Key result
An XGBoost machine learning model predicted long-term net adverse clinical events in high bleeding risk patients undergoing PCI with an area under the receiver operating characteristic curve of 0.85.
Why the study?
Existing risk scores such as CRUSADE and TIMI do not adequately address the unique risks of net adverse clinical events in patients with high bleeding risk undergoing PCI.
Can a machine learning-based predictive model accurately predict long-term net adverse clinical events in high bleeding risk patients undergoing PCI?
Cohort (n=1,512)
Can a machine learning-based predictive model accurately predict long-term net adverse clinical events in high bleeding risk patients undergoing PCI?
Effect estimate: AUC 0.85
An XGBoost machine learning model accurately predicts long-term net adverse clinical events in high bleeding risk patients undergoing PCI, potentially improving upon traditional risk scores.
ML model accurately predicts long-term NACE in PCI-HBR; hypothesis-generating and requires prospective validation before clinical use.
BACKGROUND: Patients classified as having a high bleeding risk (HBR) and undergoing percutaneous coronary intervention (PCI) face a significantly greater incidence of net adverse clinical events (NACEs) than non-HBR patients do. 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 registry database. Feature selection and interpretation were performed via a SHapley Additive exPlanations (SHAP) model based on recursive feature elimination. Model construction and evaluation were conducted via 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 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.
No takes yet. Share an insight, caveat, or question.
Lei et al. (2025) conducted a cohort in High bleeding risk undergoing percutaneous coronary intervention (n=1,512). XGBoost machine learning predictive model was evaluated on Long-term net adverse clinical events (NACE) (AUC 0.85). An XGBoost machine learning model predicted long-term net adverse clinical events in high bleeding risk patients undergoing PCI with an area under the receiver operating characteristic curve of 0.85.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: