To see if changes in the demographics and illness burden of Medicare patients hospitalized for acute myocardial infarction (AMI) from 1995 through 1999 can explain an observed rise (from 32 percent to 34 percent) in one-year mortality over that period.
Data Sources. Utilization data from the Centers for Medicare and Medicaid Services (CMS) fee-for-service claims (MedPAR, Outpatient, and Carrier Standard Analytic Files); patient demographics and date of death from CMS Denominator and Vital Status files. For over 1.5 million AMI discharges in 1995–1999 we retain diagnoses from one year prior, and during, the case-defining admission.
Study Design. We fit logistic regression models to predict one-year mortality for the 1995 cases and apply them to 1996–1999 files. The CORE model uses age, sex, and original reason for Medicare entitlement to predict mortality. Three other models use the CORE variables plus morbidity indicators from well-known morbidity classification methods (Charlson, DCG, and AHRQ's CCS). Regressions were used as is—without pruning to eliminate clinical or statistical anomalies. Each model references the same diagnoses—those recorded during the pre- and index admission periods. We compare each model's ability to predict mortality and use each to calculate risk-adjusted mortality in 1996–1999.
Principal Findings. The comprehensive morbidity classifications (DCG and CCS) led to more accurate predictions than the Charlson, which dominated the CORE model (validated C-statistics: 0.81, 0.82, 0.74, and 0.66, respectively). Using the CORE model for risk adjustment reduced, but did not eliminate, the mortality increase. In contrast, adjustment using any of the morbidity models produced essentially flat graphs.
Conclusions. Prediction models based on claims-derived demographics and morbidity profiles can be extremely accurate. While one-year post-AMI mortality in Medicare may not be worsening, outcomes appear not to have continued to improve as they had in the prior decade. Rich morbidity information is available in claims data, especially when longitudinally tracked across multiple settings of care, and is important in setting performance targets and evaluating trends.