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定义射血分数保留型心力衰竭的表型。

Defining the Phenotypes for Heart Failure With Preserved Ejection Fraction.

机构信息

Department of Medicine, Boston Medical Center, Boston, MA, USA.

Massachusetts Veterans Epidemiology Research & Information Center, Veterans Affairs Boston Healthcare System, Cardiology Section (111), 1400 VFW Parkway, West Roxbury, Boston, MA, 02132, USA.

出版信息

Curr Heart Fail Rep. 2022 Dec;19(6):445-457. doi: 10.1007/s11897-022-00582-x. Epub 2022 Sep 30.

Abstract

PURPOSE OF REVIEW

Heart failure with preserved ejection fraction (HFpEF) imposes a significant burden on society and healthcare. The lack in efficacious therapies is likely due to the significant heterogeneity of HFpEF. In this review, we define various phenotypes based on underlying comorbidities or etiologies, discuss phenotypes arrived at by novel methods, and explore therapeutic targets.

RECENT FINDINGS

A few studies have used machine learning methods to uncover sub-phenotypes within HFpEF in an unbiased manner based on clinical features, echocardiographic findings, and biomarker levels. We synthesized the literature and propose three broad phenotypes: (1) young, with few comorbidities, usually obese and with low natriuretic peptide levels, (2) obese with substantive cardiometabolic burden and comorbidities and impaired ventricular relaxation, (3) old, multimorbid, with high rates of atrial fibrillation, renal and coronary artery disease, chronic obstructive pulmonary disease, and left ventricular hypertrophy. We also propose potential therapeutic strategies for these phenotypes.

摘要

目的综述

射血分数保留的心力衰竭(HFpEF)给社会和医疗保健带来了巨大负担。缺乏有效的治疗方法可能是由于 HFpEF 存在显著的异质性。在这篇综述中,我们根据潜在的合并症或病因定义了各种表型,讨论了通过新方法得出的表型,并探讨了治疗靶点。

最新发现

一些研究使用机器学习方法,根据临床特征、超声心动图发现和生物标志物水平,以无偏倚的方式揭示 HFpEF 中的亚表型。我们综合了文献,并提出了三种广泛的表型:(1)年轻,合并症少,通常肥胖,利钠肽水平低,(2)肥胖,有实质性的心血管代谢负担和合并症,心室舒张功能障碍,(3)年老,多病,房颤、肾功能不全、冠状动脉疾病、慢性阻塞性肺疾病和左心室肥厚的发生率高。我们还为这些表型提出了潜在的治疗策略。

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