Precision Phenotyping for Curating Research Cohorts of Patients With Unexplained Post-acute Sequelae of COVID-19

Authors

Alaleh Azhir
Jonas Hügel
Jiazi Tian
Jingya Cheng
Ingrid V Bassett
Douglas S Bell
Elmer V Bernstam
Maha R Farhat
Darren W Henderson
Emily S Lau
Michele Morris
Yevgeniy R Semenov
Virginia A Triant
Shyam Visweswaran
Zachary H Strasser
Jeffrey G Klann
Shawn N Murphy
Hossein Estiri

Publication Date

3-14-2025

Journal

Medicine

Abstract

BACKGROUND: 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.

METHODS: 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.

FINDINGS: 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.

CONCLUSIONS: 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.

FUNDING: This research was funded by the US National Institute of Allergy and Infectious Diseases (NIAID).

Keywords

Humans, COVID-19, Phenotype, Retrospective Studies, Algorithms, Massachusetts, Post-Acute COVID-19 Syndrome, Female, Male, Case-Control Studies, Middle Aged, Electronic Health Records, SARS-CoV-2, Adult, Prevalence, Aged

DOI

10.1016/j.medj.2024.10.009

PMID

39520983

PMCID

PMC11911085

PubMedCentral® Posted Date

3-17-2025

PubMedCentral® Full Text Version

Author MSS

Published Open-Access

yes