4.2 Article

COVID Symptoms, Symptom Clusters, and Predictors for Becoming a Long-Hauler Looking for Clarity in the Haze of the Pandemic

Journal

CLINICAL NURSING RESEARCH
Volume 31, Issue 8, Pages 1390-1398

Publisher

SAGE PUBLICATIONS INC
DOI: 10.1177/10547738221125632

Keywords

COVID-19; long-COVID; electronic health record; machine learning

Categories

Funding

  1. National Center for Research Resources
  2. National Center for Advancing Translational Sciences, National Institutes of Health [UL1 TR001414]

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This study retrospectively analyzed electronic health records from the University of California and found that the prevalence of post-acute sequelae of SARS-CoV-2 (PASC) was 11%. Five symptom clusters associated with PASC were identified. Women were more likely than men to develop PASC, with this pattern observed across different age groups and ethnicities.
Post-acute sequelae of SARS-CoV-2 (PASC) is defined as persistent symptoms after apparent recovery from acute COVID-19 infection, also known as COVID-19 long-haul. We performed a retrospective review of electronic health records (EHR) from the University of California COvid Research Data Set (UC CORDS), a de-identified EHR of PCR-confirmed SARS-CoV-2-positive patients in California. The purposes were to (1) describe the prevalence of PASC, (2) describe COVID-19 symptoms and symptom clusters, and (3) identify risk factors for PASC. Data were subjected to non-negative matrix factorization to identify symptom clusters, and a predictive model of PASC was developed. PASC prevalence was 11% (277/2,153), and of these patients, 66% (183/277) were considered asymptomatic at days 0-30. Five PASC symptom clusters emerged and specific symptoms at days 0-30 were associated with PASC. Women were more likely than men to develop PASC, with all age groups and ethnicities represented. PASC is a public health priority.

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