Diagnosis codes are frequently used as criteria to define patient populations. While diagnosis codes alone may not define a cohort with perfect accuracy, they are a useful mechanism to narrow a population from “everyone in the EHR” to a cohort highly enriched with the condition of interest. Our analysis of U09.9 shows that this code may serve in a similar capacity to identify long COVID patients. However, temporality and rate of uptake by providers are critical issues that must be considered. U09.9 was released for use nearly 2 years into the COVID-19 pandemic, resulting in potentially millions of patients with long COVID who “missed out” on being assigned the code. Our findings must thus be interpreted through this lens of partial and incremental adoption. More work is needed to understand clinical variability and barriers to uptake by providers.
We investigated whether the use of non-specific coding such as B94.8 (“Sequelae of other specified infectious and parasitic diseases”) could be used as a proxy for early case identification. Our findings show B94.8 use increasing among COVID patients from April 2021 to October 2021, indicating a potential shift in clinical practice patterns to code for long COVID presentation as guided by the Centers for Disease Control . While B94.8 can be used for long COVID ascertainment in EHRs prior to October 2021, it should be noted that B94.8 is used to code for any sequelae of any infectious disease. For this reason, it may not be specific enough to rely on for highly precise long COVID case ascertainment without applying additional logic (e.g., requiring a positive COVID test prior to B94.8). Even still, it is likely the most reliable structured variable in the EHR to identify potential long COVID patients prior to October 1, 2021.
Our diagnosis clusters suggest that long COVID is not a single phenotype, but rather a collection of sub-phenotypes that may benefit from different diagnostics and treatments. Each of these clusters contains conditions and symptoms reported in existing long COVID literature , clearly suggests that the definition of long COVID is more expansive than lingering respiratory symptoms , and illustrates that long COVID can manifest differently among patients in different age groups. Notably, among the conditions represented in our clusters, six have overlap with the eight conditions identified in another recent large-scale EHR analysis as high confidence for association with PASC, suggesting the particular importance of those conditions: anosmia/dysgeusia, chronic fatigue syndrome, chest pain, palpitations, shortness of breath, and type 2 diabetes . Overall, the clusters can be summarized as neurological (in blue), cardiopulmonary (in green), gastrointestinal (in purple), upper respiratory (in yellow), and comorbid conditions (in red). The clustering for the youngest patients (< 21 years of age, Fig. 2a) is the most unique, with distinct upper respiratory and gastrointestinal clusters that are not seen in other age groups. Moreover, the neurological cluster for this group also includes multiple cardiopulmonary features (e.g., dyspnea, palpitations). Patients aged 65 + (Fig. 2d) are also unique, in that they present with more chronic diseases associated with aging (e.g., congestive heart failure, atherosclerosis, atrial fibrillation) in addition to long COVID symptoms. The comorbid conditions cluster is unique in that it likely does not represent symptoms of long COVID, but rather a collection of comorbid conditions that increase in incidence as patients age. The impact of these comorbid conditions on risk and outcomes of long COVID requires further study.
Also noteworthy is the fact that the neurological cluster appears more prominently in younger groups, especially patients 21–45 years of age. Of particular note is the appearance of myalgic encephalomyelitis (listed in Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT) as “chronic fatigue syndrome,” a non-preferred term)—a disease which parallels long COVID in many ways [37,38,39]—in the neurological cluster across all age groups, suggesting not only frequent co-occurrence with a U09.9 diagnosis, but also co-occurrence with other neurological symptoms. The cluster differences we see among age groups make a case for age stratification when studying U09.9, and long COVID in general. Regardless, given long COVID’s heterogeneity in presentation, course, and outcome, the clustering of symptoms may prove informative for future development of classification and diagnostic criteria .
The common procedures around the time of U09.9 index provide insight into diagnostics and treatments currently used by providers for patients presenting with long COVID, for which treatment guidelines remain under development [41,42,43,44]. For new diseases where consensus is lacking, care is often ad hoc and informed by both the symptoms that patients present with and the available diagnostics and treatments that providers can offer. The identification and characterization of care patterns is an important step in designing future research to assess the efficacy and outcomes of these interventions. Radiographic imaging is a common occurrence across all age groups, with an average of 22.8% of patients with at least one imaging procedure in the analysis window. Electrocardiography (ECG) and echocardiography are also relatively common across all age groups, though patients younger than 21 years of age have the highest proportion (20.0% and 13.2% for ECG and echo, respectively, compared with an average of 16.7% and 7.4% across the other age groups). Pulmonary function testing shows a slight increase in frequency with more advanced age. Also of interest is the fact that some patients are receiving rehabilitation services in the 60 days after diagnosis, such as physical and occupational therapy, which lends insight into the burden of functional disability for patients with long COVID. The proportion of patients receiving rehabilitation services also rises with patient age.
Differences across age groups were less apparent in the medication analysis (Additional file 1: Supplemental Fig. 2), though the youngest patients appear slightly more likely to be prescribed medications for gastrointestinal, cardiac, and neurological indications. Unsurprisingly, respiratory system drugs were also commonly prescribed across all age groups. Interestingly, antibacterials were used frequently across all age groups; it is unclear whether patients with long COVID are more susceptible to bacterial infections, or if there may be overuse of antibiotics in the setting of fluctuating respiratory long COVID symptoms or viral infections [45, 46]. Corticosteroids were also commonly used, presumably to treat persistent inflammation as a possible mechanism mediating long COVID symptoms. The variety of medication categories seen in our analysis reflect the potential multi-system organ involvement and symptom clusters in long COVID that we see in the analysis of conditions.
We also investigated how demographics and SDoH contribute to variation in diagnosis with U09.9. When evaluating the U09.9 cohort across age groups and SDoH variables, distinct trends can be observed (see Table 1). Patients with a U09.9 diagnosis code are more likely to live in areas with low percentages of residents who are unemployed or on public health insurance. Patients living in counties with a high level of poverty make up the smallest share of the U09.9 cohort. In contrast, research shows that socially deprived areas have higher rates of COVID-19 cases and deaths [47, 48]. Given the higher rates of COVID-19, lower rates of long COVID seem unlikely. Rather, patients in deprived areas may be less likely to receive a U09.9 code in a healthcare setting, which may have downstream implications for their later identification as a long COVID patient. Moreover, a large majority of the U09.9 cohort identifies as female, White, and non-Hispanic compared to all SARS-CoV-2 positive patients at the same sites. These trends are unlikely to be an accurate reflection of the true population with long COVID, but may instead illustrate racial and social disparities in access to and experience with healthcare in the USA. Clearly, the role of access to providers and the economic means to afford long COVID care should continue to be studied for their role as contributors to disparate care and outcomes, as well as sources of research and algorithmic bias.
All EHR data is limited in that patients with lower access or barriers to care are less likely to be represented. Moreover, missing race and ethnicity data is likely not missing at random , and the inclusion of patients with missing race and/or ethnicity data in this analysis may bias interpretation of our demographic findings. EHR heterogeneity across sites may mean that a U09.9 code at one site does not quite equate to a U09.9 code at another. Moreover, we are not able to know what type of provider issued the U09.9 diagnosis (i.e., specialty), and different clinical organizations have different coding practices.
As the U09.9 code is still quite new and our sample size is limited, we cannot yet confidently label these clusters as clear “long COVID subtypes.” Rather, these clusters are intended to be hypothesis generating, with additional work underway by the RECOVER consortium to further develop and validate these clusters. It should also be noted that many symptoms are not coded in the EHR (and may, for example, be more likely to appear in free-text notes rather than diagnosis code lists). Future work will incorporate these non-structured sources of symptoms for use in our clustering methodology. The newness of the code should also be taken into account when interpreting any of our findings. The CDC has created guidance for use of the code ; however, despite this, as noted by an attendee at the CDC’s March 2021 Q&A session that covered U09.9, “physicians don’t speak coding” . Thus, there is likely to be a disconnect between CDC’s intended use of the code and its actual application in practice, in both the billing and clinical contexts. Ioannou et al. echoed this in a recent paper, noting great variability in the documentation of long COVID across regions, medical centers, and populations . We are unlikely to know the extent of this disconnect until U09.9 has been in use for a longer period of time; however, it should be assumed that some number of the patients that receive a U09.9 code may indeed be “false positives.” In future work, chart reviews of U09.9 patients will shed light on this issue.
Given the variable uptake of the U09.9 code, it is challenging to accurately identify comparator groups for this population—i.e., the absence of a U09.9 code cannot, at this time, be interpreted as the absence of long COVID. Relying solely on U09.9 to identify a complete long COVID cohort will undoubtedly miss many valid cases that are simply “unlabeled.” This will continue to be an issue in future research, especially when evaluating the effect of PASC on patient morbidity and utilization of diagnostic testing and treatments.