ORIGINAL RESEARCH article
Front. Cardiovasc. Med.
Sec. Heart Failure and Transplantation
Volume 12 - 2025 | doi: 10.3389/fcvm.2025.1669538
This article is part of the Research TopicTransforming Care in Heart Failure and Cardiomyopathies: Emerging Insights and TreatmentsView all 10 articles
From Patterns to Prognosis: Machine Learning–Derived Clusters in Advanced Heart Failure
Provisionally accepted- 1TC Saglik Bakanligi Bitlis Devlet Hastanesi, Bitlis, Türkiye
- 2TC Saglik Bakanligi Tunceli Devlet Hastanesi, Tunceli, Türkiye
- 3Minneapolis Heart Institute Foundation, Minneapolis, United States
- 4TC Saglik Bakanligi Kosuyolu Yuksek Ihtisas Egitim ve Arastirma Hastanesi, Istanbul, Türkiye
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Introduction: Advanced heart failure (HF) is a clinically heterogeneous condition with poor prognosis, and traditional classification systems often fail to capture the complexity needed for personalized care. This study aimed to identify clinically meaningful phenotypic subgroups among patients with advanced HF using unsupervised machine learning and to evaluate their association with long-term outcomes. Methods: A retrospective analysis was conducted on 524 patients with advanced HF who underwent comprehensive clinical, echocardiographic, hemodynamic, and cardiopulmonary exercise assessments. Using k-means clustering on standardized, multidimensional data, two distinct phenotypes were identified. The primary composite outcome was defined as all-cause mortality, left ventricular assist device implantation, or heart transplantation. Associations between cluster assignment and outcomes were evaluated using Kaplan–Meier analysis and Cox proportional hazards regression. Results: The first cluster, representing patients with relatively preserved hemodynamics and functional status, was associated with a more favorable prognosis, while the second cluster included older individuals with significant biventricular dysfunction, higher pulmonary pressures, and poorer exercise capacity. These patients experienced a markedly higher rate of the composite outcome over a median follow-up of 2.4 years, with Cluster 2 showing a significantly increased risk (hazard ratio [HR]: 3.84; 95% CI: 2.72–5.43; p < 0.001). Conclusion: Machine learning–based clustering revealed two distinct phenotypes in advanced HF with differing clinical features and prognoses. This approach may enhance risk stratification and inform individualized therapeutic strategies in this high-risk population.
Keywords: Advanced heart failure, phenotyping, unsupervised clustering, machine learning, risk stratification
Received: 19 Jul 2025; Accepted: 07 Oct 2025.
Copyright: © 2025 KARAÇAM, KÜLTÜRSAY, MUTLU, TANYERİ, KAYA, Efe, DOĞAN, HALİL, AKBAL, KIRALİ and ACAR. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: MURAT KARAÇAM, mrtkrcm.5@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.