Abstract
BACKGROUND AND OBJECTIVES: The Elixhauser Comorbidity Model is a prominent, freely-available risk adjustment model which performs well in predicting outcomes of inpatient care. However, because it relies solely on diagnosis codes, it may not capture the full extent of patient complexity. Our objective was to enhance and validatethe Elixhauser Model by incorporating additional clinical and demographic data to improve the accuracy of outcome prediction. METHODS: This retrospective observational cohort study included 55,945 admissions to the internal medicine service of a large tertiary care hospital in Jerusalem. A model was derived and validated to predict four primary outcomes. The four primary outcomes measured were length of stay (LOS), in-hospital mortality, readmission within 30 days, and increased care. RESULTS: Initially, the Elixhauser Model was applied using standard Elixhauser definitions based on diagnosis codes. Subsequently, clinical variables such as laboratory test results, vital signs, and demographic information were added to the model. The expanded models demonstrated improved prediction compared to the baseline model. For example, the R(2) for log LOS improved from 0.101 to 0.281 and the c-statistic to predict in-hospital mortality improved from 0.711 to 0.879. CONCLUSIONS: Adding readily available clinical and demographic data to the base Elixhauser model improves outcome prediction by a considerable margin. This enhanced model provides a more comprehensive representation of patients' health status. It could be utilized to support decisions regarding admission and to what setting, determine suitability for home hospitalization, and facilitate differential payment adjustments based on patient complexity.