View clinical trials related to Heart Diseases.
Filter by:The objective of this study was to evaluate the effect of esketamine on intraoperative hemodynamics in patients with heart valve replacement and to mitigate postoperative pulmonary complications
This study is a prospective, multi-center, randomized controlled trial. The subjects are patients scheduled to undergo a combined procedure of atrial fibrillation (AF) ablation and left atrial appendage (LAA) occlusion. After signing the informed consent form, the subjects will be randomly assigned to either the cardiac Computed Tomography (CT) group or the Digital Subtraction Angiography (DSA) group. The operator will select the appropriate LAA occluder size for implantation based on different measurement methods. All subjects will undergo clinical follow-up before discharge and at 3 months postoperatively, as well as telephone follow-up 1-5 years after the surgery.
The present observational study aims to determine the degree of adherence to the recommendations of clinical guidelines regarding the prevention of cardiovascular complications in patients hospitalized due to a cardiovascular event.
A randomized clinical trial investigating the incidence and temporal dynamics of subclinical leaflet thickening by cardiac CT in transcatheter bioprosthetic aortic valves in patients randomised to different anti-thrombotic strategies. Additionally, this study aims to examine a possible association between HALT and thromboembolic events.
Inherited cardiovascular conditions generally inherit following an autosomal dominant pattern. When a mutation is detected in the proband, relatives can have predictive DNA testing, and - when they are carrier - be monitored and timely treated if needed. Currently, less than half of relatives attends genetic counselling. With the eCG Family Clinic, an easily accessible virtual clinic which better suits the needs and preferences of relatives will be offered. At the eCG Family Clinic, relatives will receive tailored information to support informed decision-making, a DNA-test at home if desired, and can be referred for local cardiac monitoring if relatives appear to be a carrier. Implementation of the eCG Family Clinic in clinical practice is compared to current practice in this clinical trial.
Valves will be taken from hearts donated by organ donors, and implanted into patients who need a new heart valve.
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.
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.