View clinical trials related to Heart Diseases.
Filter by:Clinical performance of the device will be evaluated in a fully-crossed, multiple-reader multiple-case (MRMC) study. This study will be used to determine the impact of the device on reader performance in identifying suspicious radiographic findings in fetal heart ultrasound video clips recorded during 2nd trimester anatomic ultrasound examinations conducted during the second trimester of the pregnancy.
To evaluate the safety and effectiveness of intracardiac ultrasound imaging system in Sonosemi Medical Co., Ltd.
Valves will be taken from hearts donated by organ donors, and implanted into patients who need a new heart valve.
This study is a prospective, multicenter, randomized controlled trial with a non-inferiority design. Participants are patients scheduled for atrial septal puncture. After signing informed consent, subjects will be randomly assigned to either the radiofrequency transseptal puncture system group (referred to as the trial group) or the traditional mechanical transseptal puncture system group (referred to as the control group). All participants will undergo immediate postoperative and discharge clinical follow-ups.
Cirrhotic cardiomyopathy is seen as a blunted contractile responsiveness to stress, and/or altered diastolic relaxation with electrophysiological abnormalities, in absence of known cardiac disease. Left ventricular diastolic dysfunction (LVDD) is associated with risk of hepatorenal syndrome (HRS) , septic shock. , heart failure in the perioperative period following liver transplantation, and after trans-jugular intrahepatic portosystemic shunt (TIPS) insertion . The echocardiographic E/e' ratio is a predictor of survival in LVDD, with multiple studies, including prospective data from our Centre. The inability of the heart to cope with stress or sepsis induced circulatory failure is a key concept of the increased mortality risk due to LVDD. In view of the metabolic syndrome and diabetes epidemic and an increasing number of patients being diagnosed with non-alcoholic fatty liver disease, there is increased risk of developing cardiac dysfunction due to multiple comorbidities including coronary artery disease, hypertensive heart disease, cirrhotic cardiomyopathy, which are contributors to overall cardiovascular risk of mortality.
The aim of this study is to develop a deep learning-based application of heart sounds in the diagnosis of valvular heart disease, which can be used to screen patients with valvular heart disease and promote earlier clinical monitoring and intervention.
This pilot study (n=20) is a prospective evaluation of the implementation of an indoor air pollution intervention among patients who have undergone recent percutaneous coronary intervention procedures. The intervention, called Air Improvement and Real-time Monitoring for Wellness through Interactive Strategies and Education (AIRWISE), is focused on improving indoor air quality through air filtration, education, and behavioral recommendations. All participants will receive the AIRWISE intervention with the objective of evaluating acceptance and use of the individual intervention components. This implementation study will inform the submission of a larger NIH proposal for a randomized trial. The central study hypothesis is that an intervention program with educational strategies and visual behavioral cues will increase knowledge and awareness of air pollution exposures among the participants and lead to improved intervention compliance.
Congenital heart disease (CHD) is the most common congenital disease in children. The early detection, diagnosis and treatment of CHD in children is of great significance to improve the prognosis and reduce the mortality of children, but the current screening methods have limitations. Electrocardiogram (ECG), as an economical and rapid means of heart disease detection, has a very important value in the auxiliary diagnosis of CHD.Big data and deep learning technologies in artificial intelligence (AI) have shown great potential in the medical field. The advent of the big data era provides rich data resources for the in-depth study of CHD ECG signals in children. The development of deep learning technology, especially the breakthrough in the field of image recognition, provides a strong technical support for the intelligent analysis of electrocardiogram. The particularity of children electrocardiogram requires the development of a special algorithm model. At present, the research on the application of deep learning models to identify children's electrocardiograms is limited, and the training and verification from large data sets are lacking. Based on the Chinese Congenital Heart Disease Collaborative Research Network, this project aims to integrate data and deep learning technology to develop a set of intelligent electrocardiogram assisted diagnosis system (CHD-ECG AI system) suitable for children with CHD, so as to improve the early detection rate of CHD and improve the efficiency of congenital heart disease screening.
Coronary heart disease (CHD) combined with chronic kidney disease (CKD) affects a substantial portion of the population and carries a significant disease burden, often leading to poor outcomes. Despite efforts to strictly control traditional risk factors, the efficacy in improving outcomes for patients with both CHD and CKD has been limited. Recent advancements in lipid metabolism research have identified new lipid metabolites associated with the occurrence and prognosis of CHD and CKD. Our preliminary trial has shown that levels of certain lipid metabolites, such as Cer(18:1/16:0), HexCer(18:1/16:0), and PI(18:0/18:1), are notably elevated in patients with CHD and reduced kidney function compared to those with relatively normal kidney function. This suggests that dysregulation of these non-traditional lipid metabolites may contribute to residual risk for adverse outcomes in these patients. Furthermore, the emerging concept of "cardiovascular-kidney-metabolic syndrome" and the availability of new treatment options highlight the urgent need for a risk stratification tool tailored to modern management strategies and treatment goals to guide preventive measures effectively. To address this, we propose to conduct a prospective cohort study focusing on CHD combined with CKD. This study aims to comprehensively understand the clinical characteristics, diagnosis, treatment status, and cardiovascular-kidney prognosis in these patients. Through advanced metabolomics analysis, we seek to identify lipid metabolism profiles and non-traditional lipid metabolites associated with the progression of coronary artery disease in CHD-CKD patients. Leveraging clinical databases and metabolomics data, we will develop a robust risk prediction model for adverse cardiovascular-kidney outcomes, providing valuable guidance for clinical diagnosis, treatment decisions, and ultimately improving patient prognosis.
This type of study is a randomized controlled trial. The aim is to contribute to the literature by comparing the effects of heart yoga on the depression level, sleep and quality of life of patients who do and do not do heart yoga. The main elements it aims to answer are: - Heart yoga applied to patients who have undergone heart surgery improves their sleep quality compared to those who have not. - Heart yoga applied to patients who have undergone heart surgery reduces the level of depression compared to those who have not. - Heart yoga applied to patients who have undergone heart surgery increases the quality of life compared to those who have not. Patients who accept the study will practice heart yoga simultaneously with heart yoga videos via an online link every other day for 12 weeks after heart surgery. Researchers will compare intervention and control groups to see if there are any effects on sleep quality, depression level, and quality of life.