Development and validation of risk matrices concerning ulcerative colitis outcomes - Bayesian network analysis.

Introduction: Ulcerative colitis (UC) is a chronic inflammatory disease often accompanied by severe and distressing symptoms that, in some patients, might require a surgical intervention (colectomy). This study aimed at determining the risk of experiencing progressive disease or requiring colectomy.

Material and Methods: This was a multicenter study: patients' data (n=1481) was retrieved from the Portuguese database of inflammatory bowel disease patients. Bayesian networks and logistic regression were used to build risk matrices concerning the outcomes of interest.

Results: The derivation cohort included a total of 1210 patients, of which 6% required a colectomy and 37% had progressive disease (over a median follow-up period of 12 years). The risk matrices show that previously hospitalized patients with extensive disease who are not on immunomodulators and who are refractory to corticosteroid treatment are the ones at the highest risk of undergoing a colectomy (88%), whereas male patients with extensive disease and less than 40 years old at diagnosis are the ones at the highest risk of experiencing progressive disease (72%). These results were internally- and externally-validated, and the AUC (area under the curve) of the ROC (receiver operating characteristic) analysis for the derivation cohort yielded a high discriminative power (92% for colectomy and 72% for progressivedisease).

Conclusions: This study allowed the construction of risk matrices that can be used to accurately predict a UC patient's likelihood of requiring a colectomy or of facing progressive disease and can be used to individualize therapeutic strategies.