ArticleMed (New York, N.Y.)2025
Precision phenotyping for curating research cohorts of patients with unexplained post-acute sequelae of COVID-19.
Article in Med (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Long COVID Persistence and Surveillance Gaps Across 58 US Hospitals.JAMA network open · 2026Article
- Temporal Learning with Dynamic Range (TLDR) for modeling recurrent exposure and treatment outcomes.Scientific reports · 2026Article
- Paxlovid shows organ-specific and age-specific impacts on risk of developing post-acute sequelae of COVID-19.Communications medicine · 2026Article
- The age paradox in post-infectious sequelae: physiological reserve outweighs chronological age in Long COVID susceptibility.medRxiv : the preprint server for health sciences · 2026Article
- Longitudinal modeling of Post-COVID-19 condition over three years: A machine learning approach using clinical, neuropsychological, and fluid markers.Scientific reports · 2026Article
- Metabolomics-Based Machine Learning Diagnostics of Post-Acute Sequelae of SARS-CoV-2 Infection.Metabolites · 2025Article
- Possible link between steatotic liver diseases, severe COVID-19 and cognitive impairment in post-COVID-19 syndrome.Infection · 2025Article
- Extracting post-acute sequelae of SARS-CoV-2 infection symptoms from clinical notes via hybrid natural language processing.npj health systems · 2025Article
- Stellate Ganglion Block for the Management of Long COVID Symptoms: A Retrospective Cohort Study.Cureus · 2025Article
- Post-COVID syndrome patients show reduced anti-Spike antibodies compared to COVID-recovered controls, but enhanced IgG4/IgG1 switch after the third vaccine dose.Frontiers in immunology · 2025Article
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Authors and funding
18 authors.
Funding
Abstract
backgroundScalable 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.
methodsIn 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.
findingsThe 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.
conclusionsPASC 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.
fundingThis research was funded by the US National Institute of Allergy and Infectious Diseases (NIAID).
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