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Data-driven identification of post-acute SARS-CoV-2 infection subphenotypes.
Zhang, Hao; Zang, Chengxi; Xu, Zhenxing; Zhang, Yongkang; Xu, Jie; Bian, Jiang; Morozyuk, Dmitry; Khullar, Dhruv; Zhang, Yiye; Nordvig, Anna S; Schenck, Edward J; Shenkman, Elizabeth A; Rothman, Russell L; Block, Jason P; Lyman, Kristin; Weiner, Mark G; Carton, Thomas W; Wang, Fei; Kaushal, Rainu.
  • Zhang H; Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
  • Zang C; Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
  • Xu Z; Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
  • Zhang Y; Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
  • Xu J; Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, USA.
  • Bian J; Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, USA.
  • Morozyuk D; Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
  • Khullar D; Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
  • Zhang Y; Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
  • Nordvig AS; Department of Neurology, Weill Cornell Medicine, New York, NY, USA.
  • Schenck EJ; Department of Medicine, Division of Pulmonary and Critical Care Medicine, Weill Cornell Medicine, New York, NY, USA.
  • Shenkman EA; Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, USA.
  • Rothman RL; Center for Health Services Research, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Block JP; Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, USA.
  • Lyman K; Louisiana Public Health Institute, New Orleans, LA, USA.
  • Weiner MG; Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
  • Carton TW; Louisiana Public Health Institute, New Orleans, LA, USA.
  • Wang F; Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA. few2001@med.cornell.edu.
  • Kaushal R; Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
Nat Med ; 2022 Dec 01.
Article in English | MEDLINE | ID: covidwho-2237481
ABSTRACT
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.

Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Observational study / Prognostic study Topics: Long Covid Language: English Journal subject: Molecular Biology / Medicine Year: 2022 Document Type: Article Affiliation country: S41591-022-02116-3

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Observational study / Prognostic study Topics: Long Covid Language: English Journal subject: Molecular Biology / Medicine Year: 2022 Document Type: Article Affiliation country: S41591-022-02116-3