Risk prediction models in patients undergoing percutaneous coronary intervention: A collaborative analysis from a Japanese administrative dataset and nationwide academic procedure registry

Abstract

BackgroundContemporary guidelines emphasize the importance of risk stratification in improving the quality of care for patients undergoing percutaneous coronary intervention (PCI). We aimed to investigate whether adding information from a procedure-based academic registry to administrative claims data would improve the performance of risk prediction model. MethodsWe combined two nationally representative administrative and clinical databases. The study cohort comprised 43,095 patients; 18,719 and 23, 525 with acute [ACS] and chronic [CCS] coronary syndrome, respectively. Each population was randomly divided into the logistic regression model (derivation cohort, 80%) and model validation (validation cohort, 20%) groups. The performances of the following models were compared using C-statistics: (1) variables restricted to baseline claims data (model #1), (2) clinical registry data (model #2), and (3) expanded to both claims and clinical registry data (model #3). The primary outcomes were in-hospital mortality and bleeding. ResultsThe primary outcomes occurred in 3.7% (in-hospital mortality)/5.0% (bleeding) of patients with ACS and 0.21%/0.95% of CCS patients. For each event, the model performance was 0.65 (95% confidence interval [CI], 0.60–0.69) /0.67 (0.63–0.71) in ACS and 0.52 (0.35–0.76) /0.62 (0.54–0.70) for CCS patients in model #1, 0.83 (0.80–0.87) /0.77 (0.74–0.81) in ACS and 0.76 (0.60–0.92) /0.67 (0.59–0.75) in CCS for model #2, and 0.83 (0.79–0.86) /0.78 (0.75–0.81) in ACS and 0.76 (0.61–0.92) /0.67 (0.58–0.74) in CCS for model #3. ConclusionsCombining clinical information from the academic registry with claims databases improved its performance in predicting adverse events.