Phenotypic Trajectories From Acute to Stable Phase in Heart Failure With Preserved Ejection Fraction: Insights From the PURSUIT‐HFpEF Study
Background 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 ph...
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2025-02-01
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| Series: | Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease |
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| Online Access: | https://www.ahajournals.org/doi/10.1161/JAHA.124.037567 |
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| author | Yuki Matsuoka Yohei Sotomi Daisaku Nakatani Katsuki Okada Akihiro Sunaga Hirota Kida Taiki Sato Daisuke Sakamoto Tetsuhisa Kitamura Sho Komukai Masahiro Seo Masamichi Yano Takaharu Hayashi Akito Nakagawa Yusuke Nakagawa Shunsuke Tamaki Yoshio Yasumura Takahisa Yamada Shungo Hikoso Yasushi Sakata |
| author_facet | Yuki Matsuoka Yohei Sotomi Daisaku Nakatani Katsuki Okada Akihiro Sunaga Hirota Kida Taiki Sato Daisuke Sakamoto Tetsuhisa Kitamura Sho Komukai Masahiro Seo Masamichi Yano Takaharu Hayashi Akito Nakagawa Yusuke Nakagawa Shunsuke Tamaki Yoshio Yasumura Takahisa Yamada Shungo Hikoso Yasushi Sakata |
| author_sort | Yuki Matsuoka |
| collection | DOAJ |
| description | Background 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. Methods 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. Results 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. Conclusions 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. Registration URL: https://www.umin.ac.jp/ctr/; Unique identifier: UMIN000021831. |
| format | Article |
| id | doaj-art-8d779365cb174218bf4e0194c0237213 |
| institution | Kabale University |
| issn | 2047-9980 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Wiley |
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| series | Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease |
| spelling | doaj-art-8d779365cb174218bf4e0194c02372132025-02-04T11:00:01ZengWileyJournal of the American Heart Association: Cardiovascular and Cerebrovascular Disease2047-99802025-02-0114310.1161/JAHA.124.037567Phenotypic Trajectories From Acute to Stable Phase in Heart Failure With Preserved Ejection Fraction: Insights From the PURSUIT‐HFpEF StudyYuki Matsuoka0Yohei Sotomi1Daisaku Nakatani2Katsuki Okada3Akihiro Sunaga4Hirota Kida5Taiki Sato6Daisuke Sakamoto7Tetsuhisa Kitamura8Sho Komukai9Masahiro Seo10Masamichi Yano11Takaharu Hayashi12Akito Nakagawa13Yusuke Nakagawa14Shunsuke Tamaki15Yoshio Yasumura16Takahisa Yamada17Shungo Hikoso18Yasushi Sakata19Department of Cardiovascular Medicine Osaka University Graduate School of Medicine Osaka JapanDepartment of Cardiovascular Medicine Osaka University Graduate School of Medicine Osaka JapanDepartment of Cardiovascular Medicine Osaka University Graduate School of Medicine Osaka JapanDepartment of Cardiovascular Medicine Osaka University Graduate School of Medicine Osaka JapanDepartment of Cardiovascular Medicine Osaka University Graduate School of Medicine Osaka JapanDepartment of Cardiovascular Medicine Osaka University Graduate School of Medicine Osaka JapanDepartment of Cardiovascular Medicine Osaka University Graduate School of Medicine Osaka JapanDepartment of Cardiovascular Medicine Osaka University Graduate School of Medicine Osaka JapanDepartment of Social and Environmental Medicine Osaka University Graduate School of Medicine Osaka JapanDivision of Biomedical Statistics, Department of Integrated Medicine, Graduate School of Medicine Osaka University Osaka JapanDivision of Cardiology Osaka General Medical Center Osaka JapanDivision of Cardiology Osaka Rosai Hospital Osaka JapanCardiovascular Division Osaka Police Hospital Osaka JapanDepartment of Cardiovascular Medicine Osaka University Graduate School of Medicine Osaka JapanDivision of Cardiology Kawanishi City Medical Center Amagasaki Hyogo JapanDepartment of Cardiology Rinku General Medical Center Osaka JapanDivision of Cardiology Amagasaki Chuo Hospital Amagasaki Hyogo JapanDivision of Cardiology Osaka General Medical Center Osaka JapanDepartment of Cardiovascular Medicine Osaka University Graduate School of Medicine Osaka JapanDepartment of Cardiovascular Medicine Osaka University Graduate School of Medicine Osaka JapanBackground 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. Methods 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. Results 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. Conclusions 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. Registration URL: https://www.umin.ac.jp/ctr/; Unique identifier: UMIN000021831.https://www.ahajournals.org/doi/10.1161/JAHA.124.037567HFpEFlatent class analysismachine learningphenotyping |
| spellingShingle | Yuki Matsuoka Yohei Sotomi Daisaku Nakatani Katsuki Okada Akihiro Sunaga Hirota Kida Taiki Sato Daisuke Sakamoto Tetsuhisa Kitamura Sho Komukai Masahiro Seo Masamichi Yano Takaharu Hayashi Akito Nakagawa Yusuke Nakagawa Shunsuke Tamaki Yoshio Yasumura Takahisa Yamada Shungo Hikoso Yasushi Sakata Phenotypic Trajectories From Acute to Stable Phase in Heart Failure With Preserved Ejection Fraction: Insights From the PURSUIT‐HFpEF Study Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease HFpEF latent class analysis machine learning phenotyping |
| title | Phenotypic Trajectories From Acute to Stable Phase in Heart Failure With Preserved Ejection Fraction: Insights From the PURSUIT‐HFpEF Study |
| title_full | Phenotypic Trajectories From Acute to Stable Phase in Heart Failure With Preserved Ejection Fraction: Insights From the PURSUIT‐HFpEF Study |
| title_fullStr | Phenotypic Trajectories From Acute to Stable Phase in Heart Failure With Preserved Ejection Fraction: Insights From the PURSUIT‐HFpEF Study |
| title_full_unstemmed | Phenotypic Trajectories From Acute to Stable Phase in Heart Failure With Preserved Ejection Fraction: Insights From the PURSUIT‐HFpEF Study |
| title_short | Phenotypic Trajectories From Acute to Stable Phase in Heart Failure With Preserved Ejection Fraction: Insights From the PURSUIT‐HFpEF Study |
| title_sort | phenotypic trajectories from acute to stable phase in heart failure with preserved ejection fraction insights from the pursuit hfpef study |
| topic | HFpEF latent class analysis machine learning phenotyping |
| url | https://www.ahajournals.org/doi/10.1161/JAHA.124.037567 |
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