Current Cardiovascular Risk Reports最新文献

{"title":"Machine Learning in Vascular Medicine: Optimizing Clinical Strategies for Peripheral Artery Disease.","authors":"Sean Perez, Sneha Thandra, Ines Mellah, Laura Kraemer, Elsie Ross","doi":"10.1007/s12170-024-00752-7","DOIUrl":"10.1007/s12170-024-00752-7","url":null,"abstract":"<p><strong>Purpose of review: </strong>Peripheral Artery Disease (PAD), a condition affecting millions of patients, is often underdiagnosed due to a lack of symptoms in the early stages and management can be complex given differences in genetic and phenotypic characteristics. This review aims to provide readers with an update on the utility of machine learning (ML) in the management of PAD.</p><p><strong>Recent findings: </strong>Recent research leveraging electronic health record (EHR) data and ML algorithms have demonstrated significant advances in the potential use of automated systems, namely artificial intelligence (AI), to accurately identify patients who might benefit from further PAD screening. Additionally, deep learning algorithms can be used on imaging data to assist in PAD diagnosis and automate clinical risk stratification.ML models can predict major adverse cardiovascular events (MACE) and major adverse limb events (MALE) with considerable accuracy, with many studies also demonstrating the ability to more accurately risk stratify patients for deleterious outcomes after surgical intervention. These predictions can assist physicians in developing more patient-centric treatment plans and allow for earlier, more aggressive management of modifiable risk-factors in high-risk patients. The use of proteomic biomarkers in ML models offers a valuable addition to traditional screening and stratification paradigms, though clinical utility may be limited by cost and accessibility.</p><p><strong>Summary: </strong>The application of AI to the care of PAD patients may enable earlier diagnosis and more accurate risk stratification, leveraging readily available EHR and imaging data, and there is a burgeoning interest in incorporating biological data for further refinement. Thus, the promise of precision PAD care grows closer. Future research should focus on validating these models via real-world integration into clinical practice and prospective evaluation of the impact of this new care paradigm.</p>","PeriodicalId":46144,"journal":{"name":"Current Cardiovascular Risk Reports","volume":"18 12","pages":"187-195"},"PeriodicalIF":2.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11567977/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142648852","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}

{"title":"Racial and Ethnic Disparities in Peri-and Post-operative Cardiac Surgery.","authors":"Shane S Scott, Doug A Gouchoe, Lovette Azap, Matthew C Henn, Kukbin Choi, Nahush A Mokadam, Bryan A Whitson, Timothy M Pawlik, Asvin M Ganapathi","doi":"10.1007/s12170-024-00739-4","DOIUrl":"10.1007/s12170-024-00739-4","url":null,"abstract":"<p><strong>Purpose of review: </strong>Despite efforts to curtail its impact on medical care, race remains a powerful risk factor for morbidity and mortality following cardiac surgery. While patients from racial and ethnic minority groups are underrepresented in cardiac surgery, they experience a disproportionally elevated number of adverse outcomes following various cardiac surgical procedures. This review provides a summary of existing literature highlighting disparities in coronary artery bypass surgery, valvular surgery, cardiac transplantation, and mechanical circulatory support.</p><p><strong>Recent findings: </strong>Unfortunately, specific causes of these disparities can be difficult to identify, even in large, multicenter studies, due to the complex relationship between race and post-operative outcomes. Current data suggest that these racial/ethnic disparities can be attributed to a combination of patient, socioeconomic, and hospital setting characteristics.</p><p><strong>Summary: </strong>Proposed solutions to combat the mechanisms underlying the observed disparate outcomes require deployment of a multidisciplinary team of cardiologists, anesthesiologists, cardiac surgeons, and experts in health care equity and medical ethics. Successful identification of at-risk populations and the implementation of preventive measures are necessary first steps towards dismantling racial/ethnic differences in cardiac surgery outcomes.</p>","PeriodicalId":46144,"journal":{"name":"Current Cardiovascular Risk Reports","volume":"18 7","pages":"95-113"},"PeriodicalIF":2.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11296970/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141890394","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}

{"title":"Diagnosis and Management of Hypertension in Adolescents with Obesity.","authors":"Shradha M Chhabria, Jared LeBron, Sarah D Ronis, Courtney E Batt","doi":"10.1007/s12170-024-00740-x","DOIUrl":"10.1007/s12170-024-00740-x","url":null,"abstract":"<p><strong>Purpose of review: </strong>Hypertension (HTN) and obesity are increasing in prevalence and severity in adolescents and have significant implications for long term morbidity and mortality. This review focuses on the diagnosis and management of HTN in adolescents with obesity with an emphasis on co-management of the two conditions.</p><p><strong>Recent findings: </strong>Recent studies affirm the increasing prevalence of abnormal blood pressures and diagnoses of HTN associated with increased adiposity. Current guidelines recommend routine screening with proper technique for HTN in patients with obesity. Additionally, obesity and HTN related co-occurring medical conditions should be evaluated as there is frequently a bidirectional impact on risk and outcomes. Importantly, advances in adolescent obesity management have subsequently led to positive implications for the management of obesity-related comorbidities such as HTN. The co-management of obesity and HTN is an emerging strategy for treatment and prevention of additional morbidity and mortality as patients progress to adulthood.</p><p><strong>Summary: </strong>In adolescent patients with obesity, prompt recognition and appropriate diagnosis of HTN as well as related co-occurring conditions are necessary first steps in management. Co-management of obesity and HTN is likely to lead to improved outcomes. While lifestyle interventions serve as the foundation to this management, adjunctive and emerging therapies should be considered to adequately treat both conditions.</p>","PeriodicalId":46144,"journal":{"name":"Current Cardiovascular Risk Reports","volume":"18 8-9","pages":"115-124"},"PeriodicalIF":2.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11297811/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141894581","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}

{"title":"Assisted Reproductive Technology and Cardiovascular Risk in Women","authors":"Katherine Cameron, Barbara Luke, Gaya Murugappan, Valerie L. Baker","doi":"10.1007/s12170-023-00732-3","DOIUrl":"https://doi.org/10.1007/s12170-023-00732-3","url":null,"abstract":"","PeriodicalId":46144,"journal":{"name":"Current Cardiovascular Risk Reports","volume":"17 7","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138947535","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}

{"title":"Obesity Management Solutions in Rural Communities","authors":"Elizabeth A. Beverly","doi":"10.1007/s12170-023-00733-2","DOIUrl":"https://doi.org/10.1007/s12170-023-00733-2","url":null,"abstract":"","PeriodicalId":46144,"journal":{"name":"Current Cardiovascular Risk Reports","volume":"13 8","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138970737","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}

{"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}

{"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}

{"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}

{"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}

{"title":"Preimplantation Genetic Testing for Inherited Heart Diseases","authors":"Chelsea Stevens, Robyn Hylind, Sophie Adams, Allison L Cirino","doi":"10.1007/s12170-023-00727-0","DOIUrl":"https://doi.org/10.1007/s12170-023-00727-0","url":null,"abstract":"","PeriodicalId":46144,"journal":{"name":"Current Cardiovascular Risk Reports","volume":"1 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44452965","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}