BACKGROUND Inflammatory Bowel Disease (IBD), Crohn’s Disease (CD), and Ulcerative Colitis (UC) are chronic illnesses increasingly spreading in the last decades. New ways to treat chronic diseases, such as precision medicine and predictive analytics, are developing, and digital sources of data allow the development of these new technologies to assist clinical practice.

OBJECTIVES : To identify the available predictive tools, validated by scientific literature, for patients with IBD, CD or UC and/or at risk of faster progression, and to assess how reliable and effective these are in predicting health outcomes and in identifying high-risk patient groups.

METHODS : A systematic literature review was performed on different databases (Ovid Medline, ProQuest, Web of Science, CINAHL), to find peer-reviewed publications. Grey literature research was also conducted. Predictive performance of each tool (e.g. discriminative ability, reliability) in detecting health outcomes is the endpoint of the review.

RESULTS : The review included 26 publications after 4 screening stages. The review provides a comprehensive framework of all the predictive tools publicly tested in the scientific literature. All the included articles reported data on tools’ predictive value and are classified in 5 groups (scoring systems, risk scores/index, algorithms, modelling and artificial intelligence). 24 out of 26 articles reported that the considered tool(s) are able to predict health outcomes in cohorts of patients with IBD, CD or UC, while 9 out 26 articles assess tools that try to identify high-risk patients’ groups. An analysis of tool’s validation is also reported in the section.

CONCLUSIONS : This review represents the first attempt to synthetize and classify predictive tools’ performance in the IBD context. Although most of the tested tools reported positive performance results, different challenges and limitations on data availability and quality, tool’s comparison and validation still need to be addressed.