Dynamic Prognosis Prediction for Patients on DAPT After Drug-Eluting Stent Implantation: Model Development and Validation

Authors

Fang Li
Laila Rasmy
Yang Xiang
Jingna Feng
Ahmed Abdelhameed
Xinyue Hu
Zenan Sun
David Aguilar
Abhijeet Dhoble
Jingcheng Du
Qing Wang
Shuteng Niu
Yifang Dang
Xinyuan Zhang
Ziqian Xie
Yi Nian
JianPing He
Yujia Zhou
Jianfu Li
Mattia Prosperi
Jiang Bian
Degui Zhi
Cui Tao

Publication Date

2-6-2024

Journal

Journal of the American Heart Association

Abstract

BACKGROUND: The rapid evolution of artificial intelligence (AI) in conjunction with recent updates in dual antiplatelet therapy (DAPT) management guidelines emphasizes the necessity for innovative models to predict ischemic or bleeding events after drug-eluting stent implantation. Leveraging AI for dynamic prediction has the potential to revolutionize risk stratification and provide personalized decision support for DAPT management.

METHODS AND RESULTS: We developed and validated a new AI-based pipeline using retrospective data of drug-eluting stent-treated patients, sourced from the Cerner Health Facts data set (n=98 236) and Optum's de-identified Clinformatics Data Mart Database (n=9978). The 36 months following drug-eluting stent implantation were designated as our primary forecasting interval, further segmented into 6 sequential prediction windows. We evaluated 5 distinct AI algorithms for their precision in predicting ischemic and bleeding risks. Model discriminative accuracy was assessed using the area under the receiver operating characteristic curve, among other metrics. The weighted light gradient boosting machine stood out as the preeminent model, thus earning its place as our AI-DAPT model. The AI-DAPT demonstrated peak accuracy in the 30 to 36 months window, charting an area under the receiver operating characteristic curve of 90% [95% CI, 88%-92%] for ischemia and 84% [95% CI, 82%-87%] for bleeding predictions.

CONCLUSIONS: Our AI-DAPT excels in formulating iterative, refined dynamic predictions by assimilating ongoing updates from patients' clinical profiles, holding value as a novel smart clinical tool to facilitate optimal DAPT duration management with high accuracy and adaptability.

Keywords

artificial intelligence, coronary artery disease, drug‐eluting stent implantation, dual anti‐platelet therapy, dynamic prediction, Machine Learning, Big Data and Data Standards, Ischemia

Comments

Supplementary Materials

PMID: 38293921