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

Author:

Li Fang12ORCID,Rasmy Laila1ORCID,Xiang Yang3ORCID,Feng Jingna12ORCID,Abdelhameed Ahmed12ORCID,Hu Xinyue12ORCID,Sun Zenan1ORCID,Aguilar David45ORCID,Dhoble Abhijeet4ORCID,Du Jingcheng1ORCID,Wang Qing1ORCID,Niu Shuteng1ORCID,Dang Yifang1ORCID,Zhang Xinyuan1ORCID,Xie Ziqian1ORCID,Nian Yi1ORCID,He JianPing1ORCID,Zhou Yujia1ORCID,Li Jianfu12ORCID,Prosperi Mattia6ORCID,Bian Jiang7ORCID,Zhi Degui1ORCID,Tao Cui12ORCID

Affiliation:

1. McWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USA

2. Department of Artificial Intelligence and Informatics Mayo Clinic Jacksonville FL USA

3. Peng Cheng Laboratory Shenzhen Guangdong China

4. Department of Internal Medicine, McGovern Medical School University of Texas Health Science Center at Houston Houston TX USA

5. LSU School of Medicine, LSU Health New Orleans New Orleans LA USA

6. Data Intelligence Systems Lab, Department of Epidemiology, College of Public Health and Health Professions & College of Medicine University of Florida Gainesville FL USA

7. Department of Health Outcomes and Biomedical Informatics, College of Medicine University of Florida Gainesville FL USA

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.

Publisher

Ovid Technologies (Wolters Kluwer Health)

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