Hospital-Level Variation in Death for Critically Ill Patients with COVID-19.
Am J Respir Crit Care Med
; 204(403-411)2021 08 15.
Article
em En
| MEDLINE
| ID: mdl-33891529
ABSTRACT
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 MAINRESULTS:
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/).Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
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Estado Terminal
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COVID-19
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Unidades de Terapia Intensiva
Tipo de estudo:
Etiology_studies
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Incidence_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Aged
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Female
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Humans
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Male
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Middle aged
País como assunto:
America do norte
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article