View clinical trials related to Heart Failure.
Filter by:Aortic stenosis (AS) is caused by narrowing of one of the main heart valves. Replacing the valve is the only treatment to prevent the heart from failing or death. The timing of replacement is currently often too late - half of patients are left with permanent scarring and a quarter die within 3.5 years. Studies are underway to see if earlier replacement makes a difference. But for those with scarring of the heart, there is currently no tailored treatment. I want to change this by understanding why and how patients with scar are dying and what the investigators can do to prevent this. In this study, the investigators will use a heart scan (MRI) to detect scarring before valve replacement. After replacement, patients will receive a tiny monitor (paper clip size), which the investigators inject underneath the skin. This monitor continuously checks the heartbeat and can detect increased body fluid due to heart failure. The investigators will monitor patients for an average of 3 years to see if scarring is linked to abnormal heart rhythms and heart failure. Once the investigators know how and why, the investigators can target patients with available medications and design studies using specialised treatments, eg defibrillator implantation, to protect patients with scar from dying.
COVID-19 infection has been associated with numerous cardiac manifestations. Indeed, SARS-CoV-2 may impact on cardiovascular system through a direct myocardial infection or a secondary cardiac involvement due to hypoxia or metabolic supply-demand imbalance or prothrombotic inflammatory state. As a consequence of and besides acute myocardial damages, COVID-19 could also determine chronic cardiovascular consequences, with a significant impact on long-term prognosis, quality of life and functional capacity of COVID-19 survivors. On this basis, we aim to define the clinical and prognostic effects of myocardial involvement in COVID-19 patients.
Heart Failure (HF) is a chronic disease that leads to numerous rehospitalisations and affects more than one million people in France. The main objective of this prospective multicentric French study is to describe the annual rate of unplanned hospitalisations for heart failure in a cohort of patients managed by a HeartLogic algorithm. Patients will be included if they fulfill the following requirements 1/Patient implanted with a cardiac defibrillator with or without resynchronisation with the HeartLogic index (RESONATE family, Boston Scientific); 2/History of heart failure (left ventricular ejection fraction ≤40 %; or at least one episode of clinical heart failure with elevated NT pro BNP≥450 ng/L). If a HeartLogic index ≥16 is noticed, the investigator will contact the patient to assess the patient's clinical condition and possibly adjust the heart failure treatment.
The current study aims to investigate whether telemedical monitoring in patients with terminal heart failure and an implanted left ventricular assist device (LVAD) has an influence on LVAD-associated complications, hospitalization rates and quality of life. This is a prospective observational study. Patients with terminal heart failure and an implanted LVAD, where the indication for telemonitoring has already been stated by the attending physician are included in the study. Written informed consent is obtained from all patients. The telemedical monitoring is carried out by the West German Center for Applied Telemedicine (WZAT) and includes a standardized telephone interview every 3 days. In addition, all patients are equipped with an INR measuring device, a body scale and a clinical thermometer by WZAT. The data is documented in an electronic case file (medPower®). In the event of abnormalities, the West German Heart and Vascular Center (WHGZ) is contacted, and all necessary measures are initiated.
The aim of this study is to investigate the effect of strict blood pressure control versus conventional care in patients with asymptomatic moderate to severe aortic valve stenosis. The study is a randomized (1:1), open label, controlled intervention trial. Hypothesis: 1. Strict BP control for 12 months reduces the progression of LV remodelling compared to conventional treatment. 2. Strict BP control for 2 years reduces the increase in aortic valve calcification and LV remodelling compared to conventional treatment. 3. Strict BP reduction for up to 10 years reduces the need for aortic valve replacement and cardiovascular events compared to conventional care.
There is a concept increasingly consolidated by clinical evidence that at each hospitalization due to HF decompensation there is a substantial loss of quality of life, which is associated with an initial period of great clinical vulnerability, with high rates of rehospitalization and an increased risk of death. The non-pharmacological measures that are widely practiced and recommended for HF patients, such as fluid restriction, specially at the first 30 days after hospital discharge, still lack clearer evidence of their therapeutic efficacy.
The incidence of Heart failure with preserved ejection fraction (HFpEF) in Heart failure patients increases rapidly. However, the current clinical awareness is insufficient, and the cardiac structural and functional injury are not well understood. It is difficult to recognize the subclinical changes of the cardiac in the early stage with conventional imaging techniques, and it is common to ignore the existence of the clinical alterations. This study aimed to investigate the cardiac features, early diagnosis and risk factors of HFpEF patients, based on the multi-modal (Magnetic resonance imaging- nuclear medicine imaging- echocardiography) imaging, combined with large data and artificial intelligence. This study will provide deep insights into the HFpEF derived from different causes.
The incidence of Heart failure with preserved ejection fraction (HFpEF) in Heart failure patients increases rapidly. However, the current clinical awareness is insufficient, and the cardiac structural and functional injury are not well understood. It is difficult to recognize the subclinical changes of the cardiac in the early stage with conventional imaging techniques, and it is common to ignore the existence of the clinical alterations. This study aimed to investigate the cardiac features, early diagnosis and risk factors of HFpEF patients, based on the multi-modality (Magnetic resonance imaging- nuclear medicine imaging- echocardiography) imaging and multicenter study, combined with large data and artificial intelligence. This study will provide deep insights into the HFpEF in multicenter population.
This study's main specific aims are; 1. To develop robust acquisition and reconstruction methods specifically for the study of microvascular cardiac remodeling with MRI which will include very innovative quantitative perfusion methods, as well as fibrosis quantification, longitudinal strain, and phase contrast imaging for flow. 2. Test the new methods for identifying the clinical task of characterizing HFpEF.
Despite the progress made in the management of myocardial infarction (MI), the associated morbidity and mortality remains high. Numerous scientific data show that damage of the coronary microcirculation (CM) during a STEMI remains a problem because the techniques for measuring it are still imperfect. We have simple methods for estimating the damage to the MC during the initial coronary angiography, the best known being the calculation of the myocardial blush grade (MBG), but which is semi-quantitative and therefore not very precise, or more precise imaging techniques, such as cardiac MRI, which are performed 48 hours after the infarction and which make the development of early applicable therapeutics not very propitious. Finally, lately, the use of special coronary guides to measure a precise CM index remains non-optimal because it prolongs the procedure. However, the information is in the picture and this information could allow the development of therapeutic strategies adapted to the patient's CM. Indeed, the arrival of iodine in CM increases the density of the pixels of the image, this has been demonstrated by the implementation in 2009 of a software allowing the calculation of the MBG assisted by computer. But the performances of this software did not allow its wide diffusion. Today, the field of medical image analysis presents dazzling progress thanks to artificial intelligence (AI). Deep Learning, a sub-category of Machine Learning, is probably the most powerful form of AI for automated image analysis today. Made up of a network of artificial neurons, it allows, using a very large number of known examples, to extract the most relevant characteristics of the image to solve a given problem. Thus, it uses thousands of pieces of information, sometimes imperceptible to the naked eye. We hypothesize that a supervised Deep Learning algorithm trained with a set of relevant data, will be able to identify a patient with a pejorative prognosis, probably related to a microcirculatory impairment visible in the image.