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Purpose <p>Infective endocarditis (IE) is a heterogeneous disease with diverse clinical presentations, microbiological profiles, and outcomes. Conventional classification systems may not fully capture this complexity. We aimed to identify data-driven clinical phenotypes in IE using an unsupervised machine learning (ML) approach.</p> Methods <p>In this multicenter retrospective study, 335 patients with definite IE from three hospitals were included. A total of 55 clinical, microbiological, and echocardiographic variables were analyzed. Missing data were imputed using the MissForest algorithm. Clustering was performed using spectral clustering based on a Gower dissimilarity matrix and supported by a Bayesian non-parametric approach. Associations between cluster assignment and clinical outcomes were assessed using regression analyses.</p> Results <p>Two distinct clusters were identified, reflecting different clinical profiles. Cluster 1 was characterized by native valve endocarditis, a higher burden of comorbidities, inflammatory activation, and larger vegetations. Cluster 2 was characterized by prosthetic valve and device-related IE, higher rates of atrial fibrillation, and larger cardiac dimensions. Cluster assignment was not significantly associated with in-hospital mortality in either unadjusted or adjusted analyses. Increasing age emerged as an independent predictor of in-hospital mortality. In the adjusted model, Cluster 2 was independently associated with longer hospital stay, whereas no significant difference was observed in ICU stay.</p> Conclusion <p>Unsupervised ML identified two clinically distinct phenotypes in IE, reflecting disease heterogeneity. Although these phenotypes were not associated with major clinical outcomes, they provide a data-driven framework for understanding disease heterogeneity and may contribute to future personalized approaches in the management of IE. Larger prospective studies are needed to determine their clinical applicability.</p>

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Data-driven phenotyping of infective endocarditis using unsupervised machine learning: toward a personalized approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月25日

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  • Murat Karaçam,
  • Vedat Cicek,
  • Barkın Kültürsay,
  • Almina Erdem,
  • Seda Tanyeri Üzel,
  • Şeref Berk Tuncer,
  • Tezel Kovancı,
  • Feryat İgit,
  • Çağdaş Bulus,
  • Mert Babaoğlu,
  • Muhammet Bulut,
  • Özgür Can Usta,
  • Burak Tay,
  • Muhammed Ensar Aslan,
  • Mustafa Kamil Yemiş,
  • Ahmet Öz,
  • Tufan Çınar,
  • Ulas Bagci

摘要

Purpose

Infective endocarditis (IE) is a heterogeneous disease with diverse clinical presentations, microbiological profiles, and outcomes. Conventional classification systems may not fully capture this complexity. We aimed to identify data-driven clinical phenotypes in IE using an unsupervised machine learning (ML) approach.

Methods

In this multicenter retrospective study, 335 patients with definite IE from three hospitals were included. A total of 55 clinical, microbiological, and echocardiographic variables were analyzed. Missing data were imputed using the MissForest algorithm. Clustering was performed using spectral clustering based on a Gower dissimilarity matrix and supported by a Bayesian non-parametric approach. Associations between cluster assignment and clinical outcomes were assessed using regression analyses.

Results

Two distinct clusters were identified, reflecting different clinical profiles. Cluster 1 was characterized by native valve endocarditis, a higher burden of comorbidities, inflammatory activation, and larger vegetations. Cluster 2 was characterized by prosthetic valve and device-related IE, higher rates of atrial fibrillation, and larger cardiac dimensions. Cluster assignment was not significantly associated with in-hospital mortality in either unadjusted or adjusted analyses. Increasing age emerged as an independent predictor of in-hospital mortality. In the adjusted model, Cluster 2 was independently associated with longer hospital stay, whereas no significant difference was observed in ICU stay.

Conclusion

Unsupervised ML identified two clinically distinct phenotypes in IE, reflecting disease heterogeneity. Although these phenotypes were not associated with major clinical outcomes, they provide a data-driven framework for understanding disease heterogeneity and may contribute to future personalized approaches in the management of IE. Larger prospective studies are needed to determine their clinical applicability.