COVID Symptoms, Symptom Clusters, and Predictors for Becoming a Long-Hauler Looking for Clarity in the Haze of the Pandemic. (November 2022)

• Record Type: Journal Article
• Authors: Huang, Yong; Pinto, Melissa D.; Borelli, Jessica L.; Asgari Mehrabadi, Milad; Abrahim, Heather L.; Dutt, Nikil; Lambert, Natalie; Nurmi, Erika L.; Chakraborty, Rana; Rahmani, Amir M.; Downs, Charles A.
• Is Part Of: Clinical nursing research. Volume 31:Number 8(2022)
• Page Start: 1390
• Page End: 1398
• Publication Date: 2022-11
• Subjects: COVID-19 -- long-COVID -- electronic health record -- machine learning; Nursing -- Research -- Periodicals; Nursing -- Periodicals; Clinical medicine -- Research -- Periodicals; 610.73
• DOI: 10.1177/10547738221125632
• Languages: English
• ISSNs: 1054-7738

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.