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

Background: Contemporary 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. Methods: We 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. Results: The 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. Conclusions: Combining clinical information from the academic registry with claims databases improved its performance in predicting adverse events.