Active clinical trials for "Heart Failure"

No studies have analyzed the predictive value of different anamnestic and clinical signs for the diagnosis of acute heart failure (AHF) in patients admitted to emergency department. That's why the expert group of the AHF of South West of France mobilizes to conduct this study to evaluate the diagnostic predictive value of different anamnestic and clinical signs for the diagnosis of AHF to emergencies.

The SchlaHF XT Registry is a longitudinal study of patients with heart failure (HF). The study aims to understand the importance of diagnosis and treatment of sleep disordered breathing (SDB) on HF with the following questions: How often does SDB occur in HF? What forms of SDB occur and how are they treated? How does SDB develop over time? How does the heart failure therapy affect SDB? What is the treatment adherence of patients treated? Does SBD and its treatment affect the survival of heart failure patients?

The present study proposes to utilize a novel, wearable device, QI Device, which has multiple sensors embedded in a "wrist-watch" in order to explore its monitoring capabilities in individuals with congestive heart failure.

This study will test if standardized care helps patients feel better, if it can be implemented in rural hospitals, and find out which healthcare processes and outcomes are most important to heart failure patients.

Study to assess the risk of incident heart failure associated with the use of pramipexole compared with other dopamine agonists and additionally, to assess the risk of incident heart failure associated with the use of dopamine agonists in comparison with no use of dopamine agonist therapy

The purpose of the study is to evaluate different ways to program around extracardiac stimulation.

Peripheral blood and blood following a pulmonary wedge pressure will be obtained from patients undergoing right heart catheterizations.

Prevalence of biventricular function of the heart and preoperative level of venous return is not known in non-cardiac non-morbid obese population. The aim of the study is to assess the preoperative function of the heart and status of hypovolemia on the day of surgery.

The study is designed to repeat an initial training set study conducted at Johns Hopkins Medical Center, comparing a new investigational device, Indicor, a non-invasive tool for estimating left ventricular end diastolic pressure (LVEDP), to the gold standard, invasively measured LVEDP via direct measurement via left heart catheterization. The study is divided into an initial training set, followed by the validation set which is designed to support an FDA 510(k) submission and validate the final algorithm. Patients will be enrolled who are scheduled to undergo a cardiac catheterization and will be asked to perform three tests with the Indicor.

Heart failure is a chronic disease whose prevalence, due to the aging of the population, is increasing. In France, the prevalence of this pathology is 2.3% (it reaches 10% in the over 75 years) and affects nearly a million patients. The rehospitalization of patients with heart failure affects 25% of patients within 1-3 months of hospital discharge, and 66% at 1 year while 75% of hospitalizations are preventable. These readmissions result in decreased quality of life and increased mortality; from an economic point of view, hospitalization accounts for 70% of expenses related to the management of heart failure. Avoiding rehospitalization is therefore a major public health issue. The current predictive scores remain perfectible, even though risk factors for readmission have already been the subject of numerous studies. The identification of patients at risk of rehospitalization is still an issue, especially for patients with preserved left ventricular ejection fraction. Targeting patients requiring appropriate care remains an issue. The rise of innovative statistical techniques around Big Data in health opens new perspectives for the scientific exploitation of data available in electronic medical records, for example in the field of prediction. This study aims to explore the risk of rehospitalization in heart failure patients by analyzing routine data collected in medical records and by mobilizing artificial intelligence algorithms. A review of the literature confirms the innovative nature of such an approach: the majority of the studies identified implemented a prospective collection of data; only 20% of the studies mobilized the medical file; no French study used the new machine learning algorithms.