CARAMEL: Retrospective Study for Personalized Risk Assessment of Cardiovascular Disease in Menopausal and Perimenopausal Women Using Real World Data
Trial ID: NCT06999317
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-03-01
Completion date: 2028-04-30
Sponsor: Hospital Universitario Virgen Macarena
Collaborators: VISUAL INTERACTION & COMMUNICATION TECHNOLOGIES - VICOMTECH; Clinic for Cardiovascular Diseases Magdalena; Biokeralty Research Institute; Keralty SAS. Colombia; ETHNIKO KAI KAPODISTRIAKO PANEPISTIMIO ATHINON; Fundación Pública Andaluza para la gestión de la Investigación en Sevilla; University of Dublin, Trinity College; TREE Technology S.A.; Dublin City University; Tampere University; Ben-Gurion University of the Negev; Biogipuzkoa Health Research Institute; Vilnius University Hospital Santaros Klinikos
Conditions: Cardiovascular Risk Factors; Menopausal Women; Perimenopausal Women; Real World Data
Enrollment: 1500000
Age range: 40 Years - 60 Years
Sex: FEMALE

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.

Outcomes:
Occurrence and Predicted Risk of Cardiovascular Disease (CVD) Events (fatal and non-fatal)