Identification of Distinct Clinical Subphenotypes in Critically Ill Patients With COVID-19
Copyright © 2021 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved..
BACKGROUND: Subphenotypes have been identified in patients with sepsis and ARDS and are associated with different outcomes and responses to therapies.
RESEARCH QUESTION: Can unique subphenotypes be identified among critically ill patients with COVID-19?.
STUDY DESIGN AND METHODS: Using data from a multicenter cohort study that enrolled critically ill patients with COVID-19 from 67 hospitals across the United States, we randomly divided centers into discovery and replication cohorts. We used latent class analysis independently in each cohort to identify subphenotypes based on clinical and laboratory variables. We then analyzed the associations of subphenotypes with 28-day mortality.
RESULTS: Latent class analysis identified four subphenotypes (SP) with consistent characteristics across the discovery (45 centers; n = 2,188) and replication (22 centers; n = 1,112) cohorts. SP1 was characterized by shock, acidemia, and multiorgan dysfunction, including acute kidney injury treated with renal replacement therapy. SP2 was characterized by high C-reactive protein, early need for mechanical ventilation, and the highest rate of ARDS. SP3 showed the highest burden of chronic diseases, whereas SP4 demonstrated limited chronic disease burden and mild physiologic abnormalities. Twenty-eight-day mortality in the discovery cohort ranged from 20.6% (SP4) to 52.9% (SP1). Mortality across subphenotypes remained different after adjustment for demographics, comorbidities, organ dysfunction and illness severity, regional and hospital factors. Compared with SP4, the relative risks were as follows: SP1, 1.67 (95% CI, 1.36-2.03); SP2, 1.39 (95% CI, 1.17-1.65); and SP3, 1.39 (95% CI, 1.15-1.67). Findings were similar in the replication cohort.
INTERPRETATION: We identified four subphenotypes of COVID-19 critical illness with distinct patterns of clinical and laboratory characteristics, comorbidity burden, and mortality.
| Errataetall: | |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:160 |
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| Enthalten in: |
Chest - 160(2021), 3 vom: 01. Sept., Seite 929-943 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Vasquez, Charles R [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 |
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| Anmerkungen: |
Date Completed 13.09.2021 Date Revised 29.10.2022 published: Print-Electronic CommentIn: Chest. 2021 Sep;160(3):795-796. - PMID 34488952 Citation Status MEDLINE |
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| doi: |
10.1016/j.chest.2021.04.062 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM325159793 |
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| 245 | 1 | 0 | |a Identification of Distinct Clinical Subphenotypes in Critically Ill Patients With COVID-19 |
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| 500 | |a Date Revised 29.10.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a CommentIn: Chest. 2021 Sep;160(3):795-796. - PMID 34488952 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2021 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved. | ||
| 520 | |a BACKGROUND: Subphenotypes have been identified in patients with sepsis and ARDS and are associated with different outcomes and responses to therapies | ||
| 520 | |a RESEARCH QUESTION: Can unique subphenotypes be identified among critically ill patients with COVID-19? | ||
| 520 | |a STUDY DESIGN AND METHODS: Using data from a multicenter cohort study that enrolled critically ill patients with COVID-19 from 67 hospitals across the United States, we randomly divided centers into discovery and replication cohorts. We used latent class analysis independently in each cohort to identify subphenotypes based on clinical and laboratory variables. We then analyzed the associations of subphenotypes with 28-day mortality | ||
| 520 | |a RESULTS: Latent class analysis identified four subphenotypes (SP) with consistent characteristics across the discovery (45 centers; n = 2,188) and replication (22 centers; n = 1,112) cohorts. SP1 was characterized by shock, acidemia, and multiorgan dysfunction, including acute kidney injury treated with renal replacement therapy. SP2 was characterized by high C-reactive protein, early need for mechanical ventilation, and the highest rate of ARDS. SP3 showed the highest burden of chronic diseases, whereas SP4 demonstrated limited chronic disease burden and mild physiologic abnormalities. Twenty-eight-day mortality in the discovery cohort ranged from 20.6% (SP4) to 52.9% (SP1). Mortality across subphenotypes remained different after adjustment for demographics, comorbidities, organ dysfunction and illness severity, regional and hospital factors. Compared with SP4, the relative risks were as follows: SP1, 1.67 (95% CI, 1.36-2.03); SP2, 1.39 (95% CI, 1.17-1.65); and SP3, 1.39 (95% CI, 1.15-1.67). Findings were similar in the replication cohort | ||
| 520 | |a INTERPRETATION: We identified four subphenotypes of COVID-19 critical illness with distinct patterns of clinical and laboratory characteristics, comorbidity burden, and mortality | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Multicenter Study | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a coronavirus | |
| 650 | 4 | |a latent class analysis | |
| 650 | 4 | |a phenotypes | |
| 650 | 4 | |a subphenotypes | |
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