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":"109 22","pages":"0"},"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":"22 1","pages":"0"},"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 : 2023-10-01Epub Date: 2023-08-16DOI: 10.1007/s12170-023-00725-2
Phillippe F Nyembo, Caitlin Bakker, Woubeshet Ayenew, Gautam R Shroff, Andrew M Busch, Katherine Diaz Vickery
Purpose: In this review, we examine the intersection of cardiovascular disease (CVD), race/ethnicity, and homelessness/unstable housing-a key social determinant of cardiovascular health. Homelessness has deep roots reflecting structural racism in housing, educational, and economic policies, leading to disproportional representation of Black, Native American, and Hispanic people among the U.S. homeless population.
Recent findings: Increasingly rigorous observational studies detail the disproportionate incidence, prevalence, and mortality of CVD among people experiencing homelessness. Studies of hospital admissions document concerning disparities in procedural CVD care. We summarize current evidence about CVD among people experiencing homelessness. We conducted a new structured review of 27 articles about CVD and homelessness to determine if and how they collected and reported on race/ethnicity and racism. We searched for evidence-based interventions to improve CVD for people experiencing homelessness.
Summary: A recent systematic review and additional articles addressing CVD and homelessness found no interventions targeting the intersections of these topics or any that specifically addressed race/ethnicity. We found that 16 of 27 reviewed studies (59%) collected any data on race/ethnicity, but only 5 (19%) reported CVD-specific outcomes by race/ethnicity. We summarize clinical evidence based on expert opinion that, while practical, lacks rigor and does not consider the intersectional impact of race/ethnicity, homelessness, and other factors on people experiencing CVD. Therefore, we conclude that more research and innovation are needed using community-engaged approaches to develop evidence to support improved CVD prevention and management among people experiencing homelessness who identify as Black, Native American, Hispanic, or other people of color.
{"title":"Homelessness, Race/Ethnicity, and Cardiovascular Disease: a State‑of‑the‑Evidence Summary and Structured Review of Race/Ethnicity Reporting.","authors":"Phillippe F Nyembo, Caitlin Bakker, Woubeshet Ayenew, Gautam R Shroff, Andrew M Busch, Katherine Diaz Vickery","doi":"10.1007/s12170-023-00725-2","DOIUrl":"10.1007/s12170-023-00725-2","url":null,"abstract":"<p><strong>Purpose: </strong>In this review, we examine the intersection of cardiovascular disease (CVD), race/ethnicity, and homelessness/unstable housing-a key social determinant of cardiovascular health. Homelessness has deep roots reflecting structural racism in housing, educational, and economic policies, leading to disproportional representation of Black, Native American, and Hispanic people among the U.S. homeless population.</p><p><strong>Recent findings: </strong>Increasingly rigorous observational studies detail the disproportionate incidence, prevalence, and mortality of CVD among people experiencing homelessness. Studies of hospital admissions document concerning disparities in procedural CVD care. We summarize current evidence about CVD among people experiencing homelessness. We conducted a new structured review of 27 articles about CVD and homelessness to determine if and how they collected and reported on race/ethnicity and racism. We searched for evidence-based interventions to improve CVD for people experiencing homelessness.</p><p><strong>Summary: </strong>A recent systematic review and additional articles addressing CVD and homelessness found no interventions targeting the intersections of these topics or any that specifically addressed race/ethnicity. We found that 16 of 27 reviewed studies (59%) collected any data on race/ethnicity, but only 5 (19%) reported CVD-specific outcomes by race/ethnicity. We summarize clinical evidence based on expert opinion that, while practical, lacks rigor and does not consider the intersectional impact of race/ethnicity, homelessness, and other factors on people experiencing CVD. Therefore, we conclude that more research and innovation are needed using community-engaged approaches to develop evidence to support improved CVD prevention and management among people experiencing homelessness who identify as Black, Native American, Hispanic, or other people of color.</p>","PeriodicalId":46144,"journal":{"name":"Current Cardiovascular Risk Reports","volume":"17 1","pages":"167-176"},"PeriodicalIF":1.8,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12522121/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"53316982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","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":"67 1","pages":"0"},"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":"66 1","pages":"0"},"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 : 2023-08-08DOI: 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":"17 1","pages":"155 - 165"},"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 : 2023-06-19DOI: 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":"17 1","pages":"133 - 141"},"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}
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