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基于机器学习聚类分析的心衰伴射血分数保留的表型分析:预后和治疗意义。

Phenomapping Heart Failure with Preserved Ejection Fraction Using Machine Learning Cluster Analysis: Prognostic and Therapeutic Implications.

机构信息

University of Rennes, CHU Rennes, INSERM, LTSI-UMR 1099, Rennes F-35000, France.

Cardiology Department, Wroclaw Medical University, Wroclaw, Poland.

出版信息

Heart Fail Clin. 2021 Jul;17(3):499-518. doi: 10.1016/j.hfc.2021.02.010.

DOI:10.1016/j.hfc.2021.02.010
PMID:34051979
Abstract

Heart failure with preserved ejection fraction (HFpEF) is characterized by a high rate of hospitalization and mortality (up to 84% at 5 years), which are similar to those observed for heart failure with reduced ejection fraction (HFrEF). These epidemiologic data claim for the development of specific and innovative therapies to reduce the burden of morbidity and mortality associated with this disease. Compared with HFrEF, which is due to a primary myocardial damage (eg ischemia, cardiomyopathies, toxicity), a heterogeneous etiologic background characterizes HFpEF. The authors discuss these phenotypes and specificities for defining therapeutic strategies that could be proposed according to phenotypes.

摘要

射血分数保留的心力衰竭(HFpEF)的特征是高住院率和死亡率(高达 5 年内的 84%),与射血分数降低的心力衰竭(HFrEF)相似。这些流行病学数据要求开发特定的和创新的治疗方法,以降低与这种疾病相关的发病率和死亡率。与主要心肌损伤(如缺血、心肌病、毒性)导致的 HFrEF 不同,HFpEF 的病因背景具有异质性。作者讨论了这些表型和特异性,以确定根据表型可以提出的治疗策略。

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