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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
MURAT  KARAÇAMMURAT KARAÇAM1*Barkın  KÜLTÜRSAYBarkın KÜLTÜRSAY2Deniz  MUTLUDeniz MUTLU3Seda  TANYERİSeda TANYERİ4Azmican  KAYAAzmican KAYA4Suleyman Cagan  EfeSuleyman Cagan Efe4Cem  DOĞANCem DOĞAN4Gülümser  Sevgin HALİLGülümser Sevgin HALİL4Özgür  Yaşar AKBALÖzgür Yaşar AKBAL4Kaan  KIRALİKaan KIRALİ4Rezzan  Deniz ACARRezzan Deniz ACAR4
  • 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

The final, formatted version of the article will be published soon.

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

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