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Characterizing COVID-19 clinical phenotypes and associated comorbidities and complication profiles.
Lusczek, Elizabeth R; Ingraham, Nicholas E; Karam, Basil S; Proper, Jennifer; Siegel, Lianne; Helgeson, Erika S; Lotfi-Emran, Sahar; Zolfaghari, Emily J; Jones, Emma; Usher, Michael G; Chipman, Jeffrey G; Dudley, R Adams; Benson, Bradley; Melton, Genevieve B; Charles, Anthony; Lupei, Monica I; Tignanelli, Christopher J.
  • Lusczek ER; Department of Surgery, University of Minnesota, Minneapolis, MN, United States of America.
  • Ingraham NE; Department of Medicine, Division of Pulmonary and Critical Care, University of Minnesota, Minneapolis, MN, United States of America.
  • Karam BS; Department of Surgery, Medical College of Wisconsin, Milwaukee, WI, United States of America.
  • Proper J; Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, United States of America.
  • Siegel L; Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, United States of America.
  • Helgeson ES; Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, United States of America.
  • Lotfi-Emran S; Department of Medicine, Division of Pulmonary and Critical Care, University of Minnesota, Minneapolis, MN, United States of America.
  • Zolfaghari EJ; University of Minnesota Medical School, Minneapolis, MN, United States of America.
  • Jones E; Department of Surgery, University of Minnesota, Minneapolis, MN, United States of America.
  • Usher MG; Department of Medicine, Division of General Internal Medicine, University of Minnesota, Minneapolis, MN, United States of America.
  • Chipman JG; Department of Surgery, University of Minnesota, Minneapolis, MN, United States of America.
  • Dudley RA; Department of Medicine, Division of Pulmonary and Critical Care, University of Minnesota, Minneapolis, MN, United States of America.
  • Benson B; Institute for Health Informatics, University of Minnesota, Minneapolis, MN, United States of America.
  • Melton GB; Department of Medicine, Division of General Internal Medicine, University of Minnesota, Minneapolis, MN, United States of America.
  • Charles A; Department of Surgery, University of Minnesota, Minneapolis, MN, United States of America.
  • Lupei MI; Institute for Health Informatics, University of Minnesota, Minneapolis, MN, United States of America.
  • Tignanelli CJ; Department of Surgery, University of North Carolina, Chapel Hill, NC, United States of America.
PLoS One ; 16(3): e0248956, 2021.
Article in English | MEDLINE | ID: covidwho-1574916
Preprint
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ABSTRACT

PURPOSE:

Heterogeneity has been observed in outcomes of hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of clinical phenotypes may facilitate tailored therapy and improve outcomes. The purpose of this study is to identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes.

METHODS:

This is a retrospective analysis of COVID-19 patients from March 7, 2020 to August 25, 2020 at 14 U.S. hospitals. Ensemble clustering was performed on 33 variables collected within 72 hours of admission. Principal component analysis was performed to visualize variable contributions to clustering. Multinomial regression models were fit to compare patient comorbidities across phenotypes. Multivariable models were fit to estimate associations between phenotype and in-hospital complications and clinical outcomes.

RESULTS:

The database included 1,022 hospitalized patients with COVID-19. Three clinical phenotypes were identified (I, II, III), with 236 [23.1%] patients in phenotype I, 613 [60%] patients in phenotype II, and 173 [16.9%] patients in phenotype III. Patients with respiratory comorbidities were most commonly phenotype III (p = 0.002), while patients with hematologic, renal, and cardiac (all p<0.001) comorbidities were most commonly phenotype I. Adjusted odds of respiratory, renal, hepatic, metabolic (all p<0.001), and hematological (p = 0.02) complications were highest for phenotype I. Phenotypes I and II were associated with 7.30-fold (HR7.30, 95% CI(3.11-17.17), p<0.001) and 2.57-fold (HR2.57, 95% CI(1.10-6.00), p = 0.03) increases in hazard of death relative to phenotype III.

CONCLUSION:

We identified three clinical COVID-19 phenotypes, reflecting patient populations with different comorbidities, complications, and clinical outcomes. Future research is needed to determine the utility of these phenotypes in clinical practice and trial design.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Phenotype / COVID-19 Type of study: Observational study / Prognostic study / Randomized controlled trials Topics: Long Covid Limits: Aged / Female / Humans / Male / Middle aged Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2021 Document Type: Article Affiliation country: Journal.pone.0248956

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Phenotype / COVID-19 Type of study: Observational study / Prognostic study / Randomized controlled trials Topics: Long Covid Limits: Aged / Female / Humans / Male / Middle aged Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2021 Document Type: Article Affiliation country: Journal.pone.0248956