CARAMEL: Retrospective Study for Personalized Risk Assessment of Cardiovascular Disease in Menopausal and Perimenopausal Women Using Real World Data
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 1,500,000
- 主要终点
- Occurrence and Predicted Risk of Cardiovascular Disease (CVD) Events (fatal and non-fatal)
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 40 Years 至 60 Years（Adult）
- 性别
- Female
- 接受健康志愿者
- 否
入选标准
- •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.
排除标准
- •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.
结局指标
主要结局
Occurrence and Predicted Risk of Cardiovascular Disease (CVD) Events (fatal and non-fatal)
时间窗: 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.
次要结局
- RO1. Personalized risk prediction of CVD precursors(up to 8 years)
- RO2. Personalized Risk Prediction of CVD Events and CVD trajectories(Up to 16 years)
- RO3. Novel Imaging Biomarkers and Patterns for CVD Risk Assessment(Baseline)
- RO4. Multimodal EHR and ImageBased CVD Prediction Models(Up to 16 years)
- RO5. Automatic imaging marker and pattern extraction(Baseline)
- RO6. Signal-based CVD prediction models(Up to 16 years)
研究者
Luis Gabriel Luque Romero
Head of Primary Care Clinical Research Unit
Hospital Universitario Virgen Macarena