Data-driven identification of post-acute SARS-CoV-2 infection subphenotypes.

TitleData-driven identification of post-acute SARS-CoV-2 infection subphenotypes.
Publication TypeJournal Article
Year of Publication2023
AuthorsZhang H, Zang C, Xu Z, Zhang Y, Xu J, Bian J, Morozyuk D, Khullar D, Zhang Y, Nordvig AS, Schenck EJ, Shenkman EA, Rothman RL, Block JP, Lyman K, Weiner MG, Carton TW, Wang F, Kaushal R
JournalNat Med
Date Published2023 Jan
KeywordsAnxiety, Anxiety Disorders, COVID-19, Disease Progression, Humans, Post-Acute COVID-19 Syndrome, SARS-CoV-2

The post-acute sequelae of SARS-CoV-2 infection (PASC) refers to a broad spectrum of symptoms and signs that are persistent, exacerbated or newly incident in the period after acute SARS-CoV-2 infection. Most studies have examined these conditions individually without providing evidence on co-occurring conditions. In this study, we leveraged the electronic health record data of two large cohorts, INSIGHT and OneFlorida+, from the national Patient-Centered Clinical Research Network. We created a development cohort from INSIGHT and a validation cohort from OneFlorida+ including 20,881 and 13,724 patients, respectively, who were SARS-CoV-2 infected, and we investigated their newly incident diagnoses 30-180 days after a documented SARS-CoV-2 infection. Through machine learning analysis of over 137 symptoms and conditions, we identified four reproducible PASC subphenotypes, dominated by cardiac and renal (including 33.75% and 25.43% of the patients in the development and validation cohorts); respiratory, sleep and anxiety (32.75% and 38.48%); musculoskeletal and nervous system (23.37% and 23.35%); and digestive and respiratory system (10.14% and 12.74%) sequelae. These subphenotypes were associated with distinct patient demographics, underlying conditions before SARS-CoV-2 infection and acute infection phase severity. Our study provides insights into the heterogeneity of PASC and may inform stratified decision-making in the management of PASC conditions.

Alternate JournalNat Med
PubMed ID36456834
PubMed Central IDPMC9873564
Grant ListOT2 HL161847 / HL / NHLBI NIH HHS / United States
Institute of Artificial Intelligence for Digital Health
Faculty Publication