Development and External Validation of a Machine Learning Model for Pre-Endoscopic Prediction of Montreal Disease Extent (E1/E2/E3) in Ulcerative... | NCT07721987

NCT07721987

Overview

NCT ID: NCT07721987
Lead Sponsor: Beijing University of Chinese MedicineOTHER
Official Title: Development and External Validation of a Machine Learning Model for Pre-Endoscopic Prediction of Montreal Disease Extent (E1/E2/E3) in Ulcerative Colitis
Brief Summary: This multicenter retrospective observational study aims to develop and externally validate a machine learning model that predicts Montreal ulcerative colitis (UC) disease extent (E1: limited/proctitis; E2: left-sided; E3: extensive) using pre-endoscopic clinical information, including symptoms, signs, and laboratory tests. The model is intended to assist clinical assessment before endoscopic confirmation and is not designed to replace colonoscopy or histopathology. Data from development centers (Centers A and B) will be used for model development with nested cross-validation; data from independent external centers (Centers C and D) will be used for external validation only.
Conditions: Ulcerative Colitis (Disorder)

Ages Eligible for Study: Adult (18-64)Older Adult (65+)
Sexes Eligible for Study: All
Accepts Healthy Volunteers?: No
Eligibility Criteria: Inclusion Criteria:

Confirmed diagnosis of ulcerative colitis by endoscopy ± histopathology.

Montreal disease extent classifiable as E1 (limited), E2 (intermediate/left-sided), or E3 (extensive) and mapped to study labels 1/2/3.

Pre-endoscopic baseline data available: demographics, symptoms, signs, and laboratory tests used as model predictors.

Predictors collected before or independent of endoscopic findings used for the outcome label (endoscopic extent not used as input).

One index visit per patient (duplicate/non-index visits excluded).

Exclusion Criteria:

Non-UC diagnosis or Montreal extent not assignable.

Missing patient identifier/linkage or unlabelable outcome.

Incomplete endoscopic gold standard for Montreal extent classification.

Duplicate or non-index visits.

Critical predictor data unavailable and not handled by the prespecified modeling pipeline