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Machine Learning in Vascular Medicine: Optimizing Clinical Strategies for Peripheral Artery Disease. 血管医学中的机器学习:优化外周动脉疾病的临床策略。
IF 2 Q3 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2024-01-01 Epub Date: 2024-11-04 DOI: 10.1007/s12170-024-00752-7
Sean Perez, Sneha Thandra, Ines Mellah, Laura Kraemer, Elsie Ross

Purpose of review: 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.

Recent findings: 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.

Summary: 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.

综述的目的:外周动脉疾病(PAD)是一种影响数百万患者的疾病,由于早期缺乏症状,往往诊断不足,而且由于遗传和表型特征的差异,治疗可能很复杂。本综述旨在向读者介绍机器学习(ML)在并发心肌梗塞(PAD)管理中的最新应用:最近的研究利用电子健康记录 (EHR) 数据和 ML 算法证明了自动化系统(即人工智能 (AI))在准确识别可能从进一步 PAD 筛查中受益的患者方面的潜在应用取得了重大进展。此外,深度学习算法还可用于成像数据,以协助 PAD 诊断并自动进行临床风险分层。ML 模型可以相当准确地预测主要不良心血管事件 (MACE) 和主要不良肢体事件 (MALE),许多研究还表明它能够更准确地对手术干预后出现有害结果的患者进行风险分层。这些预测可以帮助医生制定更加以患者为中心的治疗计划,并对高危患者中可改变的风险因素进行更早、更积极的管理。在 ML 模型中使用蛋白质组生物标志物为传统的筛查和分层范例提供了有价值的补充,尽管临床实用性可能会受到成本和可及性的限制。摘要:将人工智能应用于 PAD 患者的治疗,可以利用现成的 EHR 和成像数据,实现更早的诊断和更准确的风险分层,而且人们对纳入生物数据以进一步完善的兴趣也在不断增长。因此,PAD 精准治疗的希望越来越近了。未来的研究应侧重于通过将这些模型真实地融入临床实践来验证它们,并对这种新护理模式的影响进行前瞻性评估。
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引用次数: 0
Racial and Ethnic Disparities in Peri-and Post-operative Cardiac Surgery. 心脏手术围手术期和术后的种族和民族差异。
IF 2 Q3 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2024-01-01 Epub Date: 2024-07-29 DOI: 10.1007/s12170-024-00739-4
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

Purpose of review: 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.

Recent findings: 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.

Summary: 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.

回顾的目的:尽管人们努力减少种族对医疗护理的影响,但种族仍然是心脏手术后发病率和死亡率的一个重要风险因素。虽然少数种族和少数族裔患者在心脏外科手术中的比例偏低,但他们在各种心脏外科手术后的不良后果却高得不成比例。本综述对现有文献进行了总结,强调了冠状动脉搭桥手术、瓣膜手术、心脏移植和机械循环支持等方面的差异:不幸的是,由于种族与术后结果之间的复杂关系,即使在大型多中心研究中也很难确定造成这些差异的具体原因。目前的数据表明,这些种族/人种差异可归因于患者、社会经济和医院环境特征的综合作用。总结:为消除所观察到的差异结果背后的机制,建议的解决方案需要部署一个由心脏病专家、麻醉专家、心脏外科专家以及医疗公平和医学伦理专家组成的多学科团队。成功识别高危人群并实施预防措施是消除心脏手术结果中种族/民族差异的第一步。
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引用次数: 0
Diagnosis and Management of Hypertension in Adolescents with Obesity. 青少年肥胖症患者高血压的诊断和管理。
IF 2 Q3 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2024-01-01 Epub Date: 2024-07-30 DOI: 10.1007/s12170-024-00740-x
Shradha M Chhabria, Jared LeBron, Sarah D Ronis, Courtney E Batt

Purpose of review: 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.

Recent findings: 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.

Summary: 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.

综述目的:高血压(HTN)和肥胖症在青少年中的发病率和严重程度不断增加,对长期发病率和死亡率有重大影响。本综述侧重于青少年肥胖症患者高血压的诊断和管理,重点是这两种疾病的共同管理:最近的研究证实,血压异常和高血压的诊断与肥胖增加有关。目前的指南建议采用适当的技术对肥胖患者进行高血压的常规筛查。此外,还应评估与肥胖和高血压相关的并发症,因为这往往会对风险和结果产生双向影响。重要的是,青少年肥胖症管理方面的进展随后对肥胖相关合并症(如高血压)的管理产生了积极影响。摘要:对于青少年肥胖症患者,及时识别和适当诊断高血压和相关并发症是治疗的第一步。对肥胖和高血压的共同管理可能会改善治疗效果。虽然生活方式干预是这种管理的基础,但应考虑采用辅助疗法和新兴疗法来充分治疗这两种疾病。
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引用次数: 0
Assisted Reproductive Technology and Cardiovascular Risk in Women 辅助生殖技术与女性心血管风险
IF 1.9 Q3 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2023-12-22 DOI: 10.1007/s12170-023-00732-3
Katherine Cameron, Barbara Luke, Gaya Murugappan, Valerie L. Baker
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引用次数: 0
Obesity Management Solutions in Rural Communities 农村社区肥胖症管理解决方案
IF 1.9 Q3 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2023-12-15 DOI: 10.1007/s12170-023-00733-2
Elizabeth A. Beverly
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引用次数: 0
Artificial Intelligence for Risk Assessment on Primary Prevention of Coronary Artery Disease 人工智能在冠心病一级预防风险评估中的应用
Q3 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2023-11-10 DOI: 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.
冠状动脉疾病(CAD)是世界范围内常见且病因复杂的疾病。目前的一级预防或预防首次急性事件指南包括相对简单的风险评估,并在风险确定和预防战略选择方面留下了很大的改进空间。在这里,我们回顾了大数据和预测建模的进步如何预示着CAD风险评估和精准医疗的美好未来。人工智能(AI)提高了高维数据的实用性,为更好地了解众多CAD风险因素之间的相互作用提供了机会。除了人工智能在心脏成像中的应用,人工智能在医疗保健中的先锋应用,最近的转化研究也揭示了人工智能在使用标准生物标志物、遗传和其他组学技术、各种生物传感器和来自电子健康记录(EHRs)的非结构化数据进行多模式风险预测方面的有前途的途径。然而,人工智能模型的临床验证仍然存在差距,最明显的是在复杂风险预测的可操作性方面,以进行更精确的治疗干预。最近全国性生物库数据集的可用性为利用在家庭、实验室和通过诊所就诊收集的健康数据丰富地描述纵向健康轨迹提供了巨大的机会。深度基因型-表型数据的日益可用性,将推动临床决策从简单的风险预测算法向复杂的、“数据饥渴”的人工智能模型过渡。虽然人工智能模型提供了将所有风险因素纳入综合风险预测框架的手段,但仍需要将这些预测包装在可解释的框架中,以映射我们对潜在生物机制和相关个性化干预的理解。这篇综述探讨了机器学习和人工智能在CAD一级预防中的作用的最新进展,并强调了当前的优势以及潜在未来应用的局限性。
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引用次数: 0
Social and Demographic Correlates of Fast Food Consumption: A Review of Recent Findings in the United States and Worldwide 快餐消费的社会和人口关系:美国和世界范围内最近发现的综述
Q3 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2023-10-11 DOI: 10.1007/s12170-023-00730-5
Kelsey Ufholz, James J. Werner
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引用次数: 0
Genetic Contributions to Risk of Adverse Pregnancy Outcomes 遗传因素对不良妊娠结局的影响
Q3 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2023-09-28 DOI: 10.1007/s12170-023-00729-y
Zachary H. Hughes, Lydia M. Hughes, Sadiya S. Khan
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引用次数: 0
Remote Monitoring in Cardiovascular Diseases 心血管疾病的远程监测
Q3 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2023-09-25 DOI: 10.1007/s12170-023-00726-1
Megan N. Pelter, Giorgio Quer, Jay Pandit
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引用次数: 0
Preimplantation Genetic Testing for Inherited Heart Diseases 遗传性心脏病的植入前基因检测
IF 1.9 Q3 CARDIAC & CARDIOVASCULAR SYSTEMS Pub Date : 2023-09-06 DOI: 10.1007/s12170-023-00727-0
Chelsea Stevens, Robyn Hylind, Sophie Adams, Allison L Cirino
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引用次数: 0
期刊
Current Cardiovascular Risk Reports
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