Hamid Arsalan, Segar Matthew W, Bozkurt Biykem, Santos-Gallego Carlos, Nambi Vijay, Butler Javed, Hall Michael E, Fudim Marat
Division of Cardiology, Department of Medicine, Baylor College of Medicine, 6655 Travis Street, Suite 320, Houston, TX, 77030, USA.
Division of Cardiology, Department of Medicine, Texas Heart Institute, Houston, TX, USA.
Heart Fail Rev. 2025 Jan;30(1):117-129. doi: 10.1007/s10741-024-10448-0. Epub 2024 Oct 7.
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.
心力衰竭（HF）是一种全球流行疾病，患病率不断上升，给医疗系统带来日益沉重的负担。机器学习（ML）有潜力彻底改变医学，并可通过多种不同形式应用于辅助预防有症状的HF（C期）。目前HF预防面临若干挑战，尤其是在检测HF前期（B期）方面。当代模型会漏诊HF事件，经证实可预防HF的治疗选择有限，尤其缺乏针对射血分数保留的HF的预防措施。ML有潜力通过现有及未来模型克服这些挑战。ML存在局限性，但在大多数情况下，ML的诸多益处超过这些局限性和风险。ML可通过多种策略应用于HF预防，如完善HF发病风险预测模型、从心电图、胸部X光或超声心动图等现有检查中捕捉诊断迹象以识别提示HF前期（B期HF）的心脏结构/功能异常，以及解读生物标志物和表观遗传数据。总之，ML能够扩大对HF风险个体（A期HF）的筛查，识别HF前期人群（B期HF），预测C期HF事件的发病风险，并提供早期干预的能力以预防进展至C期HF或其病情恶化。在这篇叙述性综述中，我们讨论了ML用于HF预防的方法、ML在HF风险预测中的益处和缺陷，以及未来的发展方向。
