Key result
AI-BR model outperforms ACC-BR in predicting major bleeding post-PCI with an AUC of ~0.87.
Why the study?
The study was conducted to determine the accuracy of machine learning in predicting bleeding outcomes post PCI compared with the American College of Cardiology CathPCI bleeding risk model.
Does a machine learning model (AI-BR) improve the prediction of bleeding complications in patients undergoing PCI compared to the ACC-BR model?
Observational (n=15,603)
Does a machine learning model (AI-BR) improve the prediction of bleeding complications in patients undergoing PCI compared to the ACC-BR model?
Absolute Event Rate: 0.873% vs 0.764%
p-value: p=.02
A machine learning model utilizing 105 variables significantly outperformed the standard ACC-BR model in predicting major bleeding complications after PCI.
May improve post-PCI bleeding prediction; hypothesis-generating and requires prospective validation before practice change.
INTRODUCTION: The purpose of the current study is to determine the accuracy of machine learning in predicting bleeding outcomes post percutaneous coronary intervention (PCI) in comparison with the American College of Cardiology CathPCI bleeding risk (ACC-BR) model. METHODS: Mayo Clinic CathPCI registry data were retrospectively analyzed from January, 2003 to June, 2018, including 15,603 patients who underwent PCI. The cohort was randomly divided into a training sample of 11,703 patients (75%) and a unique test sample of 3900 patients (25%) prior to model generation. The risk-prediction model was generated utilizing a boosted classification tree algorithm of 105 unique variables to predict the risk of major and minor bleeding complications within 72 hours after PCI or before hospital discharge. The receiver operating characteristic (ROC) curves and areas under the curve (AUC) for the boosted classification tree algorithm (AI-BR) model and ACC-BR model were compared for the test cohort. RESULTS: The mean age of the patient cohort was 67 ± 12.7 years, and women constituted 30% of the cohort. The rate of major bleeding complications in the entire cohort was 1.8%. The sensitivity and specificity of the AIBR model were 77.3% and 80.9%, respectively. The ROC-AUC for the AI-BR model (0.873) was superior vs the ACC-BR model (0.764; P=.02) in predicting major bleeding for the test cohort. CONCLUSION: The AI-BR model accurately predicts bleeding post PCI and outperforms the ACC-BR model in predicting the risk of bleeding in patients undergoing PCI.
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Rayfield et al. (2020) conducted an observational in Post percutaneous coronary intervention (PCI) (n=15,603). Boosted classification tree algorithm (AI-BR) model vs. American College of Cardiology CathPCI bleeding risk (ACC-BR) model was evaluated on Major bleeding complications within 72 hours after PCI or before hospital discharge (ROC-AUC) (p=.02). The AI-BR machine learning model was superior to the ACC-BR model in predicting major bleeding post-PCI (AUC 0.873 vs 0.764; P=.02).