Cardiovascular disease (CVD) prevention and management strategies are effective but poorly implemented, especially in urban and underserved communities that would benefit the most. Cities@Heart aims to develop, pilot, and evaluate strategies that will reduce both the burden of CVD and health inequalities. Building on the existing European Healthy Cities Network by partner World Health Organization (WHO), and work by the World Heart Federation, European Society of Cardiology, European Public Health Association and HL7 Europe, we will provide a solid infrastructure that embeds innovative health technology to ensure scalability and sustainability. Cities@Heart will focus on obesity, hypertension, dyslipidaemia and diabetes as key drivers of CVD that can leverage engagement and citizen empowerment. We will develop and deploy derivation and implementation pilots for CVD awareness, effective prevention, early detection, and optimal management at the urban level. The municipalities of Izmir (Turkey), Belfast (Northern Ireland), Łódź (Poland), Cork (Republic of Ireland), Udine (Italy), Birmingham (England) and Utrecht (Netherlands) have diverse communities with differing health inequalities, with a commitment and past experience to deploy multi-disciplinary health strategies. Together with citizen and industry co-creation, these cities will apply a structured, multi-sector methodology that includes: (1) city-level approaches to reduce the burden of CVD; (2) a digital ecosystem that will power the development of European health technology and economic growth, (3) integration of health policy and health economics to deliver cost-effective city-level solutions; and (4) sustainability at its core using an implementation framework that can apply across European cities. Cities@Heart will build connections and capacity across our broad array of stakeholders through a strong public-private partnership. Together, we will support the next generation of health technology to address critical barriers for optimal CVD care in Europe.

Cardiovascular diseases remain the main cause of mortality worldwide; in particular, heart failure (HF) poses complex challenges in clinical practice, as it is associated with a significant variability in aetiologies, manifestations and risks, as well as in its progression and trajectories over time. Clinical risks of HF can vary from reduced cardiac function and regular hospitalisations, all the way to cardiac events and mortality. There is a need for a personalised medicine approach to tailor the care models (i.e. lifestyle changes, medications, interventions) to each HF patient’s risk profile and hence optimise the clinical outcomes. Artificial intelligence (AI) solutions trained from multi-source cardiovascular data have the potential to dissect the precise characteristics of each patient and predict their likely trajectories at an early stage. However, existing AI methods remain a far distance from clinical transfer and adoption due to a common and key limitation: their trustworthiness and acceptance by cardiologists and patients alike have not been achieved. AI4HF will develop the first trustworthy AI solutions for personalised risk assessment and management of HF patients. The project will build on a unique set of big data repositories, trustworthy AI methods, computational tools and clinical results from major EU-funded projects in cardiology. To test robustness, fairness, transparency, usability and transferability, the validation with take place in eight clinical centres in both high- and low-to-middle-income countries in the EU and internationally. AI4HF will develop a comprehensive and standardised methodological framework for trustworthy and ethical AI development and evaluation based on the FUTURE-AI guidelines developed by the consortium members. AI4HF will be implemented through continuous multi-stakeholder engagement, taking into account clinical needs and patient preferences, as well as socio-ethical and regulatory perspectives.

Hypertension, or high blood pressure (BP), is a serious medical condition, and the single biggest contributor to circulatory diseases which continue to dominate as the leading cause of death and morbidity across the EU. It accounts for almost 10 percent of all healthcare-related costs. Systolic hypertension leads to a broad variety of diseases with an immense impact on both patients and healthcare systems. HYPERMARKER will unleash the potential of pharmacometabolomics to provide a ‘smart’ prescription of antihypertensive therapy. Well-phenotyped cohorts from eleven European countries will provide metabolomic profiles and blood samples for pharmacometabolomic assessments to identify predictors of treatment response in hypertension using advanced AI and deep learning methods. Prediction models for individual treatment responses to antihypertensive medication will be clinically validated and refined through an innovative RCT across 4 sites in Europe. The result is a clinical decision support tool that will give clinicians the ability to make an informed selection of whether the patient they are treating will best respond to the use of angiotensin inhibition, calcium antagonists, beta-blockers, or a range of other existing drugs with evidence-based for BP control. To ensure sustainability, the project will also develop a framework for the uptake of this tool in routine care for patients with hypertension across Europe and beyond. HYPERMARKER will be implemented by a group of world-class scientists and clinicians from a diversity of disciplines who have collaborated multiple times and have a track record of leading key national and EU-funded initiatives to deliver high-impact results.

Integrated, high-quality personal health data (PHD) represents a potential wealth of knowledge for healthcare systems, but there is no reliable conduit for this data to become interoperable, AI-ready and reuse-ready at scale across institutions, at national and EU level. AIDAVA will fill this gap by prototyping and testing an AI-powered, virtual assistant maximizing automation of data curation & publishing of unstructured and structured, heterogeneous data. The assistant includes a backend with a library of AI-based data curation tools and a frontend based on human-AI interaction modules that will help users when automation is not possible, while adapting to users? preferences. The interdisciplinary team of the consortium will develop and test two versions of this virtual assistant with hospitals and emerging personal data intermediaries, around breast cancer patient registries and longitudinal health records for cardio-vascular patients, in three languages. The team will work around four technology pillars: 1) automation of quality enhancement and FAIRification of collected health data, in compliance with EU data privacy; 2) knowledge graphs with ontology-based standards as universal representation, to increase interoperability and portability; 3) deep learning for information extraction from narrative content; and 4) AI-generated explanations during the process to increase users? confidence. By increasing automation of data quality enhancement, AIDAVA will decrease the workload of clinical data stewards; by providing high-quality data, AIDAVA will improve the effectiveness of clinical care and support clinical research. In the long-term, AIDAVA has the potential to democratise participation in data curation & publishing by citizens/patients leading to overall savings in health care costs (through disease prevention, early diagnosis, personalized medicine) and supporting delivery of the European Health Data Space.

1–2% of the developed world suffers from congestive heart failure (CHF), which is the most frequent cause of hospitalization in people aged over 65. CHF management involves medications, monitoring of fluid intake and weight, exercise and lifestyle modifications. Since most patients are elderly and suffer from co-morbidities, they have difficulty adhering to the management guidelines, which often leads to poor outcomes. The HeartMan project will develop a personal health system to help CHF patients manage their disease. Its core will be a decision support system that will provide personalised advice to the patients. Its first key feature will be evidence-based predictive models: a short-term model developed in the European project Chiron, and long-term models adapted to focus on modifiable parameters that can improve the patients' predicted outcomes. Its second key feature will be the delivery of the advice through a cognitive behavioural therapy based on cognitive dissonance. This is a proven approach that exploits the dissonance between healthy attitudes and unhealthy behaviours to improve the behaviours. It will be augmented by mindfulness exercises, which are expected to make the patients more receptive to the HeartMan's advice. The system will also feature advanced health devices and monitoring methods to understand the patients' physical and psychological state, and standard-based data management for wide interoperability. In developing the HeartMan system, a human-centred approach will be used. The resulting system will be validated in two trials, which will test its medical effectiveness and usability. The project will also have strong dissemination and exploitation. To ensure industry-standard robustness, the industrial partners will have key role in developing the prototypes, and the documentation necessary for certification as a medical device will be prepared. All the consortium will be involved in IPR management and the building of business models.