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| ID | Type | Description | Link |
|---|---|---|---|
| 2019-A01795-52 | Other Identifier | N° IDRCB |
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| Name | Class |
|---|---|
| BPIfrance | OTHER |
| Sanofi | INDUSTRY |
| Institut de Recherches Internationales Servier | OTHER |
| Institut National de la Santé Et de la Recherche Médicale, France |
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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| HFpEF patients | Heart failure patients (NYHA II-IV) with left ventricular ejection fraction ≥ 50%, 1000 patients anticipated among which up to 300 with extensive phenotyping |
| |
| HFrEF patients | Heart failure patients (NYHA II-IV) with left ventricular ejection fraction ≤ 40%, 1000 patients anticipated among which up to 100 with extensive phenotyping (age- and gender-matched on participating HFpEF patients) |
| |
| Subjects apparently without heart failure | Subjects without history or signs of heart failure, up to 100 subjects anticipated with extensive phenotyping (age- and gender-matched on participating HFpEF patients) |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Extensive phenotyping | Other | Prospective assessment of physical evaluation, biomarkers and omics, cardiac and vascular imaging and telemonitoring of cardiovascular parameters for 14 days. |
| Measure | Description | Time Frame |
|---|---|---|
| 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 | 14 days |
| Measure | Description | Time Frame |
|---|---|---|
| Prognosis | Identify phenotypic subgroup(s) with higher risk of cardiovascular and HF outcomes | 3 years |
| Myocardial stiffness | Assess the diagnostic and prognostic value of myocardial stiffness measured with ultrafast cardiac echography |
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Inclusion Criteria:
All subjects
For HFpEF patients:
For HFrEF patients:
For subjects apparently without heart failure :
Exclusion Criteria:
All subjects
For both HFpEF and HFrEF patients:
For HFpEF patients:
- History of systolic dysfunction with proven LVEF reduction (≤ 40%)
For subjects apparently without heart failure :
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Patients with different forms of heart failure (ie with preserved left ventricular ejection fraction vs. reduced left ventricular ejection fraction) and subjects apparently without heart failure
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| Name | Affiliation | Role |
|---|---|---|
| Jean-Sébastien HULOT, MD PhD | AP - HP, Hôpital Européen Georges-Pompidou, Paris, France | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| AP - HP, Hôpital Européen Georges-Pompidou | Paris | France |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 24491689 | Background | Ambrosy AP, Fonarow GC, Butler J, Chioncel O, Greene SJ, Vaduganathan M, Nodari S, Lam CSP, Sato N, Shah AN, Gheorghiade M. The global health and economic burden of hospitalizations for heart failure: lessons learned from hospitalized heart failure registries. J Am Coll Cardiol. 2014 Apr 1;63(12):1123-1133. doi: 10.1016/j.jacc.2013.11.053. Epub 2014 Feb 5. | |
| Background | De Peretti, et al. Prévalence et statut fonctionnel des cardiopathies ischémiques et de l'insuffisance cardiaque dans la population adulte en France : apports des enquêtes déclaratives " Handicap-Santé " BEH 2014; (9-10):172-81 | ||
| Background | Galinier M, et al. Parcours de Soins. Dossier insuffisance cardiaque, encore trop d'hospitalisations pourtant évitables. État des lieux en France en 2013. Le Concours Médical 2013; 135(6):443-7 | ||
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Individual participant data used for publication could be shared through scientific collaboration with the sponsor or any collaborations implied.The sharing will respect the unital consortium agreement.
Will become available from the time when summary data are published
Sharing access criteria will be discussed between the sponsor and collaborate
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| OTHER_GOV |
| BioSerenity | INDUSTRY |
| Casis | UNKNOWN |
| Firalis SA | INDUSTRY |
| Fealinx | UNKNOWN |
| Centre National de la Recherche Scientifique, France | OTHER |
| ESPCI Paris | OTHER |
| University of Paris 5 - Rene Descartes | OTHER |
| Sorbonne University | OTHER |
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Plasma, Serum, PaxGene RNA, PBMC
| 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 | 3 years |
| Exercise tolerance | Measure exercise tolerance with 6-minute walk test | 3 years |
| Cardiac fibrosis | Prevalence, diagnostic and prognostic importance of cardiac fibrosis (as estimated by cMRI and specific biological markers) in HFpEF patients | 3 years |
| Arterial Stiffness | Assess the diagnostic and prognostic value of arterial stiffness measured by pulse wave velocity | 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 | 3 years |
| Ventricular-arterial coupling | Machine learning-based analysis on 4D MRI recordings to estimate ventricular-arterial coupling | 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 | 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. | 3 years |
| Telemonitoring of weight | Remote measurement of body weight | 3 years |
| Telemonitoring of cardiac rythm | Remote measurement of cardiac arrhythmias | 3 years |
| Telemonitoring of ECG | Remote measurement of heart rate variability | 3 years |
| Telemonitoring of physical activity | Remote measurement of physical activity with an actimeter | 3 years |
| Telemonitoring of blood pressure | Remote measurements of blood pressure in mmHg | 3 years |
| Telemonitoring of pulmonary function | Remote measurement of respiratory rate | 3 years |
| Telemonitoring of oxygen saturation | Remote measurement of oxygen saturation (%) | 3 years |
| Telemonitoring of pulmonary congestion | Remote evaluation of pulmonary congestion with measurement of thoracic impedance | 3 years |
| Digitalized ECG | Develop novel machine learning based markers of HF, of HFpEF and HFrEF | 3 years |
| Cardiac echography | Rest and low-effort evaluation of cardiac parameters | 3 years |
| Cardiac calcium scoring | Evaluation of calcium scoring among participants | 3 years |
| Cardiac MRI | Novel biomarkers of cardiac fibrosis, extra-cellular volume, matrix remodeling | 3 years |
| Left atria | Evaluation of LA remodeling (volumes) and function (strain) | 3 years |
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| ID | Term |
|---|---|
| D006333 | Heart Failure |
| ID | Term |
|---|---|
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
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