Identification of clinical-biological endophenotypes in patients with eosinophilic granulomatosis with polyangiitis using unsupervised machine learning: a real-world study.

backgroundEosinophilic granulomatosis with polyangiitis (EGPA) is a systemic vasculitis characterized by high clinical heterogeneity. The conventional binary classification based on antineutrophil cytoplasmic antibody (ANCA) status inadequately captures this heterogeneity and provides limited guidance for individualised treatment decisions. This study aimed to identify and validate clinical-biological endophenotypes in EGPA using unsupervised machine learning methods based on multidimensional real-world clinical data, to inform precision medicine approaches.

methodsIn this retrospective single-centre observational study, we included 205 patients diagnosed with EGPA between January 2015 and December 2023 at China-Japan Friendship Hospital. Comprehensive data on demographics, clinical manifestations, laboratory tests, imaging features, and treatment information were systematically collected. Consensus clustering combined with K-means algorithm was applied to identify patient subgroups based on 14 core features, including peripheral blood eosinophil count, fractional exhaled nitric oxide (FeNO), serum total IgE, C-reactive protein (CRP), ANCA status, and multi-system involvement. Principal component analysis was used for dimensionality reduction and visualisation. Differences in clinical characteristics, laboratory parameters, imaging patterns, and treatment responses were compared among the subgroups. An online prediction tool was developed and validated based on the clustering results.

resultsConsensus clustering analysis identified three stable endophenotypes (optimal cluster number k = 3). Subgroup 1 (n = 78, 38.0%): Eosinophilic-Airway Inflammation Type, characterized by markedly elevated eosinophil count (6.10 × 10⁹/L, IQR: 5.10-8.00), FeNO (79 ppb, IQR: 55-112) and serum total IgE (798 IU/mL, IQR: 602-980), predominant respiratory symptoms, with ground-glass opacities and consolidation as main findings on chest high-resolution computed tomography, and among patients who received mepolizumab, a partial remission rate of 89.7% was observed. Subgroup 2 (n = 65, 31.7%): ANCA-Systemic Vasculitis Type, defined by a significantly higher ANCA positivity rate (26.2%) compared to the other subgroups, significant systemic inflammatory response (CRP: 33.5 mg/L, IQR: 22.5-48.5) and frequent peripheral nervous system involvement (76.9%), receiving higher initial glucocorticoid doses. Subgroup 3 (n = 62, 30.2%): Paucieosinophilic-Multi-Organ Damage Type, presenting with relatively low eosinophil-related indices but higher proportions of skin, gastrointestinal, and cardiac involvement, commonly showing fibrotic changes on imaging, and with a numerically lower observed overall response rate to available biologics (51.6%). The developed online prediction tool showed good performance in an independent validation cohort (overall accuracy 90.0%, Kappa agreement with expert judgement 0.850).

conclusionUsing unsupervised machine learning on real-world data, this study successfully identified three distinct clinical-biological endophenotypes in EGPA, revealing different underlying pathophysiological mechanisms and treatment response patterns, and developed a clinically translatable prediction tool. This endophenotype framework and tool may help inform individualised treatment strategies and provide an exploratory data-driven classification basis and decision support for future precision medicine research in EGPA.