optimization, training, and testing, an overall accuracy of 99%, a sensitivity of 99.04%, and a specificity of 99.2% were acquired using a Fine Gaussian SVM model. The proposed method delivered a considerably improved result for the classification of most common lung diseases including asthma, pneumonia, COPD, URTI, LRTI, bronchiectasis, bronchiolitis, and healthy. The experimental results demonstrate that the developed system has the potential to be used as a decision support system for physicians, especially in low resource settings and in the diagnosis of lung disease.

Data Sharing Statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ethics Approval and Consent to Participate

This research has been approved by Jimma University’s research ethics institutional review board (IRB). This research has been performed in accordance with the ethical standards as laid down in the 1964\Declaration of Helsinki and its later amendments or comparable ethical standards. An informed written consent form was obtained from all participants prior to data collection.

Acknowledgments

The resources required for this research were provided by the school of Biomedical Engineering, Jimma institute of Technology, Jimma University and Jimma University Medical Center (JUMC). We would like to acknowledge our clinical collaborators Prof. Yoo, Dr. Assefa, Dr. Bekela, and Dr. Aneso, who are the medical doctors working in JUMC, Department of Internal Medicine, Pulmonology Unit, for their valuable advice and guidance from clinical perspectives.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors report no conflicts of interest in this work.

References

1. Prasad B. Chronic Obstructive Pulmonary Disease (COPD). IJPRT. 2020;10(1):67LP- 71.

2. Vestbo J. COPD: definition and phenotypes. Clin Chest Med. 2014;35(1):1–6. doi:10.1016/J.CCM.2013.10.010

3. Calverley PMA, Georgopoulos D. Chronic obstructive pulmonary disease: symptoms and signs. Eur Respir Monogr. 1998;3(7):6–24. doi:10.1183/1025448X.00038002

4. Kirenga BJ, Schwartz JI, De Jong C, van der Molen T, Okot-Nwang M. Guidance on the diagnosis and management of asthma among adults in resource limited settings. Afr Health Sci. 2015;15(4):1189. doi:10.4314/AHS.V15I4.18

5. Kim H, Mazza J. Asthma. Allergy Asthma Clin Immunol. 2011;7(Suppl1):S2. doi:10.1186/1710-1492-7-S1-S2

6. Cukic V, Lovre V, Dragisic D, Ustamujic A. Asthma and Chronic Obstructive Pulmonary Disease (COPD) – differences and similarities. Mater Sociomed. 2012;24(2):100. doi:10.5455/MSM.2012.24.100-105

7. Grief SN, Loza JK. Guidelines for the evaluation and treatment of pneumonia. Prim Care. 2018;45(3):485–503. doi:10.1016/J.POP.2018.04.001

8. Cotton MF, Innes S, Jaspan H, Madide A, Rabie H. Management of upper respiratory tract infections in children. S Afr Fam Pract. 2008;50(2):6. doi:10.1080/20786204.2008.10873685

9. Rohilla A, Sharma VK, Kumar S. Upper respiratory tract infections: an overview; Int J Curr Pharmaceut Res. 2013;5:1–3.

10. Mirkarimi M, Alisamir M, Saraf S, Heidari S, Barouti S, Mohammadi S. Clinical and epidemiological determinants of lower respiratory tract infections in hospitalized pediatric patients. Int J Pediatr. 2020;2020:1–7. doi:10.1155/2020/8844420

11. Rademacher J, Welte T. Bronchiectasis. Dtsch Arztebl Int. 2011;108(48):809–815. doi:10.3238/ARZTEBL.2011.0809

12. Islam MA, Bandyopadhyaya I, Bhattacharyya P, Saha G. Classification of normal, Asthma and COPD subjects using multichannel lung sound signals. Proc 2018 IEEE Int Conf Commun Signal Process ICCSP 2018; 2018: 290–294. doi:10.1109/ICCSP.2018.8524439

13. Dukić L, Kopčinović LM, Dorotić A, Baršić I. Blood gas testing and related measurements: national recommendations on behalf of the Croatian Society of Medical Biochemistry and Laboratory Medicine. Biochem Medica. 2016;26(3):318–336. doi:10.11613/BM.2016.036

14. Altan G, Kutlu Y, Garbi Y, Pekmezci AÖ, Nural S, Altan G. Multimedia respiratory database ([email protected]): auscultation sounds and Chest X-rays. Nat Eng Sci. 2021. doi:10.48550/arxiv.2101.10946

15. Demir F, Sengur A, Bajaj V. Convolutional neural networks based efficient approach for classification of lung diseases. Heal Inf Sci Syst. 2020;8(1). doi:10.1007/S13755-019-0091-3

16. Andrès E, Gass R, Charloux A, Brandt C, Hentzler A. Respiratory sound analysis in the era of evidence-based medicine and the world of medicine 2.0. J Med Life. 2018;11(2):89–106.

17. Göğüş FZ, Karlık B, Harman G. Identification of pulmonary disorders by using different spectral analysis methods. Int J Comput Intell Syst. 2016;9(4):595–611. doi:10.1080/18756891.2016.1204110

18. Gökçen A. Computer-aided diagnosis system for chronic obstructive pulmonary disease using empirical wavelet transform on auscultation sounds. Comput J. 2021;64(11):1775–1783. doi:10.1093/COMJNL/BXAA191

19. Aykanat M, Kılıç Ö, Kurt B, Saryal S. Classification of lung sounds using convolutional neural networks. Eurasip J Image Video Process. 2017;2017(1):1–9. doi:10.1186/S13640-017-0213-2/TABLES/2

20. Haider NS, Singh BK, Periyasamy R, Behera AK. Respiratory sound based classification of chronic obstructive pulmonary disease: a risk stratification approach in machine learning paradigm. J Med Syst. 2019;43(8):8. doi:10.1007/s10916-019-1388-0

21. Vora S, Shah PC. COPD classification using machine learning algorithms. Int Res J Eng Technol. 2019;6:608–611.

22. Osisanwo FY, Akinsola JE, Awodele O, Hinmikaiye JO, Olakanmi O, Akinjobi J. Supervised machine learning algorithms: classification and comparison. Int J Comput Trends Technol. 2017;48(3):128–138. doi:10.14445/22312803/IJCTT-V48P126

23. Do Q, Son TC, Chaudri J. Classification of asthma severity and medication using tensorflow and multilevel databases. Procedia Comput Sci. 2017;113:344–351. doi:10.1016/j.procs.2017.08.343

24. Rao A, Huynh E, Royston TJ, Kornblith A, Roy S. Acoustic methods for pulmonary diagnosis. IEEE Rev Biomed Eng. 2019;12:221. doi:10.1109/RBME.2018.2874353

25. Verma N, Verma AK. Performance analysis of wavelet thresholding methods in denoising of audio signals of some Indian musical instruments. Int J Eng Sci Technol. 2012;4:2040–2045.

26. Devnath L, Kumer S, Nath D, Das AK, Islam R. Selection of wavelet and thresholding rule for denoising the ECG signals. Annals of Pure and Applied Mathematics. 2015;10(1):65–73.

27. Elssied NOF, Ibrahim O, Osman AH. A novel feature selection based on one-way ANOVA F-test for e-mail spam classification. Res J Appl Sci Eng Technol. 2014;7(3):625–638. doi:10.19026/RJASET.7.299

28. Neili Z, Fezari M, Redjati A. ELM and K-nn machine learning in classification of breath sounds signals. Int J Electr Comput Eng. 2020;10(4):3528–3536. doi:10.11591/IJECE.V10I4.PP3528-3536

29. Gambella C, Ghaddar B, Naoum-Sawaya J. Optimization problems for machine learning: a survey. Eur J Oper Res. 2021;290(3):807–828. doi:10.1016/J.EJOR.2020.08.045

30. Don S. Random subset feature selection and classification of lung sound. Procedia Comput Sci. 2020;167(2019):313–322. doi:10.1016/j.procs.2020.03.228

31. Sengupta N, Sahidullah M, Saha G. Lung sound classification using cepstral-based statistical features. Comput Biol Med. 2016;75:118–129. doi:10.1016/J.COMPBIOMED.2016.05.013

32. Mondal A, Bhattacharya P, Saha G. Detection of lungs status using morphological complexities of respiratory sounds. Sci World J. 2014;2014:1–9. doi:10.1155/2014/182938

33. Rizal A, Hidayat R, Nugroho HA. Multiscale tsallis entropy for pulmonary crackle detection. Int J Adv Intell Informatics. 2018;4(3):192–201. doi:10.26555/ijain.v4i3.273

34. Rizal A, Hidayat R, Nugroho HA. Comparison of multiscale entropy techniques for lung sound classification. Indones J Electr Eng Comput Sci. 2018;12(3):984–994. doi:10.11591/IJEECS.V12.I3.PP984-994

35. Adhi Pramono RX, Imtiaz SA, Rodriguez-Villegas E. Evaluation of features for classification of wheezes and normal respiratory sounds. PLoS One. 2019;14(3):e0213659. doi:10.1371/JOURNAL.PONE.0213659

36. Alsheref FK, Gomaa WH. Blood diseases detection using classical machine learning algorithms. Int J Adv Comput Sci Appl. 2019;10(7):77–81. doi:10.14569/IJACSA.2019.0100712

37. Hosseini M, Ren H, Rashid H-A, Mazumder AN, Prakash B, Mohsenin T. Neural networks for pulmonary disease diagnosis using auditory and demographic information. arXiv preprint arXiv. 2020. doi:10.48550/arxiv.2011.13194

Source link