Key result
XGBoost model outperforms PRECISE-DAPT for predicting 6-month post-CABG bleeding risk.
Why the study?
Dual antiplatelet therapy after coronary artery bypass grafting can cause varying bleeding risks, necessitating accurate predictive tools for early identification.
Does an eXtreme gradient boosting (XGB) machine learning model improve the prediction of bleeding events within six months compared to the PRECISE-DAPT score in patients receiving DAPT after isolated CABG?
Cohort (n=561)
No
Does an eXtreme gradient boosting (XGB) machine learning model improve the prediction of bleeding events within six months compared to the PRECISE-DAPT score in patients receiving DAPT after isolated CABG?
p-value: p=0.009
A machine learning model using eXtreme gradient boosting (XGB) provides superior precision and recall for predicting 6-month bleeding risk in post-CABG patients on DAPT compared to the traditional PRECISE-DAPT score.
May refine post-CABG bleeding risk stratification; leaves open prospective validation before clinical adoption.
BACKGROUND: Dual antiplatelet therapy (DAPT) after coronary artery bypass grafting (CABG), although might be protective for ischemic events, can lead to varying degrees of bleeding, resulting in serious clinical events, including death. This study aims to develop accurate and scalable predictive tools for early identification of bleeding risks during the DAPT period post-CABG, comparing them with the PRECISE-DAPT score. METHODS: Clinical data were collected from patients who underwent isolated CABG at Nanjing Drum Tower Hospital between June 2021 and December 2023. The dataset was split into derivation and validation cohorts at a 7:3 ratio. Machine learning models were developed to predict bleeding within six months post-CABG in DAPT patients and tested in a temporal external validation cohort. The SHapley Additive exPlanations method visualized variable importance regarding outcomes. The performance of the PRECISE-DAPT score was also validated in this cohort. RESULTS: Among 561 enrolled patients, 165 (29.4 %) experienced bleeding events, with 49 (8.7 %) cases being significant. In the validation cohort, eXtreme gradient boosting (XGB) achieved the highest area under the receiver operating characteristic curve (0.915) and precision-recall curve (0.692). Compared to PRECISE-DAPT, XGB showed no difference in AUROC (p = 0.808) but had a higher AUPRC (p = 0.009). In the temporal external validation cohort, the XGB model has an AUROC of 0.926 and an AUPRC of 0.703. We developed a dynamic high-accuracy bleeding risk calculator based on the XGB model and created a mobile-friendly QR code for easy access to this tool. CONCLUSION: Bleeding risk during DAPT in post-CABG patients can be reliably predicted using selected baseline features. The XGB model outperforms the Precise-Dapt model, showing better precision and recall.
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Yang et al. (2024) conducted a cohort in Bleeding risk after coronary artery bypass grafting with dual antiplatelet therapy (n=561). eXtreme gradient boosting (XGB) machine learning model vs. PRECISE-DAPT score was evaluated on Prediction of bleeding within six months post-CABG (p=0.009). An eXtreme gradient boosting model outperformed the PRECISE-DAPT score in predicting 6-month bleeding risk post-CABG, showing a higher area under the precision-recall curve (P=0.009).
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