Machine-learning approach to dissect the clinical heterogeneity of IBD-associated fatigue

Objective: Extreme and persistent fatigue affects >50% of individuals with inflammatory bowel disease (IBD), with similar prevalence across many common immune-mediated inflammatory diseases (IMIDs). Despite its ubiquity, human scientific studies have yet to fully explain the mechanistic basis of this complex symptom. One fundamental reason is our inability to account for the clinical heterogeneity and multifactorial nature of fatigue. Methods and analysis: We present the conceptual machine-learning (ML) framework to dissect fatigue using one of the largest prospectively captured, real-world patient-reported outcome (PROs) on well-being from three contemporaneous cohorts (2020-present), totalling 2970 responses from 2290 participants across the UK and internationally, including non-IBD controls with 100 lines of clinical metadata. In parallel, our patient public involvement group performed thematic analysis of this PRO dataset, which identified fatigue as a key research priority (www.musicstudy.uk). Results: We systematically defined the (1) threshold of fatigue as our primary outcome (≥10/14 fatigue days in 1604 patients (1151 responses in active disease and 1061 responses in remission; some patients measured longitudinally; median fatigue days 14 vs 7, respectively; p