Development and External Validation of a Machine Learning-Based Bleeding Risk Score for Patients Undergoing Percutaneous Coronary Intervention

Abstract

Background: Conventional bleeding risk scores after percutaneous coronary intervention (PCI) have limited discrimination and external validation, so we developed and validated a machine learning (ML)-based bleeding risk score using real-world data.

Methods and Results: The primary outcome was major bleeding. In the Clinical Deep Data Accumulation System (CLIDAS) cohort, bleeding events occurring >30 days after PCI were identified by chart review and classified as moderate or severe bleeding by the Global Utilization of Streptokinase and TPA for Occluded Coronary Arteries criteria. In the Health, Clinic, and Education Information Evaluation Institute (HCEI) cohort, major bleeding was identified using ICD-10 codes and Diagnosis Procedure Combination (DPC) data. We analyzed 2,502 acute coronary syndrome patients undergoing PCI from CLIDAS and externally validated the model in 10,928 patients from HCEI. A total of 5 ML algorithms were trained. Key predictors were age, body mass index, Btype natriuretic peptide, and hemoglobin. Logistic regression demonstrated the best performance (area under the curve [AUC] 0.748) and was used to derive the CLIDAS bleeding risk score. The CLIDAS score outperformed the binary J-HBR (AUC 0.74 vs. 0.63, P=0.007) and binary ARC-HBR (AUC 0.74 vs. 0.62, P=0.011), but in external validation the CLIDAS score demonstrated limited discrimination (AUC 0.64), lower than continuous conventional scores.

Conclusions: Our novel ML-based bleeding risk score showed better discrimination than conventional binary classifications in the derivation/internal validation, but external performance was limited.