A combined comorbidity score predicted mortality in elderly patients better than existing scores.

摘要

OBJECTIVE: To develop and validate a single numerical comorbidity score for predicting short- and long-term mortality, by combining conditions in the Charlson and Elixhauser measures. STUDY DESIGN AND SETTING: In a cohort of 120,679 Pennsylvania Medicare enrollees with drug coverage through a pharmacy assistance program, we developed a single numerical comorbidity score for predicting 1-year mortality, by combining the conditions in the Charlson and Elixhauser measures. We externally validated the combined score in a cohort of New Jersey Medicare enrollees, by comparing its performance to that of both component scores in predicting 1-year mortality, as well as 180-, 90-, and 30-day mortality. RESULTS: C-statistics from logistic regression models including the combined score were higher than corresponding c-statistics from models including either the Romano implementation of the Charlson Index or the single numerical version of the Elixhauser system; c-statistics were 0.860 (95% confidence interval [CI]: 0.854, 0.866), 0.839 (95% CI: 0.836, 0.849), and 0.836 (95% CI: 0.834, 0.847), respectively, for the 30-day mortality outcome. The combined comorbidity score also yielded positive values for two recently proposed measures of reclassification. CONCLUSION: In similar populations and data settings, the combined score may offer improvements in comorbidity summarization over existing scores.