Vedolizumab in inflammatory bowel disease: Real-world outcomes and their prediction with machine learning-the IG-IBD LIVE study

Background and aims: Real-world studies on vedolizumab in inflammatory bowel disease (IBD) are often limited by small sample size and short follow-up. In this study, we investigated the 2-year effectiveness and safety of vedolizumab in patients with IBD, and applied eXplainable Artificial Intelligence (XAI) to identify predictors of both. Methods: The Long-term Italian Vedolizumab Effectiveness (LIVE) study is multicentric, ambispective, observational study enrolling 1111 IBD patients (563 Crohn's disease, CD, 542 ulcerative colitis, UC). Steroid-free clinical remission (SFCR) at 24 months was the primary endpoint. A XAI model (eXtreme Gradient Boosting, XGB) was applied to identify the main clinical predictors of SFCR and development of adverse events (AEs). Results: Rates of SFCR at 24 months were 31.6 % and 39.7 % in CD and UC patients, and 0.14 AEs per patient-year was recorded. On XGB analysis, previous exposure to anti-TNF alpha and older age were the most important drivers for the prediction of SFCR; lower baseline CRP levels and fewer comorbidities were the most important features associated with no development of AEs. Conclusions: Vedolizumab is effective and safe in IBD patients. XAI yielded promising results in identifying the most important predictors of SFCR and development of AEs. (c) 2025 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.