Clinical trial NCT06235684: Clinnova-RD: A Prospective Cohort Study of Patients With Rheumatoid Diseases
Official title: 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
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Rheumatoid Diseases
Keywords: Artificial Intelligence
Personalized medicine
Treatment change
Phenotyping
Lead sponsor: Luxembourg Institute of Health
Sponsor class: OTHER_GOV
Collaborators: name: FNR (Luxembourg National Research Fund)
name: Hôpitaux Robert Schuman Luxembourg
name: University of Luxembourg
First posted: 2024-02-01
Enrollment: 100
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Either newly diagnosed with a RD requiring initiation of therapy OR treatment change at the time of inclusion

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.

Eligibility criteria: 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 exclusion criteria:

For RA:

• overlap of RA with any other rheumatic/immunologic disease

For SLE:

* overlap of SLE with any other rheumatic/immunologic disease

For SSc:

* overlap of SSc with any other rheumatic/immunologic disease
* other forms of SSc than diffuse cutaneous or limited cutaneous systemic sclerosis

For ASSD:

* overlap of ASSD with any other rheumatic/immunologic disease
* other forms of myositis

Primary outcomes:
measure: To identify clinical, epidemiological and omics characteristics associated with RD
timeFrame: 2031
Secondary outcomes:
measure: To establish a sample and data bank to enable biomedical research.
timeFrame: 2031
Other outcomes:
measure: To develop applications for better interaction between participants and medical doctors leading to improved healthcare support services.
timeFrame: 2031
Locations:
Study officials:
References: