Hospital-Level Variation in Death for Critically Ill Patients with COVID-19
RATIONALE: Variation in hospital mortality has been described for coronavirus disease 2019 (COVID-19), but the factors that explain these differences remain unclear.
OBJECTIVE: Our objective was to utilize a large, nationally representative dataset of critically ill adults with COVID-19 to determine which factors explain mortality variability.
METHODS: In this multicenter cohort study, we examined adults hospitalized in intensive care units with COVID-19 at 70 United States hospitals between March and June 2020. The primary outcome was 28-day mortality. We examined patient-level and hospital-level variables. Mixed-effects logistic regression was used to identify factors associated with interhospital variation. The median odds ratio (OR) was calculated to compare outcomes in higher- vs. lower-mortality hospitals. A gradient boosted machine algorithm was developed for individual-level mortality models.
MEASUREMENTS AND MAIN RESULTS: A total of 4,019 patients were included, 1537 (38%) of whom died by 28 days. Mortality varied considerably across hospitals (0-82%). After adjustment for patient- and hospital-level domains, interhospital variation was attenuated (OR decline from 2.06 [95% CI, 1.73-2.37] to 1.22 [95% CI, 1.00-1.38]), with the greatest changes occurring with adjustment for acute physiology, socioeconomic status, and strain. For individual patients, the relative contribution of each domain to mortality risk was: acute physiology (49%), demographics and comorbidities (20%), socioeconomic status (12%), strain (9%), hospital quality (8%), and treatments (3%).
CONCLUSION: There is considerable interhospital variation in mortality for critically ill patients with COVID-19, which is mostly explained by hospital-level socioeconomic status, strain, and acute physiologic differences. Individual mortality is driven mostly by patient-level factors. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).
| Errataetall: |
CommentIn: Am J Respir Crit Care Med. 2021 Aug 15;204(4):381-382. - PMID 34139143 |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:204 |
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| Enthalten in: |
American journal of respiratory and critical care medicine - 204(2021), 403-411 vom: 15. Aug. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Churpek, Matthew M [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 |
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| Anmerkungen: |
Date Completed 16.12.2021 Date Revised 03.04.2024 published: Print CommentIn: Am J Respir Crit Care Med. 2021 Aug 15;204(4):381-382. - PMID 34139143 Citation Status MEDLINE |
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| doi: |
10.1164/rccm.202012-4547OC |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM324451830 |
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| 100 | 1 | |a Churpek, Matthew M |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Hospital-Level Variation in Death for Critically Ill Patients with COVID-19 |
| 264 | 1 | |c 2021 | |
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| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
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| 500 | |a Date Completed 16.12.2021 | ||
| 500 | |a Date Revised 03.04.2024 | ||
| 500 | |a published: Print | ||
| 500 | |a CommentIn: Am J Respir Crit Care Med. 2021 Aug 15;204(4):381-382. - PMID 34139143 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a RATIONALE: Variation in hospital mortality has been described for coronavirus disease 2019 (COVID-19), but the factors that explain these differences remain unclear | ||
| 520 | |a OBJECTIVE: Our objective was to utilize a large, nationally representative dataset of critically ill adults with COVID-19 to determine which factors explain mortality variability | ||
| 520 | |a METHODS: In this multicenter cohort study, we examined adults hospitalized in intensive care units with COVID-19 at 70 United States hospitals between March and June 2020. The primary outcome was 28-day mortality. We examined patient-level and hospital-level variables. Mixed-effects logistic regression was used to identify factors associated with interhospital variation. The median odds ratio (OR) was calculated to compare outcomes in higher- vs. lower-mortality hospitals. A gradient boosted machine algorithm was developed for individual-level mortality models | ||
| 520 | |a MEASUREMENTS AND MAIN RESULTS: A total of 4,019 patients were included, 1537 (38%) of whom died by 28 days. Mortality varied considerably across hospitals (0-82%). After adjustment for patient- and hospital-level domains, interhospital variation was attenuated (OR decline from 2.06 [95% CI, 1.73-2.37] to 1.22 [95% CI, 1.00-1.38]), with the greatest changes occurring with adjustment for acute physiology, socioeconomic status, and strain. For individual patients, the relative contribution of each domain to mortality risk was: acute physiology (49%), demographics and comorbidities (20%), socioeconomic status (12%), strain (9%), hospital quality (8%), and treatments (3%) | ||
| 520 | |a CONCLUSION: There is considerable interhospital variation in mortality for critically ill patients with COVID-19, which is mostly explained by hospital-level socioeconomic status, strain, and acute physiologic differences. Individual mortality is driven mostly by patient-level factors. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/) | ||
| 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 Critical Care | |
| 650 | 4 | |a Health Disparities | |
| 650 | 4 | |a Intensive Care Unit | |
| 700 | 1 | |a Gupta, Shruti |e verfasserin |4 aut | |
| 700 | 1 | |a Spicer, Alexandra B |e verfasserin |4 aut | |
| 700 | 1 | |a Parker, William F |e verfasserin |4 aut | |
| 700 | 1 | |a Fahrenbach, John |e verfasserin |4 aut | |
| 700 | 1 | |a Brenner, Samantha K |e verfasserin |4 aut | |
| 700 | 1 | |a Leaf, David E |e verfasserin |4 aut | |
| 700 | 0 | |a STOP-COVID Investigators |e verfasserin |4 aut | |
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| 700 | 1 | |a McCullough, Peter A. |e investigator |4 oth | |
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| 700 | 1 | |a Shaefi, Shahzad |e investigator |4 oth | |
| 700 | 1 | |a Krajewski, Megan L. |e investigator |4 oth | |
| 700 | 1 | |a Shankar, Sidharth |e investigator |4 oth | |
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