医学
射血分数保留的心力衰竭
心力衰竭
射血分数
内科学
心脏病学
星团(航天器)
共病
人口
队列
药方
重症监护医学
计算机科学
药理学
环境卫生
程序设计语言
作者
Adrianne Waldman Casebeer,Libby Horter,Jennifer Hayden,Jeff Simmons,Thomas Evers
出处
期刊:Journal of Cardiovascular Medicine
[Ovid Technologies (Wolters Kluwer)]
日期:2020-09-15
卷期号:22 (1): 45-52
被引量:18
标识
DOI:10.2459/jcm.0000000000001116
摘要
Aims Approximately 50% of patients with heart failure have preserved (≥50%) ejection fraction (HFpEF). Improved understanding of the phenotypic heterogeneity of HFpEF might facilitate development of targeted therapies and interventions. Methods This retrospective study characterized a cohort of patients with HFpEF based on similar clinical profiles and evaluated 1-year heart failure related hospitalization. Enrolment, medical and pharmacy data were used to identify patients newly diagnosed with heart failure enrolled in a Medicare Advantage Prescription Drug or commercial healthcare plan. To identify only those patients with HFpEF, we used natural language processing techniques of ejection fraction values abstracted from a linked free-text clinical notes data source. The study population comprised 1515 patients newly identified with HFpEF between 1 January 2011 and 31 December 2015. Results Using unsupervised machine learning, we identified three distinguishable patient clusters representing different phenotypes: cluster-1 patients had the lowest prevalence of heart failure comorbidities and highest mean age; cluster-2 patients had higher prevalence of metabolic syndrome and pulmonary disease, despite younger mean age; and cluster-3 patients had higher prevalence of cardiac arrhythmia and renal disease. Cluster-3 had the highest 1-year heart failure related hospitalization rates. Within-cluster analysis, prior use of diuretics (cluster-1 and cluster-2) and age (cluster-2 and cluster-3) was associated with 1-year heart failure related hospitalization. Combination therapy was associated with decreased 1-year heart failure related hospitalization in cluster-1. Conclusion This study demonstrated that clustering can be used to characterize subgroups of patients with newly identified HFpEF, assess differences in heart failure related hospitalization rates at 1 year and suggest patient subtypes may respond differently to treatments or interventions.
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