Risk prediction models in patients undergoing percutaneous coronary intervention: A collaborative analysis from a Japanese administrative dataset and nationwide academic procedure registry. (1st January 2023)

• Record Type: Journal Article
• Authors: Shoji, Satoshi; Kohsaka, Shun; Kumamaru, Hiraku; Nishimura, Shiori; Ishii, Hideki; Amano, Tetsuya; Fushimi, Kiyohide; Miyata, Hiroaki; Ikari, Yuji
• Is Part Of: International journal of cardiology. Volume 370(2023)
• Page Start: 90
• Page End: 97
• Publication Date: 2023-01-01
• Subjects: Percutaneous coronary intervention -- Risk model -- Administrative claims data -- Nationwide registry -- C-statistics; ACS acute coronary syndrome -- CCS chronic coronary syndrome -- CKD chronic kidney disease -- CVIT the Japanese Association of Cardiovascular Intervention and Therapeutics -- DM diabetes mellitus -- DPC Diagnosis Procedure Combination -- HF heart failure -- ICD International Classification of Disease -- J-PCI Japanese PCI registry -- PCI percutaneous coronary intervention.; Cardiology -- Periodicals; Electronic journals; 616.12
• DOI: 10.1016/j.ijcard.2022.10.144
• Languages: English
• ISSNs: 0167-5273

Abstract

Abstract: 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. Highlights: The claims + registry-based risk models outperformed claims-based only risk models in PCI population. Severity information such as cardiogenic shock only in the registry would be required for further risk stratification. Combining claims and registries provide clinically relevant stratification schemes.