Clinnova-RD: A Prospective Cohort Study of Patients With Rheumatoid Diseases: A Trans-Regional Digital Health Effort Unlocking the Potential of Artificial Intelligence and Data Science in Health Care

Major unmet clinical needs in RD are participant stratification by the predicted response to different drugs and the stratification of participants by predicted disease course, which might result in more or less aggressive treatment approaches. In this context, key unmet needs that can be addressed by data science and artificial intelligence include:

• Identification of predictive biomarkers for drug response estimation and identification of prognostic biomarkers to estimate the future course of the disease, focusing on participants in whom treatment needs to be changed.
• Improved monitoring of participant well-being. During the first year, data related to demographics, lifestyle, laboratory and physical examinations will be collected at baseline, at month 3, at month 12 and in case of unscheduled visit. Questionnaires and standardised voice collection will be collected (optionally) at different time points using the Colive web app. Physical activity and sleep quality will be optionally monitored via a smartwatch that will be provided to interested participants. Biological sample(s) and imaging data will be collected at different time points (baseline; 3 months; 12 months; unscheduled visit). A long-term follow-up (starting from month 12 and up to 4 years after month 12) is foreseen in this study. During the long-term follow-up medical data are collected on a yearly basis, and questionnaires are collected every 6 months.