Optimizing Cardiovascular Risk Management in Primary Care Using a Personalized eCoach Solution Enhanced by an Artificial Intelligence-Driven Clinical Prediction Model: Protocol from the Coronary Artery Disease Risk Estimation and Early Detection Consortium.

Background: Atherosclerotic cardiovascular disease poses a heavy burden on the population's health and health care costs. Identifying apparently healthy individuals at risk of developing cardiovascular diseases using clinical prediction models raises awareness, facilitates shared decision-making, and supports tailored management of disease prevention. In the CARRIER project, a personalized cardiovascular risk management (CVRM) eCoach approach is cocreated, in which identified individuals receive education, guidance, and monitoring to prevent atherosclerotic cardiovascular disease through existing interventions.

Development and temporal evaluation of sex-specific models to predict 4-year atherosclerotic cardiovascular disease risk based on age and neighbourhood characteristics in South Limburg, the Netherlands.

Background: To improve screening for atherosclerotic cardiovascular disease (ASCVD), we aimed to develop and temporally evaluate sex-specific models to predict 4-year ASCVD risk in South Limburg based on age and neighbourhood characteristics concerning home address.

Methods: We included 40- to 70-year-olds living in South Limburg on 1 January 2015 for model development, and 40- to 70-year-olds living in South Limburg on 1 January 2016 for model evaluation. We randomly sampled people selected in 1 year and in both years to create development and evaluation data sets.

Digital Health Solutions to Reduce the Burden of Atherosclerotic Cardiovascular Disease Proposed by the CARRIER Consortium.

Digital health is a promising tool to support people with an elevated risk for atherosclerotic cardiovascular disease (ASCVD) and patients with an established disease to improve cardiovascular outcomes. Many digital health initiatives have been developed and employed. However, barriers to their large-scale implementation have remained.

Using clinical prediction models to personalise lifestyle interventions for cardiovascular disease prevention: A systematic literature review.

This study aimed to systematically review the use of clinical prediction models (CPMs) in personalised lifestyle interventions for the prevention of cardiovascular disease. We searched PubMed and PsycInfo for articles describing relevant studies published up to August 1, 2021. These were supplemented with items retrieved via screening references of citations and cited by references.

The (un)reliability of item-level semantic priming effects.

Many researchers have tried to predict semantic priming effects using a myriad of variables (e.g., prime-target associative strength or co-occurrence frequency).