[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"persistent-covid-condition\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:persistent-covid-condition":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":4,"eligibilityCriteria":15,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":4,"enrollmentInfo":18,"targetDuration":21,"studyType":22,"phases":4,"briefSummary":23,"conditions":24,"keywords":26,"overallStatus":34,"whyStopped":4,"lastUpdateSubmitDate":35,"lastUpdatePostDateStruct":36,"startDateStruct":39,"completionDateStruct":41,"leadSponsor":43,"locationsCount":5},"100615659","predictors-of-long-term-evolution-in-long-covid-4-year-follow-up-bioicoper-follow-up-study-100615659",false,"NCT07295483","Predictors of Long-Term Evolution in Long COVID; 4-Year Follow-Up. (BioICOPER Follow-up Study)","To Analyze the Determining Factors in the Evolution of Subjects Diagnosed With Long COVID at Four Years of Follow-up. BioICOPER Follow-up Study.","Inclusion Criteria:\n\n* Adults ≥18 years old.\n* Confirmed previous SARS-CoV-2 infection.\n* Diagnosis of long COVID according to WHO criteria.\n* Participation in the baseline BioICOPER study.\n* Signed informed consent for re-evaluation.\n\nExclusion Criteria:\n\n* Acute illness preventing participation.\n* Cognitive or physical impairment limiting data collection.\n* Withdrawal of informed consent.\n* Age \\\u003C 18 years old","ALL","18 Years",{"count":19,"type":20},400,"ESTIMATED","4 Years","OBSERVATIONAL","Long COVID (persistent COVID) represents a major global health challenge due to its high prevalence (approximately 7%), significant impact on quality of life, and socioeconomic burden. Despite extensive research, diagnostic tools to objectively identify or predict long COVID evolution are still lacking.\n\nThe BioICOPER Follow-up Study aims to analyze the influence of biomarker evolution on clinical symptomatology (particularly chronic fatigue) and vascular health after four years of follow-up among 400 participants previously included in the original BioICOPER cohort.\n\nAdvanced proteomic analysis, vascular function assessment, and machine-learning-based predictive modeling will be used to identify biomarkers associated with disease progression, stratified by sex. This project will contribute to personalized clinical management of long COVID and improved diagnostic and therapeutic strategies in primary care.",[25],"Persistent COVID Condition",[27,28,29,30,31,32,33],"Post-acute COVID syndrome","Vascular aging","Proteomics","Biomarkers","Machine learning","Persistent COVID","Artificial Intelligence","RECRUITING","2026-03-10",{"date":37,"type":38},"2026-03-12","ACTUAL",{"date":40,"type":38},"2026-02-09",{"date":42,"type":20},"2029-06",{"name":44,"class":45},"Instituto de Investigación Biomédica de Salamanca","OTHER"]