ECG project UMCU

Atrial fibrillation (AF) is not benign. It commonly progresses from paroxysmal AF (PAF) to permanent AF. AF progression is associated with major adverse cardiovascular/cerebral events (MACCE). Cardiovascular risk factors and comorbidities (CVR) are present long before the first AF episode, causing a progressive atrial cardiomyopathy (ACM). The mechanisms of ACM vary between patients hindering effective AF management. The EmbRACE network now aims to unravel the diversity of mechanisms underlying ACM, identify simple diagnostic tools to identify them, and develop a therapeutic approach to prevent ACM progression. The Research Early rhythm-control therapy is one promising intervention to potentially interfere with ACM progression next to CVR management. For a sustained impact we aim to develop care pathways to prevent ACM and AF progression and MACCE. Therefore, we will identify and validate relevant cellular and molecular determinants of ACM and AF and their clinical surrogate parameters; develop an in-silico platform to simulate identified mechanisms of ACM and AF and their effects on AF progression and, based on these data, make suggestions for future refinement of ACM therapy; explore the variety of temporal patterns of PAF as markers of ACM subtypes, demonstrate their prognostic relevance and identify surrogate markers available in clinical practice, based on AI and machine learning; test in a randomized trial stratified for sex the hypothesis that early AF ablation and optimal CVR management in AF patients with ACM delays ACM progression and reduces MACCE; explore whether lifestyle management reduces ACM progression, whereas with only rate control ACM progresses; validate the RACE V AF progression score in real life cohorts and translate this and other knowledge into novel care pathways for AF. The origin Atrial fibrillation is the most common cardiac arrhythmia and can lead to a variety of complications, such as stroke. Currently, there are limited treatment options for this cardiac arrhythmia. Moreover, the disease is often noticed late, which makes proper treatment even more difficult. Therefore, the Dutch Heart Foundation funded the RACE V consortium. Afterwards, the Dutch Heart Foundation guided an exploration to form a national consortium as a follow-up around this theme. This led to the EmbRACE consortium, which is a national network of six university medical centers, UMC Groningen, Maastricht UMC+, UMC Utrecht, Amsterdam UMC and LUMC and Erasmus MC, and hospitals in Arnhem and Eindhoven. The Dutch Heart Foundation funds the research.

Smoking tobacco and physical inactivity are key preventable risk factors of cardiovascular disease (CVD). Perfect Fit aims to prevent CVD, promote well-being, and reduce healthcare costs, particularly targeting disadvantaged populations where smoking and physical inactivity are prevalent. The Research The project develops tailored, evidence-based, near real-time computer coaching for quitting smoking and enhancing PA. For every individual, a personal model is designed which generates personalized recommendations based on high-quality existing and newly collected data, and adapts to changing circumstances/progress (similar to a TomTom navigation system), using machine learning techniques and incorporating domain-specific expert knowledge (e.g. health behaviour change strategies). Virtual coaches (VCs) communicate advice in a motivating way that fits individuals’ persuasive communication styles. Perfect Fit integrates big-data science, sensor technology, and personalized real-time feedback to support smoking cessation and promote adequate physical activity (in both gym settings and daily life). The key questions of this study are: Which adaptivity is needed to create a robust, safe, and effective interaction between individuals and machines? How can we develop advanced data science methods and embed this in current smoking cessation and PA coaching practice? How do measurement modalities, feedback and communication affect individuals’ smoking status and PA? Origin This project was funded within the Big Data & Health Program. The focus of this public-private research program is the use of big data for the early detection and prevention of cardiovascular diseases. The program has been developed by NWO, ZonMw, the Dutch Heart Foundation, the Top Sectors Life Sciences & Health (LSH), ICT and Creative Industry, the Ministry of Health, Welfare and Sport, and the Netherlands eScience Center. Within this research program, the ambitions of the Dutch Heart Foundation, the Ministry of Health, Welfare and Sport, and the Netherlands eScience Center were aligned with the ambitions of Commit2Data for the Top Sectors ICT, LSH, and Creative Industry, as described in the 2018-2019 Kennis- en Innovatiecontracts between NWO and the Top Sectors.