CARAMEL: Retrospective Study for Personalized Risk Assessment of Cardiovascular Disease in Menopausal and Perimenopausal Women Using Real World Data (CARAMEL RS)
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.
The 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.
The 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.
The 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.
This project has received funding from the European Union's Horizon Europe Research and Innovation Programme under Grant Agreement No 101156210.
Study Overview
Status
Study Type
Enrollment (Estimated)
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
Self-identified as female in the electronic health record (EHR). Age between 40 and 60 years at the time of data collection/index date. Availability of at least 5-6 years of retrospective data in the EHR, depending on the research objective.
At least one healthcare encounter (visit, imaging, lab test, diagnosis, etc.) within the defined age range.
For 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.
For signal-based analysis (RO6): presence of ECG monitoring data from implanted devices and at least 2 years of follow-up.
Exclusion Criteria:
Prior diagnosis of cardiovascular disease before the observation window (only applicable to specific ROs, e.g., RO2, RO4).
Insufficient data quality or missing key variables needed for modeling (e.g., absence of blood pressure or lipid profile).
Patients with incomplete or inconsistent records (e.g., duplicate IDs, mismatched time frames).
For signal-based RO6: hospitalizations or diagnoses unrelated to cardiovascular health that may bias AI model training.
Study Plan
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
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ASCIRES IMAGE DATABASE
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 / modalities
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Basque Health Service Database
Longitudinal EHR data up to 15y including diagnosis, procedures, prescriptions, lab tests, visits, imaging, etc. ~128,00 women 40-60 14,880 DM, 3,124 DXA, 332 carotid US |
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Clalit Primary Prevention Database
Manually curated DB of structured EHR data ~750,000 middleaged women |
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Irish Implant Devices Registry
Irish Implant Devices Registry (REG) (HRI) 15y of data for implant procedures and follow-ups (pacemakers, ICD's, loop recorders) ~85,000 implant (pacemaker) proced ures ~700,000 follow-up w. indications & diagnosis |
|
Keralty Colombia Database
EHR data from primary/specialised care centres. Longitudinal EHR data up to 5-10y Including diagnosis, procedures, prescriptions, lab tests, visits, etc. ~85,593 women 40-60y ~25,000 women with CVD problems |
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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 ~700,000 middleaged women |
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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). Some patients have 5-10y longitudinal data with outcomes. ~6000 women 40-65y with high - very high cardiovascular risk, but without overt CVD; |
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National and Kapodistrian University of Athens Database - Aretaieion Hospital
EHR data from Menopause clinic of Aretaieion university hospital including blood tests, medication, prescriptions, visits ~4000 middle aged women |
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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)
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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)
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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
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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. Middle-aged women among:
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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
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International Agency for Research on Cancer (IARC) / 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) ~65k CVD cases across the full cohort |
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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 = ~4165 women (50 to 70y) (subsample of whole cohort) |
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Occurrence and Predicted Risk of Cardiovascular Disease (CVD) Events (fatal and non-fatal)
Time Frame: up to 10 years
|
The study will retrospectively evaluate the occurrence of cardiovascular disease (CVD) events and develop predictive models to estimate individual risk profiles for such events. CVD events include both fatal and non-fatal occurrences such as myocardial infarction, stroke, heart failure, arrhythmias, and atherosclerotic disease. Events will be identified using structured electronic health records (EHR) and coded using ICD-10 classifications. Risk will be modeled using multimodal data sources (EHR, imaging, and signals) to predict short- and long-term outcomes, stratified by individual characteristics. The outcome integrates: Event-based measures: Time to first fatal or non-fatal CVD event. Risk-based measures: Individual predicted probabilities of experiencing a CVD event or precursor condition (e.g., hypertension, dyslipidemia) over different time frames. |
up to 10 years
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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RO1. Personalized risk prediction of CVD precursors
Time Frame: up to 8 years
|
First observation of HT or DY registered in the EHR, registered as a diagnostic code, or as a laboratory result or test. These include:
|
up to 8 years
|
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RO2. Personalized Risk Prediction of CVD Events and CVD trajectories
Time Frame: Up to 16 years
|
The occurrence of CVD events, which will be classified in fatal (if they are registered as the cause of death) or not fatal (if they are not registered as cause of death).
|
Up to 16 years
|
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RO3. Novel Imaging Biomarkers and Patterns for CVD Risk Assessment
Time Frame: Baseline
|
Evaluates the predictive performance of multimodal models combining imaging features (e.g., cardiac MRI, DXA, digital mammography) and electronic health record (EHR) variables to estimate the mid- and long-term risk of cardiovascular events (CVD) in women aged 40-60.
The endpoint is the first occurrence of a fatal or non-fatal CVD event after the imaging test, as documented in the EHR.
The models will be compared against standard risk assessment tools (e.g., SCORE2).
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Baseline
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RO4. Multimodal EHR and ImageBased CVD Prediction Models
Time Frame: Up to 16 years
|
The occurrence of CVD events, which will be classified in fatal (if they are registered as the cause of death) or not fatal (if they are not registered as cause of death).
|
Up to 16 years
|
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RO5. Automatic imaging marker and pattern extraction
Time Frame: Baseline
|
The performance and clinical relevance of AI-based tools for the automatic extraction of cardiovascular imaging biomarkers in women aged 40-60.
These tools will be used to segment anatomical regions and calculate quantitative measures from multimodal imaging (e.g., ultrasound, DXA, cardiac CT, cMRI, mammography).
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Baseline
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RO6. Signal-based CVD prediction models
Time Frame: Up to 16 years
|
Occurrance of CVD events, which include:
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Up to 16 years
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Collaborators and Investigators
Collaborators
Study record dates
Study Major Dates
Study Start (Estimated)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
- CARAMEL RS
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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