Dynamic Prognosis Prediction for Patients on DAPT After Drug‐Eluting Stent Implantation: Model Development and Validation
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 d...
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-02-01
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| Series: | Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease |
| Subjects: | |
| Online Access: | https://www.ahajournals.org/doi/10.1161/JAHA.123.029900 |
| _version_ | 1797296281123553280 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Fang Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T22:02:20Z |
| format | Article |
| id | doaj.art-882178cd3fb04cd5bd38789abcc7ae43 |
| institution | Directory Open Access Journal |
| issn | 2047-9980 |
| language | English |
| last_indexed | 2024-03-07T22:02:20Z |
| publishDate | 2024-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease |
| spelling | doaj.art-882178cd3fb04cd5bd38789abcc7ae432024-02-24T04:06:35ZengWileyJournal of the American Heart Association: Cardiovascular and Cerebrovascular Disease2047-99802024-02-0113310.1161/JAHA.123.029900Dynamic Prognosis Prediction for Patients on DAPT After Drug‐Eluting Stent Implantation: Model Development and ValidationFang Li0Laila Rasmy1Yang Xiang2Jingna Feng3Ahmed Abdelhameed4Xinyue Hu5Zenan Sun6David Aguilar7Abhijeet Dhoble8Jingcheng Du9Qing Wang10Shuteng Niu11Yifang Dang12Xinyuan Zhang13Ziqian Xie14Yi Nian15JianPing He16Yujia Zhou17Jianfu Li18Mattia Prosperi19Jiang Bian20Degui Zhi21Cui Tao22McWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USAMcWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USAPeng Cheng Laboratory Shenzhen Guangdong ChinaMcWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USAMcWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USAMcWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USAMcWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USADepartment of Internal Medicine, McGovern Medical School University of Texas Health Science Center at Houston Houston TX USADepartment of Internal Medicine, McGovern Medical School University of Texas Health Science Center at Houston Houston TX USAMcWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USAMcWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USAMcWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USAMcWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USAMcWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USAMcWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USAMcWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USAMcWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USAMcWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USAMcWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USAData Intelligence Systems Lab, Department of Epidemiology, College of Public Health and Health Professions & College of Medicine University of Florida Gainesville FL USADepartment of Health Outcomes and Biomedical Informatics, College of Medicine University of Florida Gainesville FL USAMcWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USAMcWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston Houston TX USABackground 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.https://www.ahajournals.org/doi/10.1161/JAHA.123.029900artificial intelligencecoronary artery diseasedrug‐eluting stent implantationdual anti‐platelet therapydynamic prediction |
| spellingShingle | 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 Dynamic Prognosis Prediction for Patients on DAPT After Drug‐Eluting Stent Implantation: Model Development and Validation Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease artificial intelligence coronary artery disease drug‐eluting stent implantation dual anti‐platelet therapy dynamic prediction |
| title | Dynamic Prognosis Prediction for Patients on DAPT After Drug‐Eluting Stent Implantation: Model Development and Validation |
| title_full | Dynamic Prognosis Prediction for Patients on DAPT After Drug‐Eluting Stent Implantation: Model Development and Validation |
| title_fullStr | Dynamic Prognosis Prediction for Patients on DAPT After Drug‐Eluting Stent Implantation: Model Development and Validation |
| title_full_unstemmed | Dynamic Prognosis Prediction for Patients on DAPT After Drug‐Eluting Stent Implantation: Model Development and Validation |
| title_short | Dynamic Prognosis Prediction for Patients on DAPT After Drug‐Eluting Stent Implantation: Model Development and Validation |
| title_sort | dynamic prognosis prediction for patients on dapt after drug eluting stent implantation model development and validation |
| topic | artificial intelligence coronary artery disease drug‐eluting stent implantation dual anti‐platelet therapy dynamic prediction |
| url | https://www.ahajournals.org/doi/10.1161/JAHA.123.029900 |
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