Comparison of risk-adjustment methods to predict in-hospital mortality among emergency department patients admitted to critical care settings

Elixhauser and APACHE risk-adjustment methods are widely used in studies of administrative data but have not been directly compared in the same dataset. This was an 18-month cross-sectional, observational, registry-based study of all adult patients admitted from the emergency department to an inpatient critical care setting at one academic referral center and one community hospital. After extracting demographics, comorbidities from the preceding year, and calculating APACHE-IV scores at admission, we developed three competing logistic regression models to predict in-hospital mortality using APACHE-IV, Elixhauser comorbidities, and a combined approach. We identified 8,480 critical care admissions during the study period (84% at the academic hospital), with 12.6% overall mortality. Areas under the curve were 0.808 [95% confidence interval (CI): 0.777, 0.831] for APACHE-IV, 0.736 (95% CI: 0.715, 0.761) for Elixhauser, and 0.826 (95% CI: 0.803, 0.852) for the combined model. APACHE-IV scoring, with or without the addition of Elixhauser comorbidity measures, outperformed the Elixhauser method alone in predicting in-hospital mortality among patients admitted to a critical care unit via the emergency department. Observational studies in this population should consider employing APACHE-based risk adjustment for short-term mortality prediction, in preference to comorbidity-based risk-adjustment alone.