Open AccessIdentification of Phenotypes Among COVID-19 Patients in the United States Using Latent Class Analysis
Author(s) -
Catherine Teng,
Unnikrishna Thampy,
Ju Young Bae,
Ping Cai,
Richard Dixon,
Qi Liu,
Pengyang Li
Publication year - 2021
Publication title -
infection and drug resistance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.033
H-Index - 39
ISSN - 1178-6973
DOI - 10.2147/idr.s331907
Subject(s) - medicine , cohort , latent class model , comorbidity , covid-19 , cluster (spacecraft) , phenotype , disease , pediatrics , infectious disease (medical specialty) , biology , statistics , mathematics , computer science , programming language , biochemistry , gene
Coronavirus disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 or COVID-19) is a heterogeneous disorder with a complex pathogenesis. Recent studies from Spain and France have indicated that underlying phenotypes may exist among patients admitted to the hospital with COVID-19. Whether those same phenotypes exist in the United States (US) remains unclear. Using latent class analysis (LCA), we sought to determine whether clinical phenotypes exist among patients admitted for COVID-19.