Decision Support System for Diagnosis and Progression of Heart Failure

Diagnostic accuracy of the DSS

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 support clinical validation of
 an artificial intelligence (AI)-driven decision support system (DSS) and mobile application
 for risk prediction, diagnosis, and progression of HF to enhance patients' quality of life
 and lead to more cost-effective health care.