Phenotypic subtypes of fibrotic hypersensitivity pneumonitis identified by machine learning consensus clustering analysis

Abstract

BackgroundPatients with fibrotic hypersensitivity pneumonitis (f-HP) have varied clinical and radiologic presentations whose associated phenotypic outcomes have not been previously described. We conducted a study to evaluate mortality and lung transplant (LT) outcomes among clinical clusters of f-HP as characterized by an unsupervised machine learning approach.MethodsConsensus cluster analysis was performed on a retrospective cohort of f-HP patients diagnosed according to recent international guideline. Demographics, antigen exposure, radiologic, histopathologic, and pulmonary function findings along with comorbidities were included in the cluster analysis. Cox proportional-hazards regression was used to assess mortality or LT risk as a combined outcome for each cluster.ResultsThree distinct clusters were identified among 336 f-HP patients. Cluster 1 (n = 158, 47%) was characterized by mild restriction on pulmonary function testing (PFT). Cluster 2 (n = 46, 14%) was characterized by younger age, lower BMI, and a higher proportion of identifiable causative antigens with baseline obstructive physiology. Cluster 3 (n = 132, 39%) was characterized by moderate to severe restriction. When compared to cluster 1, mortality or LT risk was lower in cluster 2 (hazard ratio (HR) of 0.42; 95% CI, 0.21–0.82; P = 0.01) and higher in cluster 3 (HR of 1.76; 95% CI, 1.24–2.48; P = 0.001).ConclusionsThree distinct phenotypes of f-HP with unique mortality or transplant outcomes were found using unsupervised cluster analysis, highlighting improved mortality in fibrotic patients with obstructive physiology and identifiable antigens.