A distinct symptom pattern emerges for COVID-19 long-haul: a nationwide study

被引：0
作者：
Melissa D. Pinto
Charles A. Downs
Yong Huang
Sarah A. El-Azab
Nathan S. Ramrakhiani
Anthony Barisano
Lu Yu
Kaitlyn Taylor
Alvaro Esperanca
Heather L. Abrahim
Thomas Hughes
Maria Giraldo Herrera
Amir M. Rahamani
Nikil Dutt
Rana Chakraborty
Christian Mendiola
Natalie Lambert
机构：
[1] University of California Irvine,Sue & Bill Gross School of Nursing
[2] University of Miami,Department of Computer Science, Donald Bren School of Information and Computer Science
[3] University of California Irvine,Department of Health Management and Policy
[4] University of Michigan,School of Labor and Employment Relations
[5] Brown University,Lambert Health Lab
[6] University of Illinois at Urbana-Champaign,Electrical and Computer Engineering Department
[7] Indiana University School of Medicine,Division of Infectious Disease, Department of Pediatrics and Adolescent Medicine
[8] Indiana University Purdue University Indianapolis,School of Engineering and Technology
[9] Mayo Clinic,Department of Biostatistics and Health Data Sciences
[10] Indiana University Purdue University Indianapolis,undefined
[11] Indiana University School of Medicine,undefined
来源：
Scientific Reports | / 12卷
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摘要：
Long-haul COVID-19, also called post-acute sequelae of SARS-CoV-2 (PASC), is a new illness caused by SARS-CoV-2 infection and characterized by the persistence of symptoms. The purpose of this cross-sectional study was to identify a distinct and significant temporal pattern of PASC symptoms (symptom type and onset) among a nationwide sample of PASC survivors (n = 5652). The sample was randomly sorted into two independent samples for exploratory (EFA) and confirmatory factor analyses (CFA). Five factors emerged from the EFA: (1) cold and flu-like symptoms, (2) change in smell and/or taste, (3) dyspnea and chest pain, (4) cognitive and visual problems, and (5) cardiac symptoms. The CFA had excellent model fit (x2 = 513.721, df = 207, p < 0.01, TLI = 0.952, CFI = 0.964, RMSEA = 0.024). These findings demonstrate a novel symptom pattern for PASC. These findings can enable nurses in the identification of at-risk patients and facilitate early, systematic symptom management strategies for PASC.
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