Characterizing COVID-19 clinical phenotypes and associated comorbidities and complication profiles
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 (HR:7.30, 95% CI:(3.11-17.17), p<0.001) and 2.57-fold (HR:2.57, 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.
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:16 |
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| Enthalten in: |
PloS one - 16(2021), 3 vom: 31., Seite e0248956 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lusczek, Elizabeth R [VerfasserIn] |
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Date Completed 09.04.2021 Date Revised 02.11.2023 published: Electronic-eCollection UpdateOf: medRxiv. 2020 Sep 14;:. - PMID 32995813 Citation Status MEDLINE |
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| doi: |
10.1371/journal.pone.0248956 |
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| PPN (Katalog-ID): |
NLM323445810 |
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| 100 | 1 | |a Lusczek, Elizabeth R |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Characterizing COVID-19 clinical phenotypes and associated comorbidities and complication profiles |
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| 500 | |a Date Revised 02.11.2023 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a UpdateOf: medRxiv. 2020 Sep 14;:. - PMID 32995813 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 (HR:7.30, 95% CI:(3.11-17.17), p<0.001) and 2.57-fold (HR:2.57, 95% CI:(1.10-6.00), p = 0.03) increases in hazard of death relative to phenotype III | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 700 | 1 | |a Ingraham, Nicholas E |e verfasserin |4 aut | |
| 700 | 1 | |a Karam, Basil S |e verfasserin |4 aut | |
| 700 | 1 | |a Proper, Jennifer |e verfasserin |4 aut | |
| 700 | 1 | |a Siegel, Lianne |e verfasserin |4 aut | |
| 700 | 1 | |a Helgeson, Erika S |e verfasserin |4 aut | |
| 700 | 1 | |a Lotfi-Emran, Sahar |e verfasserin |4 aut | |
| 700 | 1 | |a Zolfaghari, Emily J |e verfasserin |4 aut | |
| 700 | 1 | |a Jones, Emma |e verfasserin |4 aut | |
| 700 | 1 | |a Usher, Michael G |e verfasserin |4 aut | |
| 700 | 1 | |a Chipman, Jeffrey G |e verfasserin |4 aut | |
| 700 | 1 | |a Dudley, R Adams |e verfasserin |4 aut | |
| 700 | 1 | |a Benson, Bradley |e verfasserin |4 aut | |
| 700 | 1 | |a Melton, Genevieve B |e verfasserin |4 aut | |
| 700 | 1 | |a Charles, Anthony |e verfasserin |4 aut | |
| 700 | 1 | |a Lupei, Monica I |e verfasserin |4 aut | |
| 700 | 1 | |a Tignanelli, Christopher J |e verfasserin |4 aut | |
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