Division of Cardiology, Duke University School of Medicine, Durham, NC, USA.
Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan.
Eur J Heart Fail. 2023 Sep;25(9):1507-1525. doi: 10.1002/ejhf.2994. Epub 2023 Sep 8.
Heart failure (HF) is a heterogeneous syndrome affecting more than 60 million individuals globally. Despite recent advancements in understanding of the pathophysiology of HF, many issues remain including residual risk despite therapy, understanding the pathophysiology and phenotypes of patients with HF and preserved ejection fraction, and the challenges related to integrating a large amount of disparate information available for risk stratification and management of these patients. Risk prediction algorithms based on artificial intelligence (AI) may have superior predictive ability compared to traditional methods in certain instances. AI algorithms can play a pivotal role in the evolution of HF care by facilitating clinical decision making to overcome various challenges such as allocation of treatment to patients who are at highest risk or are more likely to benefit from therapies, prediction of adverse outcomes, and early identification of patients with subclinical disease or worsening HF. With the ability to integrate and synthesize large amounts of data with multidimensional interactions, AI algorithms can supply information with which physicians can improve their ability to make timely and better decisions. In this review, we provide an overview of the AI algorithms that have been developed for establishing early diagnosis of HF, phenotyping HF with preserved ejection fraction, and stratifying HF disease severity. This review also discusses the challenges in clinical deployment of AI algorithms in HF, and the potential path forward for developing future novel learning-based algorithms to improve HF care.
心力衰竭（HF）是一种影响全球超过 6000 万人的异质性综合征。尽管近年来对 HF 病理生理学的理解有了进展，但仍存在许多问题，包括尽管进行了治疗，但仍存在残余风险、理解 HF 和射血分数保留患者的病理生理学和表型，以及与整合大量用于这些患者风险分层和管理的不同信息相关的挑战。基于人工智能（AI）的风险预测算法在某些情况下可能具有比传统方法更高的预测能力。AI 算法可以通过促进临床决策来在 HF 护理的发展中发挥关键作用，以克服各种挑战，例如将治疗分配给风险最高或更有可能从治疗中获益的患者、预测不良结局，以及早期识别患有亚临床疾病或 HF 恶化的患者。AI 算法具有整合和综合大量具有多维相互作用的数据的能力，可以提供信息，医生可以利用这些信息提高及时做出更好决策的能力。在这篇综述中，我们概述了为早期诊断 HF、HF 伴射血分数保留表型以及 HF 疾病严重程度分层而开发的 AI 算法。这篇综述还讨论了 AI 算法在 HF 中的临床部署所面临的挑战，以及开发未来基于学习的新型算法以改善 HF 护理的潜在途径。
