Precision phenotyping for curating research cohorts of patients with unexplained post-acute sequelae of COVID-19

Scalable identification of patients with post-acute sequelae of COVID-19 (PASC) is challenging due to a lack of reproducible precision phenotyping algorithms, which has led to suboptimal accuracy, demographic biases, and underestimation of the PASC. In a retrospective case-control study, we developed a precision phenotyping algorithm for identifying cohorts of patients with PASC. We used longitudinal electronic health records data from over 295,000 patients from 14 hospitals and 20 community health centers in Massachusetts. The algorithm employs an attention mechanism to simultaneously exclude sequelae that prior conditions can explain and include infection-associated chronic conditions. We performed independent chart reviews to tune and validate the algorithm. The PASC phenotyping algorithm improves precision and prevalence estimation and reduces bias in identifying PASC cohorts compared to the ICD-10-CM code U09.9. The algorithm identified a cohort of over 24,000 patients with 79.9% precision. Our estimated prevalence of PASC was 22.8%, which is close to the national estimates for the region. We also provide in-depth analyses, encompassing identified lingering effects by organ, comorbidity profiles, and temporal differences in the risk of PASC. PASC precision phenotyping boasts superior precision and prevalence estimation while exhibiting less bias in identifying patients with PASC. The cohort derived from this algorithm will serve as a springboard for delving into the genetic, metabolomic, and clinical intricacies of PASC, surmounting the constraints of prior PASC cohort studies. This research was funded by the US National Institute of Allergy and Infectious Diseases (NIAID). [Display omitted] •Precision PASC phenotyping algorithm identifies long COVID with attention mechanism•Incorporates both infection-association chronic condition and diagnosis of exclusion•Outperforms U09.9 in precision and reduces bias in long COVID identification•Captures rare long COVID symptoms, including vision loss and diabetic complications Identifying cohorts of patients with post-acute sequelae of COVID-19 (PASC), or long COVID, using real-world data is complex. The absence of precise definitions for PASC poses significant challenges in clinical research and patient care. Utilizing electronic health records from a large integrated healthcare system, Azhir et al. developed a precision phenotyping algorithm incorporating a novel attention mechanism that accounts for both infe