Clinical Validation of an Artificial Intelligence Based Decision Support System for Predicting Risk, Diagnosis, and Progression of Heart Failure
Trial ID: NCT06377319
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-11-01
Completion date: 2027-07-31
Sponsor: Coventry University
Collaborators: Newcastle University; University of Cambridge; University of Novi Sad; University of Florence; University of Regensburg; Utrecht University, Utrecht, the Netherlands.; Servicio Madrileño de Salud, Madrid, Spain
Conditions: Heart Failure
Interventions: Cardiac Output Response to Stress (CORS) test
Enrollment: 1600
Age range: 45 Years - None
Sex: ALL

Summary:
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.

Eligibility criteria:
1. Inclusion criteria for patients at risk of heart failure:

   1. Individuals at risk of developing HF ≥45 years of age sub-divided into two categories based on current definitions i.e.,

      (i) patients with current or prior symptoms or signs of HF, but without structural, biomarker, or genetic markers of heart disease but have evidence of one of the following: hypertension, cardiovascular disease, type 2 diabetes mellitus, metabolic syndrome, known exposure to cardiotoxins and family history of cardiomyopathy and

      (ii) Patients with current or prior symptoms or signs of HF and have evidence of one of the following - structural heart disease (e.g. Left ventricular (LV) hypertrophy, chamber enlargement, wall motion abnormality, valvular disease), abnormal cardiac function (e.g., reduced LV or right ventricular systolic function, evidence of increased filling pressures or abnormal diastolic function), elevated natriuretic peptide or elevated cardiac troponin on exposure to a cardiotoxin.
   2. All patients willing to visit the clinical research facility and able to provide written informed consent.
2. Inclusion criteria for patients with confirmed diagnosis of heart failure:

   1. Patients with confirmed diagnosis of HF (heart failure reduced ejection fraction; heart failure mildly reduced ejection fraction; heart failure improved ejection fraction; and heart failure preserved ejection fraction) over the previous 24 months or
   2. hospitalisation due to HF ≥45 years of age
   3. willing to visit the clinical research facility and able to provide written informed consent.
3. Exclusion criteria for at-risk individuals and diagnosed heart failure patients:

   1. inability to provide verbal informed consent
   2. presenting with severe symptoms
   3. major co-morbidity or other alternative diagnoses (e.g., malignancy, severe respiratory disease, mental health problem); recent acute coronary syndrome (within 60 days)
   4. severe physical disability preventing independence
   5. scheduled or implanted pacemaker or cardio-defibrillator in the last 3 months
   6. severe renal insufficiency
   7. present or planned pregnancy
   8. life expectancy less than 12 months.

Outcomes:
Diagnostic accuracy of the DSS