Clinical Phenotypes of Intracranial Suppurative Infections Identified by Unsupervised Machine Learning

Background: Intracranial suppurative infections (ISIs) are conventionally classified by anatomical compartment—brain abscess, subdural empyema, extradural empyema—a taxonomy that captures neither the heterogeneity of routes of infection nor the diversity of care pathways. No previous study has examined whether unsupervised learning can redefine clinically meaningful phenotypes of ISI beyond conventional anatomical categories, and none has done so in an African cohort. Methods: We analysed 150 consecutive observations (148 patients) of ISI managed at Bouaké University Hospital, C？te d’Ivoire, between 2016 and 2026. In the primary analysis, nineteen pre-therapeutic variables spanning host factors, symptom-to-admission delay, neurological severity, grouped portal of entry, lesion type and lesion burden were entered into a factor analysis of mixed data (FAMD); the origin of the suppuration, almost collinear with the portal of entry, was removed, and the specification including both was kept as a sensitivity analysis. Phenotypes were derived by K-means clustering on the retained factor coordinates, with Ward clustering on the same Euclidean coordinates and partitioning around medoids (PAM) on Gower distances as concordance checks. The reproducibility of the whole pipeline was assessed by repeated nested cross-validation, imputation, FAMD and K-means being re-estimated in every training set. In-hospital mortality and functional status were analysed as separate outcomes, and the incremental prognostic value of phenotype beyond GCS and age was assessed with optimism correction by bootstrap (1000 replicates). Results: In the primary non-redundant specification, a four-class solution was retained (silhouette 0.296). Its concordance with the dual-coded partition was moderate (adjusted Rand index [ARI] 0.325), and the frontal-interhemispheric sinogenic subdural empyema phenotype was the most robust (28 of 33 observations recovered). PAM on Gower distances identified four groups of 48, 36, 34 and 32 observations (silhouette 0.312; ARI with FAMD-K-means 0.671). Out-of-sample agreement of the complete pipeline was 87.6% ± 4.8% (mean ARI 0.79 ± 0.09). The dual-coded specification, used for detailed characterisation, separated community-acquired brain abscess of ENT-dental or cryptogenic origin (n = 54), delayed post-traumatic suppuration (n = 33; median delay 29 days), sinogenic subdural empyema (n = 33; craniotomy 91%, external ventricular drainage 24%) and post-operative suppuration (n =