Prediction of long-term mortality after percutaneous coronary intervention in older adults: results from the National Cardiovascular Data Registry.

BACKGROUND: The purpose of this study was to develop a long-term model to predict mortality after percutaneous coronary intervention in both patients with ST-segment elevation myocardial infarction and those with more stable coronary disease. METHODS AND RESULTS: The American College of Cardiology Foundation CathPCI Registry data were linked to the Centers for Medicare and Medicaid Services 100% denominator file by probabilistic matching. Preprocedure demographic and clinical variables from the CathPCI Registry were used to predict the probability of death over 3 years as recorded in the Centers for Medicare and Medicaid Services database. Between 2004 and 2007, 343 466 patients (66%) of 518 195 patients aged ≥65 years undergoing first percutaneous coronary intervention in the CathPCI Registry were successfully linked to Centers for Medicare and Medicaid Services data. This study population was randomly divided into 60% derivation and 40% validation cohorts. Median follow-up was 15 months, with mortality of 3.0% at 30 days and 8.7%, 13.4%, and 18.7% at 1, 2, and 3 years, respectively. Twenty-four characteristics related to demographics, clinical comorbidity, prior history of disease, and indices of disease severity and acuity were identified as being associated with mortality. The C indices in the validation cohorts for patients with and without ST-segment elevation myocardial infarction were 0.79 and 0.78. The model calibrated well across a wide range of predicted probabilities. CONCLUSIONS: On the basis of the large and nationally representative CathPCI Registry, we have developed a model that has excellent discrimination, calibration, and validation to predict survival up to 3 years after percutaneous coronary intervention.

Duke Scholars

Author Elizabeth Ray DeLong Biostatistics & Bioinformatics, Division of Biostatistics

Author Sean Michael O'Brien Biostatistics & Bioinformatics, Division of Biostatistics