Best Therapeutic Sequence of Second-Line Biologics After Anti-TNF or Vedolizumab in IBD
Dernière mise à jour : 1 sept.
Routine care data analyzed using explainable AI identify the best therapeutic sequence of 2nd-line biologic treatments after a 1st line with Anti-TNF or Vedolizumab in inflammatory bowel disease. Presented at United European Gastroenterology 2024 (UEG).
Inflammatory bowel disease (IBD) - including Crohn's disease and ulcerative colitis - is characterized by chronic inflammation of the gastrointestinal tract, which can damage the bowel and cause serious disabilities. Treatments only have a suspensive effect, and therapeutic sequences are currently based on reimbursement criteria or prescriber expertise, in a "trial-and-error" approach rather than validated scientific evidence.
Routine clinical care data from 1,190 IBD patients and 1,269 variables, collected by a private tertiary care center dedicated to IBD patients using the customized Instamed® platform, includes comprehensive information on treatment history, clinical variables, and disease activity scores - in particular the Harvey-Bradshaw Index (HBI), Partial Mayo Score (PMS), and Simple Clinical Colitis Activity Index (SCCAI).
The KEM® platform uses Formal Concept Analysis (FCA) to systematically extract and evaluate all relations between variables in a database, enabling the identification of patient subgroups with a greater likelihood of response to specific treatment sequences.
This proof-of-concept study uses routine clinical care data from an initial set of 1,190 IBD patients and the KEM® platform to provide actionable hypotheses supporting therapeutic decisions for disease management. These initial results will be further improved and validated as real-world data accumulates on new patients and follow-up continues on those already included.
The flexibility of the platform will enable the inclusion of more therapeutic sequences and new treatments as they become available in clinical practice — building a therapeutic decision-support tool that gains robustness over time, guiding patient management toward digital precision medicine.
