Sudden cardiac arrest (SCA) causes ~20% of all deaths in Europe. SCA is lethal within minutes if left untreated and survival rates are presently only 5-20%. Therefore, there is a large medical need to improve SCA prevention and treatment. Designing effective individualized prevention and treatment strategies requires knowledge on genetic and environmental risk factors. So far, these efforts have been hampered by the lack of sufficiently large study cohorts of SCA patients with detailed information. Obtaining SCA patient samples is challenging as the condition happens suddenly and unexpectedly. In this project, leading European scientific teams which have created large relevant population cohorts, mostly dedicated to SCA research, join forces to fully exploit available data towards improving SCA management. This will be done by: - Building an unique and growing database of >100.000 (DNA) samples including >20.000 SCA patient samples, by combining existing European databases and infrastructures. - Identifying risk factors (inherited, acquired, environmental) and first-response treatment strategies that may explain the differences in SCA occurrence and survival between European countries - Collaborating with professional networks, such as the European Heart Rhythm Association, and European Resuscitation Council, to translate the outcomes into changes in clinical practice and influencing European health policies on SCA management.

Long QT Syndrome (LQTS) is a severe arrhythmogenic condition characterised by the prolongation of the QT interval on the electrocardiogram. It is caused by genetic factors (congenital) or drugs (acquired) and sudden cardiac death can be the first manifestations of the disease. Advancements in genetic screenings have revealed profound links between genotype and phenotype for LQTS, improving diagnosis, risk stratification and therapy; However, it is still poorly understood why patients with identical pathogenic mutations have different clinical phenotypes, which factors are involved in this unpredictable disease severity and how we can protect these subjects from drug treatments that are safe in the general population. We do need improved and more physiological in vitro models to simulate arrhythmias in vitro, effective for drug testing, to identify, evaluate and study factors that shape the arrhythmogenic risk in vulnerable subjects. Here I propose a precision medicine approach that uses human pluripotent stem cells-derived cardiomyocytes (hPSC-CMs) from LQTS families (rare resources that include male, female, symptomatic and asymptomatic patients) to: i) demonstrate that the hiPSC technology can reproduce in vitro the clinical disease severity observed in symptomatic vs asymptomatic LQTS mutation carriers; ii) create an in vitro interdisciplinary pharmacological approach with proarrhythmic drugs which combines matched electrophysiological, contractile, molecular and genetic assays; iii) identify and evaluate the factors affecting the arrhythmogenic risk in predisposed subjects. This pipeline to assess arrhythmia susceptibility from patient-specific hiPSC-CMs can be applicable to other arrhythmogenic syndromes. The results of this project will contribute to reduce the use of animal models in preclinical research, to create safer, more effective drugs for humans and to promote the shift of new therapeutic approaches towards precision or personalised medicine.

Obstructive sleep apnea (OSA) is associated with various negative health consequences including increased risk of heart disease, hypertension and daytime sleepiness causing road accidents. The economic burden of OSA is rising as almost 1 billion people worldwide are estimated to have OSA. The current diagnostic metric, however, relates poorly to these symptoms and comorbidities. It merely measures the frequency of breathing cessations without assessing OSA severity in any other physiologically relevant way. Furthermore, the clinical methods for analyzing PSG signals are outdated, expensive and laborious. Due to this, the majority of OSA patients remain without diagnosis or have an inaccurate diagnosis leading to sub-optimal treatment. Thus, it is evident that more personalized diagnostics are required including predictive and preventive health care and patient participation. The SLEEP REVOLUTION aims to develop machine learning techniques to better estimate OSA severity and treatment needs to improve health outcomes and quality of life. These techniques are implemented to high-end wearables developed in this project to alleviate the costs and increase the availability of PSGs. Finally, we aim to design a digital platform that functions as a bridge between researchers, patients and healthcare professionals. We will achieve these ambitious goals throughout extensive collaboration between sleep specialists, computer scientists and industry partners. The collaboration network consists of over 30 sleep centers working together to provide the needed retrospective data (over 10.000 sleep studies). The multi-center prospective trials involve experts and end-users to assess and validate the new SLEEP REVOLUTION diagnostic algorithms, wearables and platforms. With the commitment of the European Sleep Research Society and Assembly of National Sleep Societies (over 8000 members), we have the unique possibility to create new standardized guidelines for sleep medicine in the EU.