Due to the complexity and heterogeneity of IBD, personalized treatment should be implemented in the management of patients.~In particular, the patient stratification by their predicted response to different drugs and the stratification of patients by predicted disease course, which might result in the use of more or less aggressive treatment approaches, are the major unmet clinical needs that should be addressed.~In this context, key unmet needs that can be addressed by data science and Artificial Intelligence (AI) include:~Identification of predictive biomarkers for drug response estimation and identification of prognostic biomarkers to estimate the future course of the disease, focusing on patients in whom treatment needs to be changed.~Improved monitoring of patient well-being.~Patients deemed eligible for the study will be asked to provide data and samples for collection and analysis. They will be followed up for a maximum of 5 years starting from the date of inclusion.~During the first year, data related to demographics, lifestyle, laboratory and physical examinations will be collected at baseline, at 3 months and at 12 months. Patient Reported Outcomes (PROs), including voice recording will be collected optionally at different time points using the Colive smartphone app while physical activity and quality of sleep will be monitored optionally via a smartwatch.~Additionally, participants will be asked to provide biological samples and imaging data (if performed as per standard of care) at different time points (baseline; 3 months; 12 months).~A long-term Follow-up (FU) (starting from month 12 and up to 4 years after month 12) is foreseen in this study. During the long-term FU medical data are collected on a yearly basis, and PROs are collected every 6 months.
This study is part of the Clinnova program. This is a prospective cohort study including patients with IBD recruited at the time of a treatment change.~At least 800 participants (recruited in France, Germany and Luxembourg) will be enrolled, of which 100 participants are expected to be recruited in Luxembourg with the present study protocol.~The mission of Clinnova is to support the digitalization of healthcare and precision medicine by creating a data-enabling environment for accessing, sharing and analyzing interoperable, high-quality health data.~The main hypothesis is that treatment change decided by clinicians is predictable using objective surrogate markers derived from clinical, epidemiological, and omics data. Identifying these objective markers may facilitate future treatment decisions, provide new insights on the molecular causes for differential treatment response, pathogenesis and progression, and potential pointers for improved personalized therapeutic interventions.