This is a prospective multicenter study to decipher phenotypic variability within patients with heart failure and preserved left ventricular ejection fraction (HFpEF). From a registry of heart failure patients (2500 anticipated) hospitalized in the participating centers in the last 3 years, up to 300 participants (with a final ratio of 3 HFpEF patients, 2 patients with heart failure and reduced ejection fraction (HFrEF) and 1 matched subjects without heart failure will be enrolled for an extensive phenotyping with physical evaluation, biomarkers and omics, cardiac and vascular imaging and telemonitoring of cardiovascular parameters. Cluster analysis with machine learning methods will be performed to define phenogroups unique to the HFpEF patient population.
Heart failure with preserved ejection fraction (HFpEF) is a complex and prevalent syndrome with currently no efficient therapy. This syndrome is likely explained by different pathophysiological inputs leading to common symptoms of heart failure. These pathophysiological abnormalities can primarily involve the heart but also other organs with secondary impact on the myocardium. There is however no clear understanding and diagnostic algorithms of the different HFpEF subpopulations. Novel mathematical methods (such as machine learning) can help identifying clusters within an heterogeneous population such as HFpEF patients. A registry (2500 anticipated) will be constituted with patients hospitalized for congestive heart failure in the participating centers during the last 3 years. From this registry, up to 500 patients will be invited to visit in the hospital for 8-10 hours for physical examination, ECG, performance-based tests, blood draw, cMRI, echocardiography (rest and low-level exercise), Ultrafast echo (for non-invasive measurement of myocardial stiffness), low radiation cardiac CT (for calcium scoring), non-invasive measurement of arterial stiffness. They will be asked to fill out questionnaires about dyspnea, depression and about general health and quality of life. They will then be equipped with a smart connected garment (with cardiovascular \& hemodynamic sensors), a connected weight balance and a blood pressure monitoring device for telemonitoring collection of cardiovascular hemodynamic parameters in real-life conditions (for 14 days). Patients included in the registry will be followed-up for 3 years using medico-administrative databases and vital status, cardiovascular and heart failure outcomes will be collected.
Study Type
OBSERVATIONAL
Enrollment
175
Prospective assessment of physical evaluation, biomarkers and omics, cardiac and vascular imaging and telemonitoring of cardiovascular parameters for 14 days.
AP - HP, Hôpital Européen Georges-Pompidou
Paris, France
Machine learning algorithm to identify distinct phenotypic subgroups among HFpEF patients
Machine learning-based cluster analysis using extensive phenotyping data from HFpEF, HFrEF and subjects without apparent HF
Time frame: 14 days
Prognosis
Identify phenotypic subgroup(s) with higher risk of cardiovascular and HF outcomes
Time frame: 3 years
Myocardial stiffness
Assess the diagnostic and prognostic value of myocardial stiffness measured with ultrafast cardiac echography
Time frame: 3 years
Sarcopenia and muscular capacity
Prevalence and importance of muscle loss, weakness measured with hand grip strength test (Kg) and with the short physical performance battery (SPPB, combining the results of gait speed, chair stand and balance tests) in HFpEF patients
Time frame: 3 years
Exercise tolerance
Measure exercise tolerance with 6-minute walk test
Time frame: 3 years
Cardiac fibrosis
Prevalence, diagnostic and prognostic importance of cardiac fibrosis (as estimated by cMRI and specific biological markers) in HFpEF patients
Time frame: 3 years
Arterial Stiffness
Assess the diagnostic and prognostic value of arterial stiffness measured by pulse wave velocity
Time frame: 3 years
Right heart and pulmonary circulation
Assess the diagnostic and prognostic value of novel markers to quantify right heart function and pulmonary circulation measured with cMRI
Time frame: 3 years
Ventricular-arterial coupling
Machine learning-based analysis on 4D MRI recordings to estimate ventricular-arterial coupling
Time frame: 3 years
Omics signature
Apply multi-omics techniques (including measurements of miRNA, lNcRNA, inflammation markers, and DNA methylation level) to define specific biological signatures to HF and HFpEF patients
Time frame: 3 years
Quality of life evaluation
General and HF QOL questionnaires: Kansas city cardiomyopathy questionnaire - the sum of responses from all 12 items, Range for subscale is 0-100 and the range for the summary score is 0-100 with lower scores indicating more significant disease impact; Global quality of life score with SF36 (Short form 36 health survey): The norm data is 0-100, the health related quality of life is increased as the scores are increased.
Time frame: 3 years
Telemonitoring of weight
Remote measurement of body weight
Time frame: 3 years
Telemonitoring of cardiac rythm
Remote measurement of cardiac arrhythmias
Time frame: 3 years
Telemonitoring of ECG
Remote measurement of heart rate variability
Time frame: 3 years
Telemonitoring of physical activity
Remote measurement of physical activity with an actimeter
Time frame: 3 years
Telemonitoring of blood pressure
Remote measurements of blood pressure in mmHg
Time frame: 3 years
Telemonitoring of pulmonary function
Remote measurement of respiratory rate
Time frame: 3 years
Telemonitoring of oxygen saturation
Remote measurement of oxygen saturation (%)
Time frame: 3 years
Telemonitoring of pulmonary congestion
Remote evaluation of pulmonary congestion with measurement of thoracic impedance
Time frame: 3 years
Digitalized ECG
Develop novel machine learning based markers of HF, of HFpEF and HFrEF
Time frame: 3 years
Cardiac echography
Rest and low-effort evaluation of cardiac parameters
Time frame: 3 years
Cardiac calcium scoring
Evaluation of calcium scoring among participants
Time frame: 3 years
Cardiac MRI
Novel biomarkers of cardiac fibrosis, extra-cellular volume, matrix remodeling
Time frame: 3 years
Left atria
Evaluation of LA remodeling (volumes) and function (strain)
Time frame: 3 years
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.