ArticleEuropean heart journal. Digital health2021
Development of an accessible 10-year Digital CArdioVAscular (DiCAVA) risk assessment: a UK Biobank study.
Article in European heart journal. Digital health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Characteristics of Cardiovascular Disease Prediction Models Considering Mental Disorders: A Systematic Review.Journal of the American Heart Association · 2026Pooled it
- Derivation and validation of a widely accessible cardiovascular risk prediction model and derived heart age for the general UK and US populations.American journal of preventive cardiology · 2026Article
- Adiposity-lipid-glycemic clusters as potential warning signals of bone mass reduction in Asia's largest urban communities - based bone health assessment via ultrasound.Lipids in health and disease · 2025Article
- AI-driven preclinical disease risk assessment using imaging in UK biobank.NPJ digital medicine · 2025Article
- Do positive psychosocial factors contribute to the prediction of coronary artery disease? A UK Biobank-based machine learning approach.European journal of preventive cardiology · 2025Article
- A digital tool for self-reporting cardiovascular risk factors: The RADICAL study.International journal of cardiology. Cardiovascular risk and prevention · 2025Article
- Feasibility of multiorgan risk prediction with routinely collected diagnostics: a prospective cohort study in the UK Biobank.BMJ evidence-based medicine · 2024Observational
- Adopting artificial intelligence in cardiovascular medicine: a scoping review.Hypertension research : official journal of the Japanese Society of Hypertension · 2024Article
- NHS Health Check attendance is associated with reduced multiorgan disease risk: a matched cohort study in the UK Biobank.BMC medicine · 2024Article
- Development of machine learning-based models to predict 10-year risk of cardiovascular disease: a prospective cohort study.Stroke and vascular neurology · 2023Article
- Comparison of State-of-the-Art Neural Network Survival Models with the Pooled Cohort Equations for Cardiovascular Disease Risk Prediction.BMC medical research methodology · 2023Article
- Actionable absolute risk prediction of atherosclerotic cardiovascular disease based on the UK Biobank.PloS one · 2022Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: Cardiovascular diseases (CVDs) are among the leading causes of death worldwide. Predictive scores providing personalized risk of developing CVD are increasingly used in clinical practice. Most scores, however, utilize a homogenous set of features and require the presence of a physician. The aim was to develop a new risk model (DiCAVA) using statistical and machine learning techniques that could be applied in a remote setting. A secondary goal was to identify new patient-centric variables that could be incorporated into CVD risk assessments. Methods and results: Across 466 052 participants, Cox proportional hazards (CPH) and DeepSurv models were trained using 608 variables derived from the UK Biobank to investigate the 10-year risk of developing a CVD. Data-driven feature selection reduced the number of features to 47, after which reduced models were trained. Both models were compared to the Framingham score. The reduced CPH model achieved a c-index of 0.7443, whereas DeepSurv achieved a c-index of 0.7446. Both CPH and DeepSurv were superior in determining the CVD risk compared to Framingham score. Minimal difference was observed when cholesterol and blood pressure were excluded from the models (CPH: 0.741, DeepSurv: 0.739). The models show very good calibration and discrimination on the test data. Conclusion: We developed a cardiovascular risk model that has very good predictive capacity and encompasses new variables. The score could be incorporated into clinical practice and utilized in a remote setting, without the need of including cholesterol. Future studies will focus on external validation across heterogeneous samples.
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