[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"rheumatoid-diseases\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:rheumatoid-diseases":25},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,1,0,[8],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":15,"eligibilityCriteria":16,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":23,"conditions":24,"keywords":26,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":32,"lastUpdatePostDateStruct":33,"startDateStruct":36,"completionDateStruct":38,"leadSponsor":40,"locationsCount":4},"100534196","clinnova-rd-a-prospective-cohort-study-of-patients-with-rheumatoid-diseases-100534196",false,"NCT06235684","Clinnova-RD: A Prospective Cohort Study of Patients With Rheumatoid Diseases","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","Clinnova-RD","Inclusion criteria:\n\n● Signed informed consent form\n\n* ≥ 18 years of age\n* Willing and able to comply with the protocol for the duration of the study including data and samples collection, study visits and examinations\n* 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:\n* Participants fulfilling the 2010 ACR\u002FEULAR criteria for RA\n* Newly diagnosed RA in the last 2 years requiring initiation\u002Fchange of therapy OR any type of RA requiring treatment change\n\n  * For SLE:\n\n    * Participants fulfilling the 2019 ACR\u002FEULAR Classification Criteria of Systemic Lupus Erythematosus (SLE)\n  * For SSc:\n\n    • Participants fulfilling the 2013 ACR\u002FEULAR Classification Criteria for Systemic Sclerosis (Ssc)\n  * For ASSD:\n\n    * Participants fulfilling the 2010 Connor's criteria for ASSD\n\nExclusion criteria\n\n1. 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)\n2. Not fluent in any of the following languages: French, English or German\n3. Known pregnancy Note: If the participant is pregnant after inclusion and\u002For follow-up, this is not a reason for withdrawal.\n4. Participation in a prospective randomised interventional trial\n5. Decrease of disease activity\n6. Treatment change due to the unavailability of medication\n7. Treatment change due to safety reasons\n8. Disease-specific exclusion criteria:\n\nFor RA:\n\n• overlap of RA with any other rheumatic\u002Fimmunologic disease\n\nFor SLE:\n\n* overlap of SLE with any other rheumatic\u002Fimmunologic disease\n\nFor SSc:\n\n* overlap of SSc with any other rheumatic\u002Fimmunologic disease\n* other forms of SSc than diffuse cutaneous or limited cutaneous systemic sclerosis\n\nFor ASSD:\n\n* overlap of ASSD with any other rheumatic\u002Fimmunologic disease\n* other forms of myositis","ALL","18 Years",{"count":20,"type":21},100,"ESTIMATED","OBSERVATIONAL","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.",[25],"Rheumatoid Diseases",[27,28,29,30],"Artificial Intelligence","Personalized medicine","Treatment change","Phenotyping","NOT_YET_RECRUITING","2025-09-02",{"date":34,"type":35},"2025-09-09","ACTUAL",{"date":37,"type":21},"2026-04-01",{"date":39,"type":21},"2032-05-01",{"name":41,"class":42},"Luxembourg Institute of Health","OTHER_GOV"]