DOP093 ECCO Topical Review on predictive models for IBD disease course and treatment response
Authors: Kirchgesner MD- PhD J , Verstockt B , Allin K H , Allocca M , Bourgonje A R , Burisch J , Doherty G , Dulai P S , El-Hussuna A , Misra R , Noor N , Pittet V , Powell N , Rodríguez-Lago I , Restellini S 
Published: January 2025

Background

Inflammatory Bowel Disease (IBD) poses a clinical challenge due to its variable progression and treatment response. Despite the development of predictive models, their clinical application remains limited due to validation and methodological inconsistencies. This ECCO Topical Review examines existing predictive models, assesses their relevance, and discusses the barriers to their clinical implementation.

Methods

A panel formed by the European Crohn's and Colitis Organisation (ECCO), including gastroenterologists, surgeons, and methodologists, reviewed predictive models on IBD disease course and treatment response. Using the Medline, PubMed, EMBASE, and Scopus databases, they systematically identified relevant literature to evaluate clinical significance, performance, and barriers to clinical integration.

Results

The panel developed 21 statements on predictive models for IBD disease course and response, with supporting text.

Predictive models for IBD disease course

For ulcerative colitis (UC), models included clinical factors (e.g., age at diagnosis, male gender), biochemical markers (e.g., C-reactive protein [CRP], anemia), and treatment history. Crohn’s disease (CD) models incorporated disease characteristics like stricturing behavior, perianal disease, and imaging findings.

Predictive models for treatment response

UC models focused on severe flares and moderate-to-severe cases managed with biologics, with predictors like CRP, disease duration, and endoscopic severity. CD models emphasized predictors like body mass index, smoking status, and CRP levels. The utility of omics data remains inconsistent.

Barriers to clinical implementation

Models lacked generalizability across diverse populations and face barriers related to clinical integration, healthcare acceptance, regulatory factors, and economics.

Methodological approaches

Future models should use diverse datasets and adhere to TRIPOD guidelines (1). Validation through metrics like calibration and discrimination, as well as trials, will be essential for real-world effectiveness.

Future directions

Predictive models should identify treatment responders, aid in safe discontinuation, and support personalized strategies. Models need biomarkers that are cost-effective, reproducible, and adaptable to individual patient profiles.

Conclusion

Based on this ECCO Topical Review currently being finalized, predictive models for IBD hold promise, but challenges persist in clinical integration. Overcoming methodological issues, performing validation, improving biomarkers, and conducting impact studies are critical. Collaboration among researchers, clinicians, and regulators is pivotal to advance models that improve patient outcomes.

References

(1)Collins et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. The TRIPOD Group. Circulation 2015;131(2):211–9.