Advancements in Incident Heart Failure Risk Prediction and Screening Tools

被引:0
作者
Matasic, Daniel S. [1 ]
Zeitoun, Ralph [1 ]
Fonarow, Gregg C. [2 ]
Razavi, Alexander C. [3 ]
Blumenthal, Roger S. [1 ]
Gulati, Martha [4 ]
机构
[1] Johns Hopkins Univ, Ciccarone Ctr Prevent Cardiovasc Dis, Div Cardiol, Baltimore, MD USA
[2] Univ Calif Los Angeles, Div Cardiol, Los Angeles, CA USA
[3] Emory Univ, Sch Med, Div Cardiol, Atlanta, GA USA
[4] Barbra Streisand Womens Heart Ctr, Cedars Sinai Smidt Heart Inst, Los Angeles, CA 90048 USA
关键词
heart failure; risk prediction; preventive cardiology; DISEASE; PROFILE; MODELS;
D O I
10.1016/j.amjcard.2024.07.014
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Heart failure (HF) is a major cause of mortality and morbidity in the United States that carries substantial healthcare costs. Multiple risk prediction models and strategies have been developed over the past 30 years with the aim of identifying those at high risk of developing HF and of implementing preventive therapies effectively. This review highlights recent developments in HF risk prediction tools including emerging risk factors, innovative risk prediction models, and novel screening strategies from artificial intelligence to biomarkers. These developments allow more accurate prediction, but their impact on clinical outcomes remains to be investigated. Implementation of these risk models in clinical practice is a considerable challenge, but HF risk prediction tools offer a promising opportunity to improve outcomes while maintaining value. (c) 2024 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY- NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) (Am J Cardiol 2024;227:105-110) - 110)
引用
收藏
页码:105 / 110
页数:6
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