Hospital-Level Variation in Death for Critically Ill Patients with COVID-19

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/).

Keywords: COVID-19; Critical Care; Health Disparities; Intensive Care Unit.

Figures

Figure 1.

Case mix–adjusted probabilities of 28-day mortality. The graphs illustrate the change in interhospital variation in death as each domain is added to the unadjusted mixed-effect model (leftmost panel) and end with the fully adjusted model (rightmost panel), which shows that most of the variation in mortality across hospitals can be explained by the domains included. The x-axis is hospital ranked by increasing probability of death in 28 days, and the y-axis shows the case mix–adjusted probability of death in the mixed-effect regression model, with the red dots denoting the point estimates and the whiskers denoting the 95% confidence intervals. The median OR and range in mortality are presented for each model. Demo = demographics; OR = odds ratio; SES = socioeconomic status.

Figure 2.

Contributions to 28-day mortality risk based on Shapley values. The figure illustrates the relative contribution of all variables in each domain based on Shapley values calculated from the XGBoost machine learning model (red bars; left y-axis). The cumulative contribution of the domains, moving from left to right in the figure, is shown with the line plot (right y-axis). Demo = demographics; SES = socioeconomic status.