Development of Clinical Prediction Models for Surgery and Complications in Crohn's Disease.

Background and Aims: Crohn's disease-related complications account for a substantial proportion of inflammatory bowel disease-associated health care expenditure. Identifying patients at risk for complications may allow for targeted use of early therapeutic interventions to offset this natural course. We aimed to develop risk prediction models for Crohn's disease-related surgery and complications.

Methods: Using data from the Randomised Evaluation of an Algorithm for Crohn's Disease cluster-randomised clinical Trial [REACT], which involved 1898 patients from 40 community practices, separate prediction models were derived and internally validated for predicting Crohn's disease-related surgery and disease-related complications [defined as the first disease-related surgery, hospitalisation, or complication within 24 months]. Model performance was assessed in terms of discrimination and calibration, decision curves, and net benefit analyses.

Results: There were 130 [6.8%] disease-related surgeries and 504 [26.6%] complications during the 24-month follow-up period. Selected baseline predictors of surgery included age, gender, disease location, Harvey-Bradshaw Index [HBI] score, stool frequency, antimetabolite or 5-aminosalicylate use, and the presence of a fistula, abscess, or abdominal mass. Selected predictors of complications included those same factors for surgery, plus corticosteroid or anti-tumour necrosis factor use, but excluded 5-aminosalicylate use. Discrimination ability, as measured by validated c-statistics, was 0.70 and 0.62 for the surgery and complication models, respectively. Score charts and nomograms were developed to facilitate future risk score calculation.

Conclusions: Separate risk prediction models for Crohn's disease-related surgery and complications were developed using clinical trial data involving community gastroenterology practices. These models could be used to guide Crohn's disease management. External validation is warranted.