Auxiliary Data as Surrogates for Systematic Coronary Risk Evaluation Model Version 2 Risk Calculator Inputs: Design...

The aim of the study was to investigate whether data collected from wearable devices, such as heart rate, step count, and sleep-related parameters, can reduce uncertainty in cardiovascular disease (CVD) risk prediction among apparently healthy women aged 40-69 years when blood pressure and blood lipid values required for the SCORE2 risk calculator are unavailable. A secondary objective was to identify wearable-derived features that contribute most to reducing uncertainty in CVD risk prediction.

This was a monocentric, exploratory, cross-sectional observational study. Participants/Materials: Women aged 40-69 years with and without cardiovascular risk factors were invited to participate. Eligible participants were biologically female, fluent in German, owned a smartphone, and provided written informed consent. Women with diagnosed CVD, diabetes mellitus, chronic kidney disease, or familial hypercholesterolemia were excluded. The study uses clinical assessments, blood sampling, validated questionnaires, and 7-day continuous monitoring with a Garmin® Vivosmart 5 wearable device.

The study was conducted at the Department of Obstetrics and Gynecology, University Hospital of Bern, Inselspital, Switzerland.

Participants attended two study visits scheduled 7 to 10 days apart. At baseline, anthropometric measures, blood pressure, pulse, waist circumference, blood samples for lipid profile and HbA1c, and questionnaire data on medical history, nutrition, menopausal symptoms, psychosocial health, anxiety, depression, stress, sleep, and quality of life were collected. Participants wore a Garmin® wearable device for 7 days, generating data on heart rate, physical activity, sleep, and related digital biomarkers. Statistical analyses will explore probabilistic approaches to impute systematically missing SCORE2 input parameters, particularly blood pressure and blood lipids, using auxiliary variables such as age, BMI, and wearable-derived features. Model performance will be evaluated by comparing probabilistic CVD risk predictions with SCORE2 estimates based on complete clinical input data.

As this manuscript describes the study design and methodology, no outcome results are reported. The planned sample size is 250 participants, accounting for an estimated dropout rate of approximately 10%.

The exploratory design and monocentric setting may limit generalizability. The sample size may be limited for selecting predictors from high-dimensional wearable data, and identified surrogate features will require external validation. In addition, adherence to wearing the device and potential recording or transmission errors may affect the completeness and quality of wearable data.

The Frauenherzen study provides a methodological framework for evaluating whether wearable-derived digital biomarkers can support CVD risk prediction in middle-aged women when conventional clinical parameters are missing. By combining clinical, questionnaire-based, and wearable data, the study aimed to inform future sex-specific cardiovascular risk stratification and the development of accessible preventive digital health tools.