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Hospital-Level Variation in Death for Critically Ill Patients with COVID-19.
Churpek, Matthew M; Gupta, Shruti; Spicer, Alexandra B; Parker, William F; Fahrenbach, John; Brenner, Samantha K; Leaf, David E.
  • Churpek MM; University of Wisconsin Madison, 5228, Medicine; Division of Pulmonary and Critical Care, Madison, Wisconsin, United States; mchurpek@medicine.wisc.edu.
  • Gupta S; Brigham and Women's Hospital Department of Medicine, 370908, Division of Renal Medicine, Boston, Massachusetts, United States.
  • Spicer AB; University of Wisconsin-Madison, 5228, Medicine; Division of Pulmonary and Critical Care, Madison, Wisconsin, United States.
  • Parker WF; The University of Chicago, 2462, Department of Medicine, Chicago, Illinois, United States.
  • Fahrenbach J; The University of Chicago, 2462, Department of Medicine, Chicago, Illinois, United States.
  • Brenner SK; Hackensack University Medical Center, 3673, Internal Medicine, Hackensack, New Jersey, United States.
  • Leaf DE; Brigham and Women's Hospital, 1861, Division of Renal Medicine, Boston, Massachusetts, United States.
Am J Respir Crit Care Med ; 204(403-411)2021 08 15.
Article in English | MEDLINE | ID: covidwho-1199842
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 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/).
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Algorithms / Critical Illness / COVID-19 / Intensive Care Units Type of study: Cohort study / Observational study / Prognostic study Limits: Aged / Female / Humans / Male / Middle aged Country/Region as subject: North America Language: English Journal subject: Critical Care Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Algorithms / Critical Illness / COVID-19 / Intensive Care Units Type of study: Cohort study / Observational study / Prognostic study Limits: Aged / Female / Humans / Male / Middle aged Country/Region as subject: North America Language: English Journal subject: Critical Care Year: 2021 Document Type: Article