Clinical Validation of an Artificial Intelligence Based Decision Support System for Predicting Risk, Diagnosis, and Progression of Heart Failure

Heart failure (HF) is a complex clinical syndrome associated with impaired heart function, poor quality of life for patients and high healthcare costs. Accurate risk stratification and early diagnosis in HF are challenging as signs and symptoms are non-specific. Here the investigators propose to address this global challenge by developing novel analytic methods for HF (STRATIFYHF). A prospective clinical study will collect patient-specific data related to medical history, a physical examination for signs and symptoms, blood tests including natriuretic peptides, an electrocardiogram (ECG), an echocardiogram (ultrasound of the heart), cardiovascular magnetic resonance imaging (MRI), demographic, socio-economic and lifestyle data along with novel technologies (cardiac output response to stress (CORS) test and voice recognition biomarkers) from individuals at-risk of developing HF and those with a confirmed diagnosis of HF. STRATIFYHF will use these data to develop, validate and implement the first artificial intelligence (AI)-based, Decision Support System (DSS) for assessing and predicting the risk of HF development, its early diagnosis and progression. STRATIFYHF will integrate 1) patient-specific data i.e. demographic, clinical, genetic, lifestyle and socio-economic, 2) an AI-based digital patient library and AI-driven algorithms for risk stratification, early diagnosis, and disease progression in HF, and 3) a highly innovative multifunctional AI-based DSS and mobile application for informing a patient-centred, personalised, prevention and treatment strategies for HF.