OBJECTIVE: To derive and validate the health-related quality of life comorbidity index (HRQL-CI). STUDY DESIGN AND SETTING: Of 261 clinical classification codes (CCCs) in the 2003 Medical Expenditure Panel Survey (MEPS), 44 were identified as adult, gender-neutral, chronic conditions. The least absolute shrinkage and selection operator (LASSO) procedure identified CCCs significantly associated with the Short Form-12 physical component summary (PCS) and mental component summary (MCS) scores. Regression models were fitted with the selected CCCs, resulting in two subsets corresponding to PCS and MCS, collectively called the HRQL-CI. Internal validation was assessed using 10-fold cross-validation, whereas external validation in terms of prediction accuracy was assessed in the 2005 MEPS database. Prediction errors and model R² were compared between HRQL-CI models and models using the Charlson-CI. RESULTS: LASSO identified 20 CCCs significantly associated with PCS and 15 with MCS. The R² for the models, including the HRQL-CI (0.28 for PCS and 0.16 for MCS) were greater than those using the Charlson-CI (0.13 for PCS and 0.01 for MCS). The same pattern of higher R² for models using the HRQL-CI was observed in the validation tests. CONCLUSION: The HRQL-CI is a valid risk adjustment index, outperforming the Charlson-CI. Further work is needed to test its performance in other patient populations and measures of HRQL. Copyright Â
OBJECTIVE: To derive and validate the health-related quality of life comorbidity index (HRQL-CI). STUDY DESIGN AND SETTING: Of 261 clinical classification codes (CCCs) in the 2003 Medical Expenditure Panel Survey (MEPS), 44 were identified as adult, gender-neutral, chronic conditions. The least absolute shrinkage and selection operator (LASSO) procedure identified CCCs significantly associated with the Short Form-12 physical component summary (PCS) and mental component summary (MCS) scores. Regression models were fitted with the selected CCCs, resulting in two subsets corresponding to PCS and MCS, collectively called the HRQL-CI. Internal validation was assessed using 10-fold cross-validation, whereas external validation in terms of prediction accuracy was assessed in the 2005 MEPS database. Prediction errors and model R² were compared between HRQL-CI models and models using the Charlson-CI. RESULTS: LASSO identified 20 CCCs significantly associated with PCS and 15 with MCS. The R² for the models, including the HRQL-CI (0.28 for PCS and 0.16 for MCS) were greater than those using the Charlson-CI (0.13 for PCS and 0.01 for MCS). The same pattern of higher R² for models using the HRQL-CI was observed in the validation tests. CONCLUSION: The HRQL-CI is a valid risk adjustment index, outperforming the Charlson-CI. Further work is needed to test its performance in other patient populations and measures of HRQL. Copyright Â
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