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

Yong Huang, Melissa D. Pinto, Jessica L. Borelli, Milad Asgari Mehrabadi, Heather L. Abrahim, Nikil Dutt, Natalie Lambert, Erika L. Nurmi, Rana Chakraborty, Amir M. Rahmani and Charles A. Downs

Clinical Nursing Research, 2022, vol. 31, issue 8, 1390-1398

Abstract: 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.

Keywords: COVID-19; long-COVID; electronic health record; machine learning (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:clnure:v:31:y:2022:i:8:p:1390-1398

DOI: 10.1177/10547738221125632

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