View clinical trials related to Heart Failure.
Filter by:The specific objectives and methods of this project are: (1) To test the feasibility and accuracy of integrating EEG, MECG and EMG for detecting the severity of diseases such as aortic stenosis, heart failure and ischemic stroke. (2) Improve the accuracy of this multi-channel brain-heart-muscle device by using an artificial intelligence auxiliary system. (3) Provide tailor-made interdisciplinary treatment strategies for patients with different disease states.
This study aims to explore the potential of RDN as a therapy for HFpEF in a Prospective, Multicenter, Randomized, Blinded, Sham-controlled Study.
This study aims to compare the efficacy of vericiguat versus placebo on change in n-terminal pro-brain natriuretic peptide (NTproBNP) from baseline to Week 16. The primary hypothesis is Vericiguat is superior to placebo in reducing NT-proBNP at Week 16.
The objective of this study is to evaluate the safety and performance of the V-LAP System in subjects with New York Heart Association (NYHA) functional class II and III HF, irrespective of left ventricular ejection fraction.
To confirm the safety of tolvaptan sodium phosphate in patients with volume overload in heart failure.
In this prospective validation study, researchers investigates accuracy of EHMRG (Emergency Heart Failure Mortality Risk Grade) score in predicting the 7th and 30th day risk of mortality in patients with acute heart failure who applying to the emergency department.
A prospective, multi-center, single-arm study. This study will enroll a maximum of 35 subjects treated with the Revivent TC System.
The iPeer2Peer (iP2P) program is an online peer support mentorship program that provides modelling and reinforcement by trained young adult peer mentors to adolescent mentees with the same condition. A waitlist hybrid implementation-effectiveness type 3 pilot randomized controlled trial design will be employed across four sites. We will recruit 40 mentees (12-17 years of age) and 12-15 mentors (18-25 years of age) who will undergo training in mentoring and the use of eHealth technology. Mentor-mentee pairings will connect over 15 weeks through video calls and text messaging to provide peer support and encourage disease self-management skills. Data will be collected using standardized instruments and interviews across three time points.
In the present project, we propose to run an observational study in order to create a huge dataset with telemonitoring data from heart failure (HF) patients. The dataset will contain physiological measurements, socio-demographic data, risk factor information, medication tracking, symptomatology, clinical events and health-related questionnaire answers from each patient. Furthermore, health-related alarms will be delivered to the medical professionals whenever a measure from a patient is out of a predefined clinical range. These alarms and its defined level of relevance (indicated by the medical professionals) will also be Included in the dataset. With the annotated dataset we will be able to implement and train Machine Learning (ML) models that will improve the alarm-based system by making it more robust, trustworthy and reliable.
This is a prospective, multicentre, unblinded, randomised, controlled trial. The primary aim is to assess a targeted screening strategy to detect undiagnosed heart failure in high-risk patients with diabetes.