A comparison of comorbidity measurements to predict healthcare expenditures

Objective: To compare the performance of the Elixhauser, Charlson, and RxRisk-V comorbidity indices and several simple count measurements, including counts of prescriptions, physician visits, hospital claims, unique prescription classes, and diagnosis clusters. Study Design: Each measurement was calculated using claims data during a 1-year period before the initial filling of an antihypertensive medication among 20 378 members of a managed care organization. The primary outcome variable was the log-transformed sum of prescription, physician, and hospital expenditures in the year following the prescription encounter. Methods: In addition to descriptive statistics and Spearman rank correlations between measurements, the predictive performance was determined using linear regression models and corresponding adjusted R² statistics. Results: The Charlson index and the Elixhauser index performed similarly (adjusted R² = 0.11 72 and 0.1148, respectively), while the prescription claims-based RxRisk-V (adjusted R² = 0.1573) outperformed both. An age- and gender-adjusted regression model that included a count of diagnosis clusters was the best individual predictor of payments (adjusted R² = 0.1814). This outperformed age- and gender-adjusted models of the number of unique prescriptions filled (adjusted R² = 0.1669), number of prescriptions filled (R² = 0.1573), number of physician visits (adjusted R² = 0.1546), log-transformed prior healthcare payments (adjusted R² = 0.1359), and number of hospital claims (adjusted R² = 0.1115). Conclusion: Simple count measurements appear to be better predictors of future expenditures than the comorbidity indices, with a count of diagnosis clusters being the single best predictor of future expenditures among the measurements examined.