Matsuoka Yuki, Sotomi Yohei, Nakatani Daisaku, Okada Katsuki, Sunaga Akihiro, Kida Hirota, Sato Taiki, Sakamoto Daisuke, Kitamura Tetsuhisa, Komukai Sho, Seo Masahiro, Yano Masamichi, Hayashi Takaharu, Nakagawa Akito, Nakagawa Yusuke, Tamaki Shunsuke, Yasumura Yoshio, Yamada Takahisa, Hikoso Shungo, Sakata Yasushi
Department of Cardiovascular Medicine Osaka University Graduate School of Medicine Osaka Japan.
Department of Medical Informatics Osaka University Graduate School of Medicine Osaka Japan.
J Am Heart Assoc. 2025 Feb 4;14(3):e037567. doi: 10.1161/JAHA.124.037567. Epub 2025 Feb 3.
Using machine learning for the phenotyping of patients with heart failure with preserved ejection fraction (HFpEF) has emerged as a novel approach to understanding the pathophysiology and stratifying the patients. Our objective is to perform phenotyping of patients with HFpEF in stable phase and to investigate the phenotypic trajectory from acute worsening phase to stable phase.
The present study is a post hoc analysis of the PURSUIT-HFpEF (Prospective Multicenter Observational Study of Patients with Heart Failure with Preserved Ejection Fraction) study. We applied the latent class analysis to the discharge data of patients hospitalized for acute decompensated heart failure.
We finally included patient data of 1100 cases and 63 features in the latent class analysis. All patients were subclassified into 5 phenogroups as follows: Phenotype 1, characterized by better renal function and lower NT-proBNP (N-terminal pro-B-type natriuretic peptide) level [N=325 (29.5%)]; Phenotype 2, higher blood pressure, sinus rhythm, and poor renal function. [N=242 (22.0%)]; Phenotype 3, higher prevalence of atrial fibrillation, higher tricuspid pressure gradient, and lower tricuspid annular plane systolic excursion [N=214 (19.5%)]; Phenotype 4, higher C-reactive protein level and higher tricuspid pressure gradient [N=245 (22.3%)]; and Phenotype 5, poor nutritional status, poor renal function, and higher NT-proBNP level [N=74 (6.7%)]. A particular phenotype observed at the time of discharge was correlated with a distinct phenotype of acute worsening.
We identified 5 distinct stable phase phenotypes of the patients with HFpEF from the data at discharge. A specific phenotype at discharge was associated with a particular phenotype of acute worsening. This grouping can be a basis for future precision medicine of patients with HFpEF.
URL: https://www.umin.ac.jp/ctr/; Unique identifier: UMIN000021831.
利用机器学习对射血分数保留的心力衰竭（HFpEF）患者进行表型分析已成为一种理解病理生理学和对患者进行分层的新方法。我们的目的是对处于稳定期的HFpEF患者进行表型分析，并研究从急性加重期到稳定期的表型轨迹。
本研究是对PURSUIT-HFpEF（射血分数保留的心力衰竭患者前瞻性多中心观察性研究）研究的事后分析。我们将潜在类别分析应用于因急性失代偿性心力衰竭住院患者的出院数据。
我们最终在潜在类别分析中纳入了1100例患者的数据和63个特征。所有患者被分为以下5个表型组：表型1，以更好的肾功能和更低的N末端B型利钠肽原（NT-proBNP）水平为特征[N = 325（29.5%）]；表型2，高血压、窦性心律和肾功能差[N = 242（22.0%）]；表型3，房颤患病率更高、三尖瓣压力梯度更高和三尖瓣环平面收缩期位移更低[N = 214（19.5%）]；表型4，C反应蛋白水平更高和三尖瓣压力梯度更高[N = 245（22.3%）]；以及表型5，营养状况差、肾功能差和NT-proBNP水平更高[N = 74（6.7%）]。出院时观察到的特定表型与急性加重的不同表型相关。
我们从出院数据中识别出HFpEF患者的5种不同的稳定期表型。出院时的特定表型与急性加重的特定表型相关。这种分组可为未来HFpEF患者的精准医学提供依据。
