ArticleBMC infectious diseases2026
Data-driven phenotyping of infective endocarditis using unsupervised machine learning: toward a personalized approach.
Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeInfective 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.
methodsIn 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.
resultsTwo 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.
conclusionUnsupervised 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.
Indexed as
Identifiers
42919191What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.