Pub Date : 2023-11-10DOI: 10.1007/s12170-023-00731-4
Shang-Fu Chen, Salvatore Loguercio, Kai-Yu Chen, Sang Eun Lee, Jun-Bean Park, Shuchen Liu, Hossein Javedani Sadaei, Ali Torkamani
Abstract Purpose of Review Coronary artery disease (CAD) is a common and etiologically complex disease worldwide. Current guidelines for primary prevention, or the prevention of a first acute event, include relatively simple risk assessment and leave substantial room for improvement both for risk ascertainment and selection of prevention strategies. Here, we review how advances in big data and predictive modeling foreshadow a promising future of improved risk assessment and precision medicine for CAD. Recent Findings Artificial intelligence (AI) has improved the utility of high dimensional data, providing an opportunity to better understand the interplay between numerous CAD risk factors. Beyond applications of AI in cardiac imaging, the vanguard application of AI in healthcare, recent translational research is also revealing a promising path for AI in multi-modal risk prediction using standard biomarkers, genetic and other omics technologies, a variety of biosensors, and unstructured data from electronic health records (EHRs). However, gaps remain in clinical validation of AI models, most notably in the actionability of complex risk prediction for more precise therapeutic interventions. Summary The recent availability of nation-scale biobank datasets has provided a tremendous opportunity to richly characterize longitudinal health trajectories using health data collected at home, at laboratories, and through clinic visits. The ever-growing availability of deep genotype-phenotype data is poised to drive a transition from simple risk prediction algorithms to complex, “data-hungry,” AI models in clinical decision-making. While AI models provide the means to incorporate essentially all risk factors into comprehensive risk prediction frameworks, there remains a need to wrap these predictions in interpretable frameworks that map to our understanding of underlying biological mechanisms and associated personalized intervention. This review explores recent advances in the role of machine learning and AI in CAD primary prevention and highlights current strengths as well as limitations mediating potential future applications.
{"title":"Artificial Intelligence for Risk Assessment on Primary Prevention of Coronary Artery Disease","authors":"Shang-Fu Chen, Salvatore Loguercio, Kai-Yu Chen, Sang Eun Lee, Jun-Bean Park, Shuchen Liu, Hossein Javedani Sadaei, Ali Torkamani","doi":"10.1007/s12170-023-00731-4","DOIUrl":"https://doi.org/10.1007/s12170-023-00731-4","url":null,"abstract":"Abstract Purpose of Review Coronary artery disease (CAD) is a common and etiologically complex disease worldwide. Current guidelines for primary prevention, or the prevention of a first acute event, include relatively simple risk assessment and leave substantial room for improvement both for risk ascertainment and selection of prevention strategies. Here, we review how advances in big data and predictive modeling foreshadow a promising future of improved risk assessment and precision medicine for CAD. Recent Findings Artificial intelligence (AI) has improved the utility of high dimensional data, providing an opportunity to better understand the interplay between numerous CAD risk factors. Beyond applications of AI in cardiac imaging, the vanguard application of AI in healthcare, recent translational research is also revealing a promising path for AI in multi-modal risk prediction using standard biomarkers, genetic and other omics technologies, a variety of biosensors, and unstructured data from electronic health records (EHRs). However, gaps remain in clinical validation of AI models, most notably in the actionability of complex risk prediction for more precise therapeutic interventions. Summary The recent availability of nation-scale biobank datasets has provided a tremendous opportunity to richly characterize longitudinal health trajectories using health data collected at home, at laboratories, and through clinic visits. The ever-growing availability of deep genotype-phenotype data is poised to drive a transition from simple risk prediction algorithms to complex, “data-hungry,” AI models in clinical decision-making. While AI models provide the means to incorporate essentially all risk factors into comprehensive risk prediction frameworks, there remains a need to wrap these predictions in interpretable frameworks that map to our understanding of underlying biological mechanisms and associated personalized intervention. This review explores recent advances in the role of machine learning and AI in CAD primary prevention and highlights current strengths as well as limitations mediating potential future applications.","PeriodicalId":46144,"journal":{"name":"Current Cardiovascular Risk Reports","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135138429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-11DOI: 10.1007/s12170-023-00730-5
Kelsey Ufholz, James J. Werner
{"title":"Social and Demographic Correlates of Fast Food Consumption: A Review of Recent Findings in the United States and Worldwide","authors":"Kelsey Ufholz, James J. Werner","doi":"10.1007/s12170-023-00730-5","DOIUrl":"https://doi.org/10.1007/s12170-023-00730-5","url":null,"abstract":"","PeriodicalId":46144,"journal":{"name":"Current Cardiovascular Risk Reports","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136063725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-28DOI: 10.1007/s12170-023-00729-y
Zachary H. Hughes, Lydia M. Hughes, Sadiya S. Khan
{"title":"Genetic Contributions to Risk of Adverse Pregnancy Outcomes","authors":"Zachary H. Hughes, Lydia M. Hughes, Sadiya S. Khan","doi":"10.1007/s12170-023-00729-y","DOIUrl":"https://doi.org/10.1007/s12170-023-00729-y","url":null,"abstract":"","PeriodicalId":46144,"journal":{"name":"Current Cardiovascular Risk Reports","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135386961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-25DOI: 10.1007/s12170-023-00726-1
Megan N. Pelter, Giorgio Quer, Jay Pandit
{"title":"Remote Monitoring in Cardiovascular Diseases","authors":"Megan N. Pelter, Giorgio Quer, Jay Pandit","doi":"10.1007/s12170-023-00726-1","DOIUrl":"https://doi.org/10.1007/s12170-023-00726-1","url":null,"abstract":"","PeriodicalId":46144,"journal":{"name":"Current Cardiovascular Risk Reports","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135815275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-16DOI: 10.1007/s12170-023-00725-2
Phillippe F. Nyembo, C. Bakker, W. Ayenew, G. Shroff, Andrew M. Busch, K. Vickery
{"title":"Homelessness, Race/Ethnicity, and Cardiovascular Disease: a State-of-the-Evidence Summary and Structured Review of Race/Ethnicity Reporting","authors":"Phillippe F. Nyembo, C. Bakker, W. Ayenew, G. Shroff, Andrew M. Busch, K. Vickery","doi":"10.1007/s12170-023-00725-2","DOIUrl":"https://doi.org/10.1007/s12170-023-00725-2","url":null,"abstract":"","PeriodicalId":46144,"journal":{"name":"Current Cardiovascular Risk Reports","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"53316982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-08DOI: 10.1007/s12170-023-00724-3
Caraline Watkins, Zoe Schilling, Kevin Kawalec, Darrell Hulisz
{"title":"Does GLP-RA Plus an SGLT2 Inhibitor Yield Greater Weight Loss in Patients with Obesity and Diabetes than Monotherapy?","authors":"Caraline Watkins, Zoe Schilling, Kevin Kawalec, Darrell Hulisz","doi":"10.1007/s12170-023-00724-3","DOIUrl":"https://doi.org/10.1007/s12170-023-00724-3","url":null,"abstract":"","PeriodicalId":46144,"journal":{"name":"Current Cardiovascular Risk Reports","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42193961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-22DOI: 10.1007/s12170-023-00723-4
G. Adasuriya, S. Haldar
{"title":"Next Generation ECG: The Impact of Artificial Intelligence and Machine Learning","authors":"G. Adasuriya, S. Haldar","doi":"10.1007/s12170-023-00723-4","DOIUrl":"https://doi.org/10.1007/s12170-023-00723-4","url":null,"abstract":"","PeriodicalId":46144,"journal":{"name":"Current Cardiovascular Risk Reports","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48801333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-19DOI: 10.1007/s12170-023-00722-5
D. D. De Lurgio, M. Meador
{"title":"Hybrid Ablation Procedures of Atrial Fibrillation—How to Optimize Patient Selection and Improve the Procedural Approach","authors":"D. D. De Lurgio, M. Meador","doi":"10.1007/s12170-023-00722-5","DOIUrl":"https://doi.org/10.1007/s12170-023-00722-5","url":null,"abstract":"","PeriodicalId":46144,"journal":{"name":"Current Cardiovascular Risk Reports","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45989403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-31DOI: 10.1007/s12170-023-00718-1
D. Soltani, S. Stavrakis
{"title":"Neuromodulation for the Management of Atrial Fibrillation—How to Optimize Patient Selection and the Procedural Approach","authors":"D. Soltani, S. Stavrakis","doi":"10.1007/s12170-023-00718-1","DOIUrl":"https://doi.org/10.1007/s12170-023-00718-1","url":null,"abstract":"","PeriodicalId":46144,"journal":{"name":"Current Cardiovascular Risk Reports","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44086660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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