预防心力衰竭的机器学习

IF 4.5 2区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
Arsalan Hamid, Matthew W Segar, Biykem Bozkurt, Carlos Santos-Gallego, Vijay Nambi, Javed Butler, Michael E Hall, Marat Fudim
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引用次数: 0

摘要

心力衰竭(HF)是一种全球流行病,发病率越来越高,对医疗保健系统造成的负担也越来越重。机器学习(ML)具有彻底改变医学的潜力,可以以多种不同的形式应用于无症状心力衰竭(C 阶段)的预防。高血压预防目前面临着一些挑战,特别是在检测高血压前期(B 阶段)方面。在现代模型中,心房颤动事件被遗漏,经证实可预防心房颤动的治疗方案有限,尤其缺乏对射血功能保留的心房颤动的预防。通过现有和未来的模型,ML 有可能克服这些挑战。ML 有其局限性,但在大多数情况下,ML 的诸多益处超过了这些局限性和风险。ML 可通过各种策略应用于高血压预防,如完善高血压发病风险预测模型,从心电图、胸部 X 光片或超声心动图等现有检查中捕捉诊断征象,以确定提示高血压前期(B 期高血压)的心脏结构/功能异常,以及解读生物标志物和表观遗传学数据。总之,ML 能够扩大对高危人群(A 期高血压)的筛查范围,识别高血压前期(B 期高血压)人群,预测 C 期高血压事件的发生风险,并提供早期干预的能力,以防止进展为 C 期高血压或病情恶化。在这篇叙述性综述中,我们将讨论在高频预防中使用 ML 的方法、ML 在高频风险预测中的益处和缺陷以及未来的发展方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning in the prevention of heart failure.

Heart failure (HF) is a global pandemic with a growing prevalence and is a growing burden on the healthcare system. Machine learning (ML) has the potential to revolutionize medicine and can be applied in many different forms to aid in the prevention of symptomatic HF (stage C). HF prevention currently has several challenges, specifically in the detection of pre-HF (stage B). HF events are missed in contemporary models, limited therapeutic options are proven to prevent HF, and the prevention of HF with preserved ejection is particularly lacking. ML has the potential to overcome these challenges through existing and future models. ML has limitations, but the many benefits of ML outweigh these limitations and risks in most scenarios. ML can be applied in HF prevention through various strategies such as refinement of incident HF risk prediction models, capturing diagnostic signs from available tests such as electrocardiograms, chest x-rays, or echocardiograms to identify structural/functional cardiac abnormalities suggestive of pre-HF (stage B HF), and interpretation of biomarkers and epigenetic data. Altogether, ML is able to expand the screening of individuals at risk for HF (stage A HF), identify populations with pre-HF (stage B HF), predict the risk of incident stage C HF events, and offer the ability to intervene early to prevent progression to or decline in stage C HF. In this narrative review, we discuss the methods by which ML is utilized in HF prevention, the benefits and pitfalls of ML in HF risk prediction, and the future directions.

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来源期刊
Heart Failure Reviews
Heart Failure Reviews 医学-心血管系统
CiteScore
10.40
自引率
2.20%
发文量
90
审稿时长
6-12 weeks
期刊介绍: Heart Failure Reviews is an international journal which develops links between basic scientists and clinical investigators, creating a unique, interdisciplinary dialogue focused on heart failure, its pathogenesis and treatment. The journal accordingly publishes papers in both basic and clinical research fields. Topics covered include clinical and surgical approaches to therapy, basic pharmacology, biochemistry, molecular biology, pathology, and electrophysiology. The reviews are comprehensive, expanding the reader''s knowledge base and awareness of current research and new findings in this rapidly growing field of cardiovascular medicine. All reviews are thoroughly peer-reviewed before publication.
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