AI Insight
Researchers developed a machine learning model that analyzes electronic health records to predict inflammatory bowel disease (IBD) months to years before clinical diagnosis. The model achieved high accuracy (AUROC 0.969) at identifying future IBD cases among symptomatic patients, with a median lead time of 15-20 months before actual diagnosis. The system successfully flagged 84-89% of patients who initially had normal or inconclusive colonoscopy results but later developed IBD, and maintained strong performance when tested at an external hospital serving a different patient population.
Why it matters
Early identification of IBD could reduce diagnostic delays that are associated with disease complications and allow earlier intervention. This approach may enable prospective studies of early IBD development and biology, and could help prioritize patients for closer monitoring or additional testing, though prospective trials are needed to determine if earlier detection actually improves patient outcomes.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
BACKGROUND & AIMS: Inflammatory bowel disease (IBD) is often diagnosed months to years after symptoms begin, and diagnostic delay is associated with complications. We tested whether longitudinal machine learning using electronic health record (EHR) data can identify incident IBD before diagnosis, and quantified lead time and deployment burden. METHODS: We studied adults with an IBD-compatible symptom, sign, or finding at UCSF (2012-2024). Incident cases were confirmed by structured filtering and validated large language model note review. We trained a state space model (Mamba) and comparators on pre-diagnostic EHR data, excluding diagnostic-workup features, and assessed discrimination at 6- and 12-month landmarks. We externally tested Mamba without recalibration at ZSFG, a safety-net hospital serving a demographically and socioeconomically distinct population, and simulated diagnosis-code-only deployment at UCSF. RESULTS: Six months after presentation, among patients still undiagnosed (67% of UCSF test cases), Mamba achieved an AUROC of 0.969 (95% CI, 0.928-0.999) at UCSF and 0.965 (0.952-0.976) at ZSFG. Median lead time was 20 months at UCSF and 15 months at ZSFG. Among future cases with an initially normal colonoscopy (N=32) or a histological finding of acute inflammation without chronicity (N=63), the model flagged 84% and 89% of these patients, respectively, before those procedures, indicating the potential for pre-diagnostic detection. Among 358,886 symptomatic patients not used for training (60 incident cases), the diagnosis-code-only model achieved an AUROC of 0.926; reviewing 20 alerts/month identified 34 cases (PPV, 1.09%; 64-fold above baseline). CONCLUSIONS: Longitudinal machine learning can identify increased IBD risk months to years before diagnosis and may define a population for prospective studies of early IBD biology. Prospective evaluation should determine whether earlier identification reduces diagnostic delay.