Comparing Charlson and Elixhauser comorbidity indices with different weightings to predict in-hospital mortality: an analysis of national inpatient data
Abstract Background When chronic conditions are associated with outcomes such as mortality, comorbidity measures are essential both to describe patient health status and to adjust for potential confounding. The Charlson and Elixhauser comorbidity indices are well-established for risk adjustment and mortality prediction. Still, as optimal comorbidity weightings remain undetermined. The present study aimed to derive a set of new population-based Elixhauser comorbidity weightings, then to validate and compare their mortality predictivity against those of the Charlson and Elixhauser-based van Walraven weightings estimates in a population-based cohort.Methods Retrospective analysis was conducted with routine Swiss general hospital (102 hospitals) data (2012–2017) for 6.09 million inpatient cases. To derive the population-based weightings for the Elixhauser comorbidity index, we randomly halved the inpatient data and validated the results for Part 1 alongside the established weighting systems used for Part 2. Charlson and van Walraven weightings were applied to Charlson and Elixhauser comorbidity indices. Generalized additive models were weighted and adjusted for age, gender and hospital types.Results Overall, the population-based weights’ c-statistic (0.867, 95% CI: 0.865–0.868) was consistently higher than Elixhauser-van Walraven’s (0.863, 95% CI: 0.862–0.864) and Charlson’s (0.850, 95% CI: 0.849–0.851) in the derivation and validation groups and net reclassification improvement of new weights offers improved predictive performance of 0.4% on the Elixhauser-van Walraven and 6.1% on the Charlson weightings.Conclusions All weightings were validated with the national dataset and the new population-based weightings model improved the prediction of in-hospital mortality. The newly derive weights support patient population-based analysis of health outcomes.