Clinical trial NCT06999317: CARAMEL: Retrospective Study for Personalized Risk Assessment of Cardiovascular Disease in Menopausal and Perimenopausal Women Using Real World Data
Official title: CARAMEL: Retrospective Study for Personalized Risk Assessment of Cardiovascular Disease in Menopausal and Perimenopausal Women Using Real World Data
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Cardiovascular Risk Factors
Menopausal Women
Perimenopausal Women
Real World Data
Keywords: Cardiovascular Disease
Cardiovascular risk factors
Menopausal women
Perimenopausal women
real world data
personalised prevention
computational modelling
women-specific risks
Lead sponsor: Hospital Universitario Virgen Macarena
Sponsor class: OTHER
Collaborators: name: VISUAL INTERACTION & COMMUNICATION TECHNOLOGIES - VICOMTECH
name: Clinic for Cardiovascular Diseases Magdalena
name: Biokeralty Research Institute
name: Keralty SAS. Colombia
name: ETHNIKO KAI KAPODISTRIAKO PANEPISTIMIO ATHINON
name: Fundación Pública Andaluza para la gestión de la Investigación en Sevilla
name: University of Dublin, Trinity College
name: TREE Technology S.A.
name: Dublin City University
name: Tampere University
name: Ben-Gurion University of the Negev
name: Biogipuzkoa Health Research Institute
name: Vilnius University Hospital Santaros Klinikos
First posted: 2025-05-31
Enrollment: 1500000
Sex: FEMALE
Minimum age (years): 40
Maximum age (years): 60
Standard ages: ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Participants are identified retrospectively from electronic health records, imaging archives, and device registries across multiple healthcare systems and countries

Brief summary: 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.


Eligibility criteria: 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.

Primary outcomes:
measure: Occurrence and Predicted Risk of Cardiovascular Disease (CVD) Events (fatal and non-fatal)
timeFrame: up to 10 years
Secondary outcomes:
measure: RO1. Personalized risk prediction of CVD precursors
timeFrame: up to 8 years
measure: RO2. Personalized Risk Prediction of CVD Events and CVD trajectories
timeFrame: Up to 16 years
measure: RO3. Novel Imaging Biomarkers and Patterns for CVD Risk Assessment
timeFrame: Baseline
measure: RO4. Multimodal EHR and ImageBased CVD Prediction Models
timeFrame: Up to 16 years
measure: RO5. Automatic imaging marker and pattern extraction
timeFrame: Baseline
measure: RO6. Signal-based CVD prediction models
timeFrame: Up to 16 years
Other outcomes:
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