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

Figures

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  Central Figure.

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  Figure 1.. Study flowchart for machine learning (ML) model development and validation. Patient selection and ML workflow from the CLIDAS database for bleeding risk prediction model development. The cohort was divided into derivation and internal validation sets, followed by feature selection, model training, and external validation using the HCEI database. CLIDAS, Clinical Deep Data Accumulation System; HCEI, Health, Clinic, and Education Information Evaluation Institute; PCI, percutaneous coronary intervention; SMOTE-NC, Synthetic Minority Over-sampling Technique for Nominal and Continuous.

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  Figure 2.. Variable selection and performance evaluation of machine learning algorithms for bleeding risk prediction. (A) Feature importance of four variables selected by the Boruta algorithm. (B) ROC curves showing discriminative performance of five algorithms. Logistic regression (AUC 0.748) and random forest (AUC 0.744) demonstrated the highest discriminative performance. (C) Performance metrics comparison. AUC, area under the curve; ROC, receiver operating characteristic.

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  Figure 3.. ROC-AUC of risk scores in the external validation cohort. ROC curves comparing the CLIDAS bleeding risk score with conventional risk scores in the external validation cohort (n=10,928 patients from the HCEI database). The CLIDAS score showed limited discrimination in the external validation cohort (AUC 0.64), with lower AUC values than the continuous J-HBR and ARC-HBR scores (AUC 0.67 and 0.68, respectively), although no statistically significant differences were observed by DeLong test. ARC-HBR, Academic Research Consortium for High Bleeding Risk; AUC, area under the curve; CLIDAS, Clinical Deep Data Accumulation System; HCEI, Health, Clinic, and Education Information Evaluation Institute; J-HBR, Japan-High Bleeding Risk; ROC, receiver operating characteristic.