Comparison of the performance of the CMS Hierarchical Condition Category (CMS-HCC) risk adjuster with the Charlson and Elixhauser comorbidity measures in predicting mortality

• Published in: BMC health services research Vol. 10; no. 1; p. 245
• Main Authors: Li, Pengxiang, Kim, Michelle M, Doshi, Jalpa A
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 20.08.2010; BioMed Central; BMC
• Subjects: Beneficiaries; Capitation; Centers for Medicare and Medicaid Services (U.S.); Chronic illnesses; Codes; Comorbidity; Demographic aspects; Diabetes; Disease; Female; Forecasting; Health care policy; Health risk assessment; Hospital Mortality - trends; Hospitalization; Hospitals; Humans; Male; Medicaid; Medicare; Methods; Mortality; Patients; Risk Adjustment - methods; Risk Adjustment - standards; Risk factors; Stroke; Studies; United States; United States
• ISSN: 1472-6963; 1472-6963
• DOI: 10.1186/1472-6963-10-245

The Centers for Medicare and Medicaid Services (CMS) has implemented the CMS-Hierarchical Condition Category (CMS-HCC) model to risk adjust Medicare capitation payments. This study intends to assess the performance of the CMS-HCC risk adjustment method and to compare it to the Charlson and Elixhauser comorbidity measures in predicting in-hospital and six-month mortality in Medicare beneficiaries. The study used the 2005-2006 Chronic Condition Data Warehouse (CCW) 5% Medicare files. The primary study sample included all community-dwelling fee-for-service Medicare beneficiaries with a hospital admission between January 1st, 2006 and June 30th, 2006. Additionally, four disease-specific samples consisting of subgroups of patients with principal diagnoses of congestive heart failure (CHF), stroke, diabetes mellitus (DM), and acute myocardial infarction (AMI) were also selected. Four analytic files were generated for each sample by extracting inpatient and/or outpatient claims for each patient. Logistic regressions were used to compare the methods. Model performance was assessed using the c-statistic, the Akaike's information criterion (AIC), the Bayesian information criterion (BIC) and their 95% confidence intervals estimated using bootstrapping. The CMS-HCC had statistically significant higher c-statistic and lower AIC and BIC values than the Charlson and Elixhauser methods in predicting in-hospital and six-month mortality across all samples in analytic files that included claims from the index hospitalization. Exclusion of claims for the index hospitalization generally led to drops in model performance across all methods with the highest drops for the CMS-HCC method. However, the CMS-HCC still performed as well or better than the other two methods. The CMS-HCC method demonstrated better performance relative to the Charlson and Elixhauser methods in predicting in-hospital and six-month mortality. The CMS-HCC model is preferred over the Charlson and Elixhauser methods if information about the patient's diagnoses prior to the index hospitalization is available and used to code the risk adjusters. However, caution should be exercised in studies evaluating inpatient processes of care and where data on pre-index admission diagnoses are unavailable.