View clinical trials related to Heart Diseases.
Filter by: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.
The goal of this observational study is to learn about the frequency of ATTR amyloid, cardiac involvement and associated features, in 150 patients aged 50 or more years, and operated for an idiopathic carpal tunnel syndrome, lumbar spine stenosis or total hip or knee arthroplasty for primary osteoarthritis. The main questions to be answered are: 1. What is the frequency of ATTR deposits in samples retrieved during surgery? 2. What is the frequency of cardiac involvement in ATTR positive patients? 3. What are the preoperative predictors of ATTR deposits? Participants will - have operative samples stained by Congo red in search of amyloid, which will be typed by immunochemistry in positive samples, - undergo a multimodal imaging search for cardiac involvement, if ATTR is identified, - undergo a preoperative complete clinical examination, including collection of medical history, ECG, biochemical tests, and imaging (ultrasound scans of rotator cuff and hip capsule in all participating patients, of the carpal tunnel in patients operated at this site, and MRI + standing profile radiography of the lumbar spine, in patients operated for lumbar stenosis) - ATTR positive patients will be proposed to be followed-up by a reference center, with the aim of an early diagnosis of cardiac involvement, allowing efficient mamagement. Researchers will assess the frequency of ATTR deposits at each operated site, the frequency or ATTR cardiopathy in ATTR + patients, and will compare demographic, clinical, biochemical, and imaging features in patients with and without ATTR deposits, to guide the indications of pathological examination during these frequent orthopedic surgeries
The purpose of this study is to test a culturally tailored, smartphone-delivered intervention designed to increase physical activity and reduce risk for heart disease and type 2 diabetes among African American women.
Heart failure (HF) is a global, public health issue that affects more than 63 million people worldwide; this burden is expected to increase substantially as the population ages. Despite advancements in treatment, a HF diagnosis still leads to significant morbidity and mortality; there is also an immense impact on patients' health-related quality of life (HRQoL). Dapagliflozin was recently granted approval for heart failure by the European Commission, regardless of ejection fraction and whether the patient has diabetes. Real-world observational data are necessary to describe dapagliflozin use in real-world settings in order to assess treatment patterns, HF symptoms and their impact on physical limitation, HRQoL and work productivity, as well as health care utilization of patients treated with dapagliflozin in this setting under local treatment standard conditions in Germany.
Sudden cardiac death (SCD) is the final result of cardiac arrest (CA) , defined as an abrupt and unexpected loss of cardiovascular function resulting in circulatory collapse and death. Up to 50% of cardiac deaths in Europe are due to CA. The estimated mortality of CA is approximately 90%, and significant functional and/or cognitive disabilities often persist among those who survive. The advent of the implantable cardioverter-defibrillator (ICD) has revolutionized the prevention of SCD in high-risk patients with reduced left ventricular ejection fraction (LVEF<35%). However, the algorithm recommended by current guidelines based on LVEF, considered the only parameter to identify high-risk patients, cannot stratify the population and the spectrum of risk with high accuracy. Although the risk of CA is higher among patients with LVEF<35% and NYHA class>1, because of the enormity of the population size at risk (i.e., with organic heart disease and LVEF>35%), most SCD does occur in patients with LVEF>35%. Additionally, the majority of pts who receive the ICD for primary prevention of SCD will not benefit from the device (in the Sudden Cardiac Death in Heart Failure Trial published in 2005, the rate of appropriate ICD therapy was 21% at five years), and/or will experience some side effects of it. In the Israeli registry of patients who underwent ICD (n= 1729) or cardiac resynchronization therapy (n= 1326), the 12-year cumulative incidence of adverse events was 20% for inappropriate shock, 6% for device-related infection, and 17% for lead failure. Moreover, recent improvements in drug treatment for HF and myocardial revascularization have further reduced the incidence of SCD in pts with low LVEF. Finally, pts with advanced HF are unlikely to benefit from ICD therapy because of the high rates of non-arrhythmic deaths. Therefore, improved risk stratification approaches to guide the selection of pts for ICD implantation are needed, and only a multiparametric approach may aim to personalize the risk prediction of SCD across the broad spectrum of the phenotypes of HF patients. The RESPECT project has been designed to personalize the risk of SCD by integrating and interpreting information highly multidisciplinary: clinical and bio-humoral, genetics and electrocardiography, conventional and advanced cardiac imaging, and data science. The investigators hypothesized that machine learning models capable of dealing with non-linearities and complex interactions among predictors, including genetic, clinical, electrocardiographic, bio-humoral, echocardiographic, cardiac magnetic resonance (CMR), and nuclear cardiology data, would have superior accuracy in predicting the occurrence of SCD compared with the currently recommended metrics of NYHA class and LVEF by two-dimensional echocardiography and that the personalized risk prediction of SCD will translate in more cost-effective use of ICDs. In addition, the investigators will use the multiparametric predictive models to develop a cloud-computing app that will allow clinicians to predict the risk of occurrence of SCD based on specific covariate profiles of individual patients.
To investigate clinical and survival outcomes following transcatheter tricuspid valve repair or replacement.