RESUMO
BACKGROUND: Early treatment for Crohn's disease (CD) with immunomodulators and/or anti-TNF agents improves outcomes in comparison to a slower 'step up' algorithm. However, there remains a limited ability to identify those who would benefit most from early intensive therapy. AIM: To develop a validated, individualised, web-based tool for patients and clinicians to visualise individualised risks for developing Crohn's disease complications. METHODS: A well-characterised cohort of adult patients with CD was analysed. Available data included: demographics; clinical characteristics; serologic immune responses; NOD2 status; time from diagnosis to complication; and medication exposure. Cox proportional analyses were performed to model the probability of developing a CD complication over time. The Cox model was validated externally in two independent CD cohorts. Using system dynamics analysis (SDA), these results were transformed into a simple graphical web-based display to show patients their individualised probability of developing a complication over a 3-year period. RESULTS: Two hundered and forty three CD patients were included in the final model of which 142 experienced a complication. Significant variables in the multivariate Cox model included small bowel disease (HR 2.12, CI 1.05-4.29), left colonic disease (HR 0.73, CI 0.49-1.09), perianal disease (HR 4.12, CI 1.01-16.88), ASCA (HR 1.35, CI 1.16-1.58), Cbir (HR 1.29, CI 1.07-1.55), ANCA (HR 0.77, CI 0.62-0.95), and the NOD2 frameshift mutation/SNP13 (HR 2.13, CI 1.33-3.40). The Harrell's C (concordance index for predictive accuracy of the model) = 0.73. When applied to the two external validation cohorts (adult n = 109, pediatric n = 392), the concordance index was 0.73 and 0.75, respectively, for adult and pediatric patients. CONCLUSIONS: A validated, web-based tool has been developed to display an individualised predicted outcome for adult patients with Crohn's disease based on clinical, serologic and genetic variables. This tool can be used to help providers and patients make personalised decisions about treatment options.