4.5 Article

A Novel 8-Predictors Signature to Predict Complicated Disease Course in Pediatric-onset Crohn's Disease: A Population-based Study

Journal

INFLAMMATORY BOWEL DISEASES
Volume -, Issue -, Pages -

Publisher

OXFORD UNIV PRESS INC
DOI: 10.1093/ibd/izad090

Keywords

inflammatory bowel disease; Crohn's disease; prognosis; complication; genetics; prediction

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By combining clinical, serological, and genetic factors, we developed a scoring system that can predict the disease progression of pediatric-onset Crohn's disease, which is valuable for guiding initial therapy.
The identification of patients at high risk of a disabling disease course would be invaluable in guiding initial therapy in Crohn's disease. We constructed a score that combines clinical, serological, and genetic factors able to predict the evolution of pediatric-onset inflammatory Crohn's disease to a complicated disease course. Background The identification of patients at high risk of a disabling disease course would be invaluable in guiding initial therapy in Crohn's disease (CD). Our objective was to evaluate a combination of clinical, serological, and genetic factors to predict complicated disease course in pediatric-onset CD. Methods Data for pediatric-onset CD patients, diagnosed before 17 years of age between 1988 and 2004 and followed more than 5 years, were extracted from the population-based EPIMAD registry. The main outcome was defined by the occurrence of complicated behavior (stricturing or penetrating) and/or intestinal resection within the 5 years following diagnosis. Lasso logistic regression models were used to build a predictive model based on clinical data at diagnosis, serological data (ASCA, pANCA, anti-OmpC, anti-Cbir1, anti-Fla2, anti-Flax), and 369 candidate single nucleotide polymorphisms. Results In total, 156 children with an inflammatory (B1) disease at diagnosis were included. Among them, 35% (n = 54) progressed to a complicated behavior or an intestinal resection within the 5 years following diagnosis. The best predictive model (PREDICT-EPIMAD) included the location at diagnosis, pANCA, and 6 single nucleotide polymorphisms. This model showed good discrimination and good calibration, with an area under the curve of 0.80 after correction for optimism bias (sensitivity, 79%, specificity, 74%, positive predictive value, 61%, negative predictive value, 87%). Decision curve analysis confirmed the clinical utility of the model. Conclusions A combination of clinical, serotypic, and genotypic variables can predict disease progression in this population-based pediatric-onset CD cohort. Independent validation is needed before it can be used in clinical practice.

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