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A Novel 8-Predictors Signature to Predict Complicated Disease Course in Pediatric-onset Crohn's Disease: A
Hélène Sarter1,2, Guillaume Savoye3, Guillemette Marot4,5
1Lille Hospital and University, Public Health, Epidemiology and Economic Health, EPIMAD registry, Regional house of clinical research, F-59000 Lille, France.
Insights
Identifying high-risk pediatric Crohn's disease (CD) patients early is crucial. A new model combining clinical, serological, and genetic factors accurately predicts complicated disease courses in children.
Area of Science:
- Pediatric Gastroenterology
- Inflammatory Bowel Disease Research
- Genetics of Autoimmune Diseases
Background:
- Early identification of high-risk patients is vital for tailoring Crohn's disease (CD) therapy.
- Pediatric-onset CD requires specific predictive models due to distinct disease trajectories.
- Disabling disease courses in CD necessitate proactive management strategies.
Purpose of the Study:
- To develop and evaluate a predictive model for complicated disease course in pediatric-onset CD.
- To assess the utility of combining clinical, serological, and genetic markers.
- To identify key factors associated with disease progression in young CD patients.
Main Methods:
- Retrospective analysis of a population-based cohort of pediatric CD patients (EPIMAD registry).
- Defined complicated disease course as stricturing/penetrating behavior or intestinal resection within 5 years.
- Utilized Lasso logistic regression incorporating clinical, serological (e.g., ASCA, pANCA), and genetic (369 SNPs) data.
Main Results:
- The PREDICT-EPIMAD model, including disease location, pANCA, and 6 SNPs, predicted complicated CD with an AUC of 0.80 (corrected).
- The model demonstrated good discrimination (sensitivity 79%, specificity 74%) and calibration.
- 35% of patients experienced a complicated disease course within 5 years of diagnosis.
Conclusions:
- A combination of clinical, serological, and genetic variables effectively predicts disease progression in pediatric CD.
- The PREDICT-EPIMAD model shows clinical utility for risk stratification in pediatric Crohn's disease.
- Further independent validation is required prior to clinical implementation.
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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