The XGBoost machine learning model predicted long-term adverse events in PCI-HBR patients with high accuracy, achieving an AUC of 0.85.
Can a machine learning-based predictive model accurately predict long-term net adverse clinical events in high bleeding risk patients undergoing percutaneous coronary intervention?
An XGBoost machine learning model accurately predicts long-term net adverse clinical events (AUC 0.85) in high bleeding risk patients undergoing PCI, offering a tailored risk assessment tool.
Tasa de eventos absoluta: 0% vs 0%
Abstract Background Patients classified as high bleeding risk (HBR) undergoing percutaneous coronary intervention (PCI) face a significantly higher incidence of net adverse clinical events (NACE) compared to non-HBR patients. Existing risk assessment models, such as the CRUSADE and TIMI scores, do not adequately address the unique risks faced by the HBR population. There is an urgent need for a precise and comprehensive predictive model tailored to PCI-HBR patients to guide clinical decision-making and improve patient outcomes. Methods This study aimed to develop a machine learning-based predictive model for long-term NACE in PCI-HBR patients. We utilized data from the Prognostic Analysis and an Appropriate Antiplatelet Strategy for Patients with Percutaneous Coronary Intervention and High Bleeding Risk (PPP-PCI) registry database. Feature selection and interpretation were performed using a SHAP (SHapley Additive exPlanations) model based on Recursive Feature Elimination (RFE). Model construction and evaluation were conducted using four algorithms: logistic regression, random forest, gradient boosting, and XGBoost. Results A total of 1512 PCI-HBR patients were included in the study. The XGBoost model demonstrated the highest predictive performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.85. The SHAP model identified 24 significant variables contributing to the prediction of NACE, including clinical parameters, laboratory findings, and echocardiographic data. Conclusions Our machine learning-based model offers a promising tool for predicting long-term NACE in PCI-HBR patients. The model’s high predictive accuracy and interpretability have the potential to enhance clinical decision-making and improve patient care. Further validation in larger, diverse populations is warranted to confirm these findings.SHAP summary plot. AUC plot.
Zhang et al. (Sat,) reported a other. The XGBoost machine learning model predicted long-term adverse events in PCI-HBR patients with high accuracy, achieving an AUC of 0.85.