[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100592890":3},{"organization":4,"armGroups":7,"interventions":10,"overallOfficials":10,"centralContacts":10,"locations":10,"responsibleParty":54,"collaborators":58,"id":88,"slug":89,"hasResults":90,"nctId":91,"briefTitle":92,"officialTitle":92,"acronym":93,"eligibilityCriteria":94,"healthyVolunteers":90,"sex":95,"minAge":96,"maxAge":97,"enrollmentInfo":98,"targetDuration":10,"studyType":101,"phases":10,"briefSummary":102,"conditions":103,"keywords":108,"overallStatus":117,"whyStopped":10,"lastUpdateSubmitDate":118,"lastUpdatePostDateStruct":119,"startDateStruct":122,"completionDateStruct":124,"leadSponsor":126,"locationsCount":10},{"fullName":5,"class":6},"Hospital Universitario Virgen Macarena","OTHER",[8,12,15,18,21,24,27,30,33,36,39,42,45,48,51],{"label":9,"type":10,"description":11,"interventionNames":10},"ASCIRES IMAGE DATABASE",null,"Digital imaging biobank 10y long from several manufact 1,000 cMRI; 500 cardiac CT; 500 coronary artery calcification; 1,000 DXA From women 40- 60y urers \u002F modalities",{"label":13,"type":10,"description":14,"interventionNames":10},"Basque Health Service Database","Longitudinal EHR data up to 15y including diagnosis, procedures, prescriptions, lab tests, visits, imaging, etc.\n\n\\~128,00 women 40-60 14,880 DM, 3,124 DXA, 332 carotid US",{"label":16,"type":10,"description":17,"interventionNames":10},"Clalit Primary Prevention Database","Manually curated DB of structured EHR data\n\n\\~750,000 middleaged women",{"label":19,"type":10,"description":20,"interventionNames":10},"Irish Implant Devices Registry","Irish Implant Devices Registry (REG) (HRI) 15y of data for implant procedures and follow-ups (pacemakers, ICD's, loop recorders)\n\n\\~85,000 implant (pacemaker) proced ures \\~700,000 follow-up w. indications \\& diagnosis",{"label":22,"type":10,"description":23,"interventionNames":10},"Keralty Colombia Database","EHR data from primary\u002Fspecialised care centres. Longitudinal EHR data up to 5-10y Including diagnosis, procedures, prescriptions, lab tests, visits, etc.\n\n\\~85,593 women 40-60y \\~25,000 women with CVD problems",{"label":25,"type":10,"description":26,"interventionNames":10},"Andalusian Health Population Database & Macarena University Hospital EHR","Longitudinal EHR data up to 15y including diagnosis, clinical procedures, prescriptions, lab tests, visits, etc. The hospital Dataset is OMOP CMD mapped\n\n\\~700,000 middleaged women",{"label":28,"type":10,"description":29,"interventionNames":10},"Lithuanian High Cardiovascular Risk (LitHiR) primary prevention programme database","EHR data from primary cardiovascular prevention programme in VULSK (1 centre). Data including demographics, risk factors, lab tests (including lipid profile, renal function, etc.), arterial markers (pulse wave velocity analysis data; CardioAngle Vascular Index data; carotid artery intimamedia thickness data).\n\nSome patients have 5-10y longitudinal data with outcomes.\n\n\\~6000 women 40-65y with high - very high cardiovascular risk, but without overt CVD;",{"label":31,"type":10,"description":32,"interventionNames":10},"National and Kapodistrian University of Athens Database - Aretaieion Hospital","EHR data from Menopause clinic of Aretaieion university hospital including blood tests, medication, prescriptions, visits\n\n\\~4000 middle aged women",{"label":34,"type":10,"description":35,"interventionNames":10},"CoroPrevention - Tampere University (TAU)","Pan-European (25 sites) contemporary prospective CVD prevention cohort from ongoing HEU project it includes clinical data, 3-year CV event data, lifestyle, RFs. Standard + CVD biomarkers (CERT2, hsTNI, NTproBNP, Cystatin C…) N=\\~3,000 women (subsample of whole cohort)",{"label":37,"type":10,"description":38,"interventionNames":10},"AKRIBEA - Cooperative Research Centre for Biosciences Association (CIC)","Non-oriented 7y follow-up cohort from Basque Country Region. Urine+serum biomarkers and metabolome; serum lipoproteins by NMR; demographics \\& RFs N=\\~ 2,500 women (40 to 60 y)",{"label":40,"type":10,"description":41,"interventionNames":10},"MENO - Cooperative Research Centre for Biosciences Association (CIC)","Pre- and post-menopausal women cohort from Basque Country Region. Urine+serum biomarkers and metabolome; serum lipoproteins by NMR; demographics \\& RFs N =\\~ 1,700 women",{"label":43,"type":10,"description":44,"interventionNames":10},"UK Biobank - UK Biobank","Largest geno-phenotype-rich population-based study in the world (500K), includes multi-modal imaging data (60K) and eye and vision (67K), biomarkers, demographic data, lifestyle (100K with wearables) and health outcomes.\n\nMiddle-aged women among:\n\n* 500K baseline\n* 60K imaging study\n* 67K retina \\& OCT",{"label":46,"type":10,"description":47,"interventionNames":10},"Qatar Biobank","Population-based with annotated data, biological samples, tests and imaging for 60K participants. It includes Demographics data, lifestyle, biomarkers, weight \\& body fat, hip\\&waist, BP, ECG, carotid US, full-body MRI, retinography, DXA Middle-aged women among \\~60K total participants",{"label":49,"type":10,"description":50,"interventionNames":10},"International Agency for Research on Cancer (IARC) \u002F EPIC-Europa","Long-term European population-based cohort (520K participants across 10 countries). Includes clinical data, anthropometric measurements, demographic, lifestyle, dietary habits, and socioeconomic data, reproductive history, and biological samples such as serum, plasma and DNA for biochemical data and genotyping data N = \\~367k women between 35 to 65 years old (subsample of whole cohort)\n\n\\~65k CVD cases across the full cohort",{"label":52,"type":10,"description":53,"interventionNames":10},"ILERVAS -Institute for Research in Biomedicine IRB Lleida","Interventional longitudinal study that includes detailed assessments of subclinical atheromatosis in 12 vascular territories using ultrasound, along with clinical, anthropometric, lifestyle, dietary, and biochemical data.\n\nN = \\~4165 women (50 to 70y) (subsample of whole cohort)",{"type":55,"investigatorFullName":56,"investigatorTitle":57,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"PRINCIPAL_INVESTIGATOR","Luis Gabriel Luque Romero","Head of Primary Care Clinical Research Unit",[59,62,65,68,70,72,74,76,78,80,82,84,86],{"name":60,"class":61},"VISUAL INTERACTION & COMMUNICATION TECHNOLOGIES - VICOMTECH","UNKNOWN",{"name":63,"class":64},"Clinic for Cardiovascular Diseases Magdalena","NETWORK",{"name":66,"class":67},"Biokeralty Research Institute","INDUSTRY",{"name":69,"class":6},"Keralty SAS. Colombia",{"name":71,"class":61},"ETHNIKO KAI KAPODISTRIAKO PANEPISTIMIO ATHINON",{"name":73,"class":6},"Fundación Pública Andaluza para la gestión de la Investigación en Sevilla",{"name":75,"class":6},"University of Dublin, Trinity College",{"name":77,"class":61},"TREE Technology S.A.",{"name":79,"class":6},"Dublin City University",{"name":81,"class":6},"Tampere University",{"name":83,"class":6},"Ben-Gurion University of the Negev",{"name":85,"class":6},"Biogipuzkoa Health Research Institute",{"name":87,"class":6},"Vilnius University Hospital Santaros Klinikos","100592890","caramel-retrospective-study-for-personalized-risk-assessment-of-cardiovascular-disease-in-menopausal-and-perimenopausal-women-using-real-world-data-100592890",false,"NCT06999317","CARAMEL: Retrospective Study for Personalized Risk Assessment of Cardiovascular Disease in Menopausal and Perimenopausal Women Using Real World Data","CARAMEL RS","Inclusion Criteria:\n\nSelf-identified as female in the electronic health record (EHR). Age between 40 and 60 years at the time of data collection\u002Findex date. Availability of at least 5-6 years of retrospective data in the EHR, depending on the research objective.\n\nAt least one healthcare encounter (visit, imaging, lab test, diagnosis, etc.) within the defined age range.\n\nFor imaging substudies (e.g., RO3-RO5): availability of at least one relevant imaging test (e.g., DXA, digital mammography, cMRI, CCTA, US) during the age range.\n\nFor signal-based analysis (RO6): presence of ECG monitoring data from implanted devices and at least 2 years of follow-up.\n\nExclusion Criteria:\n\nPrior diagnosis of cardiovascular disease before the observation window (only applicable to specific ROs, e.g., RO2, RO4).\n\nInsufficient data quality or missing key variables needed for modeling (e.g., absence of blood pressure or lipid profile).\n\nPatients with incomplete or inconsistent records (e.g., duplicate IDs, mismatched time frames).\n\nFor signal-based RO6: hospitalizations or diagnoses unrelated to cardiovascular health that may bias AI model training.","FEMALE","40 Years","60 Years",{"count":99,"type":100},1500000,"ESTIMATED","OBSERVATIONAL","This retrospective observational study, part of the EU-funded CARAMEL project, aims to develop and validate personalized cardiovascular disease (CVD) risk assessment models specifically designed for menopausal and perimenopausal women (ages 40-60). The study leverages Real World Data (RWD) collected from multiple international clinical partners, including electronic health records (EHR), diagnostic imaging data, and signal data.\n\nThe main objective is to improve the prediction of CVD precursors such as hypertension and dyslipidemia, as well as mid- and long-term risk of CVD events, through advanced artificial intelligence (AI) models. These models will be trained on multimodal data to capture complex, individualized risk trajectories that current risk calculators fail to address, particularly in women. Special focus is placed on under-researched, women-specific risk factors and their interactions with traditional predictors.\n\nThe study includes several research objectives: (1) predicting the onset of hypertension and dyslipidemia using EHR data; (2) modeling the long-term risk of fatal and non-fatal cardiovascular events and disease trajectories; (3) identifying novel imaging biomarkers from routine screening tests such as mammography, DXA, ultrasound, and cardiac MRI; (4) developing multimodal prediction models combining imaging and clinical data; (5) creating automated AI tools for imaging biomarker extraction; and (6) using signal data from cardiac devices to predict disease progression and events.\n\nThe study population consists of middle-aged women with retrospective data available across different health systems. The expected outcome is a validated set of stratified, personalized CVD risk models that can support targeted prevention strategies and enable more equitable, sex-specific care. This will contribute to reducing the burden of CVD in women and addressing critical gaps in early detection, clinical decision-making, and health policy.\n\nThis project has received funding from the European Union's Horizon Europe Research and Innovation Programme under Grant Agreement No 101156210.",[104,105,106,107],"Cardiovascular Risk Factors","Menopausal Women","Perimenopausal Women","Real World Data",[109,110,111,112,113,114,115,116],"Cardiovascular Disease","Cardiovascular risk factors","Menopausal women","Perimenopausal women","real world data","personalised prevention","computational modelling","women-specific risks","NOT_YET_RECRUITING","2026-01-13",{"date":120,"type":121},"2026-01-15","ACTUAL",{"date":123,"type":100},"2026-03-01",{"date":125,"type":100},"2028-04-30",{"name":5,"class":6}]