Personalized Management for Heart Failure with Preserved Ejection Fraction

被引:6
作者
Lin, Chang-Yi [1 ]
Sung, Heng-You [1 ]
Chen, Ying-Ju [2 ]
Yeh, Hung-I. [1 ,3 ]
Hou, Charles Jia-Yin [3 ]
Tsai, Cheng-Ting [1 ,4 ]
Hung, Chung-Lieh [1 ,5 ]
机构
[1] MacKay Mem Hosp, Dept Internal Med, Div Cardiol, 92,Sec 2,Zhongshan N Rd, Taipei 10449, Taiwan
[2] MacKay Mem Hosp, Telemed Ctr, Taipei 10449, Taiwan
[3] Mackay Med Coll, Dept Internal Med, New Taipei City 25245, Taiwan
[4] Mackay Jr Coll Med Nursing & Management, New Taipei City 25245, Taiwan
[5] Mackay Med Coll, Inst Biomed Sci, New Taipei City 25245, Taiwan
来源
JOURNAL OF PERSONALIZED MEDICINE | 2023年 / 13卷 / 05期
关键词
heart failure with preserved ejection fraction; artificial intelligence; phenotype; cluster; machine learning; latent class analysis; PATHOGENESIS; ALDOSTERONE; NEPRILYSIN; PHENOTYPE; PROGNOSIS; OBESITY;
D O I
10.3390/jpm13050746
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome with multiple underlying mechanisms and comorbidities that leads to a variety of clinical phenotypes. The identification and characterization of these phenotypes are essential for better understanding the precise pathophysiology of HFpEF, identifying appropriate treatment strategies, and improving patient outcomes. Despite accumulating data showing the potentiality of artificial intelligence (AI)-based phenotyping using clinical, biomarker, and imaging information from multiple dimensions in HFpEF management, contemporary guidelines and consensus do not incorporate these in daily practice. In the future, further studies are required to authenticate and substantiate these findings in order to establish a more standardized approach for clinical implementation.
引用
收藏
页数:12
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