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Data-driven phenotyping of infective endocarditis using unsupervised machine learning: toward a personalized approach
Murat Karaçam1, Vedat Cicek2, Barkın Kültürsay3
1Department of Cardiology, Bitlis State Hospital, Beş Minare, Selahattin Eyyübi Caddesi, No:160, Merkez, Bitlis, Turkey. mrtkrcm.5@gmail.com.
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.