Precision Phenotyping for Curating Research Cohorts of Patients with Post-Acute Sequelae of COVID-19 (PASC) as a Diagnosis of Exclusion (preprint)

Scalable identification of patients with the post-acute sequelae of COVID-19 (PASC) is challenging due to a lack of reproducible precision phenotyping algorithms and the suboptimal accuracy, demographic biases, and underestimation of the PASC diagnosis code (ICD-10 U09.9). In a retrospective case-control study, we developed a precision phenotyping algorithm for identifying research cohorts of PASC patients, defined as a diagnosis of exclusion. We used longitudinal electronic health records (EHR) data from over 295 thousand patients from 14 hospitals and 20 community health centers in Massachusetts. The algorithm employs an attention mechanism to exclude sequelae that prior conditions can explain. We performed independent chart reviews to tune and validate our precision phenotyping algorithm. Our PASC phenotyping algorithm improves precision and prevalence estimation and reduces bias in identifying Long COVID patients compared to the U09.9 diagnosis code. Our algorithm identified a PASC research cohort of over 24 thousand patients (compared to about 6 thousand when using the U09.9 diagnosis code), with a 79.9 percent precision (compared to 77.8 percent from the U09.9 diagnosis code). Our estimated prevalence of PASC was 22.8 percent, which is close to the national estimates for the region. We also provide an in-depth analysis outlining the clinical attributes, encompassing identified lingering effects by organ, comorbidity profiles, and temporal differences in the risk of PASC. The PASC phenotyping method presented in this study boasts superior precision, accurately gauges the prevalence of PASC without underestimating it, and exhibits less bias in pinpointing Long COVID patients. The PASC cohort derived from our algorithm will serve as a springboard for delving into Long COVID's genetic, metabolomic, and clinical intricacies, surmounting the constraints of recent PASC cohort studies, which were hampered by their limited size and available outcome data.

Comprehensive characterization of COVID-19 patients with repeatedly positive SARS-CoV-2 tests using a large US electronic health record database (preprint)

Background: In the absence of genome sequencing, two positive molecular SARS-CoV-2 tests separated by negative tests, prolonged time, and symptom resolution remain the best surrogate measure of possible re-infection. Methods: Using a large electronic health record database, we characterized clinical and testing data for 23 patients with repeatedly positive SARS-CoV-2 PCR test results >60 days apart, separated by >2 consecutive negative test results. Prevalence of chronic medical conditions, symptoms and severe outcomes related to COVID-19 illness were ascertained. Results: Median age was 64.5 years, 40% were Black, and 39% were female. 83% smoked within the prior year, 61% were overweight/obese, 83% had immune compromising conditions, and 96% had >2 comorbidities. Median interval between the two positive tests was 77 days. Among the 19 patients with 60-89 days between positive tests, 17 (89%) exhibited symptoms or clinical manifestations indicative of COVID-19 at the time of the second positive test and 14 (74%) were hospitalized at the second positive test. Of the four patients with >90 days between two positive tests, two had mild or no symptoms at the second positive test and one, an immune compromised patient, had a brief hospitalization at the first diagnosis, followed by ICU admission at the second diagnosis three months later. Conclusions: Our study demonstrated a high prevalence of immune compromise, comorbidities, obesity and smoking among patients with repeatedly positive SARS-CoV-2 tests. Despite limitations, including lack of semi-quantitative estimates of viral load, these data may help prioritize suspected cases of reinfection for investigation and continued surveillance.