Rheumatoid Diseases
Conditions
Keywords
Artificial Intelligence, Personalized medicine, Treatment change, Phenotyping
Brief summary
This study is part of the Clinnova program. This is a prospective cohort study including patients with RD 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.
Detailed description
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.
Interventions
OTHEREither newly diagnosed with a RD requiring initiation of therapy OR treatment change at the time of inclusion
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.
Sponsors
FNR (Luxembourg National Research Fund)
Hôpitaux Robert Schuman Luxembourg
University of Luxembourg
Luxembourg Institute of Health
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Inclusion criteria
● Signed informed consent form * ≥ 18 years of age * Willing and able to comply with the protocol for the duration of the study including data and samples collection, study visits and examinations * Either newly diagnosed with a RD as defined in the diseases of interest below\* requiring initiation of therapy OR treatment change , as per physician's discretion OR any increase in disease activity assessed by the rheumatologist to be relevant (e.g., flare(s) before the study visit) \*For RA: * Participants fulfilling the 2010 ACR/EULAR criteria for RA * Newly diagnosed RA in the last 2 years requiring initiation/change of therapy OR any type of RA requiring treatment change * For SLE: * Participants fulfilling the 2019 ACR/EULAR Classification Criteria of Systemic Lupus Erythematosus (SLE) * For SSc: • Participants fulfilling the 2013 ACR/EULAR Classification Criteria for Systemic Sclerosis (Ssc) * For ASSD: * Participants fulfilling the 2010 Connor's criteria for ASSD
Exclusion criteria
1. Any condition that could potentially hamper the compliance with the study protocol, including study procedures and study visits (such as mental disability that makes it difficult or impossible to answer questionnaires) 2. Not fluent in any of the following languages: French, English or German 3. Known pregnancy Note: If the participant is pregnant after inclusion and/or follow-up, this is not a reason for withdrawal. 4. Participation in a prospective randomised interventional trial 5. Decrease of disease activity 6. Treatment change due to the unavailability of medication 7. Treatment change due to safety reasons 8. Disease-specific
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| To identify clinical, epidemiological and omics characteristics associated with RD | 2031 |
Secondary
| Measure | Time frame |
|---|---|
| To establish a sample and data bank to enable biomedical research. | 2031 |
Other
| Measure | Time frame |
|---|---|
| To develop applications for better interaction between participants and medical doctors leading to improved healthcare support services. | 2031 |
Contacts
Primary ContactJasmin Schulz, PhD
Outcome results
None listed