Clinical Research Directory

NHLBI-Emory Advanced Cardiac CT Reconstruction

Background: Doctors use computed tomography (CT) to get detailed pictures of the heart. CT uses x-rays to gather raw data. Computers assemble this data to make the images doctors look at. A new computer technique can make higher resolution images from the same CT scans. In this natural history study, researchers will take normal CT images of the heart. They will compare those images to super high-resolution (super high-res) images made with a super-computer. Objective: To improve the quality of heart CT scans by using new methods to create the images. Eligibility: People aged 18 years or older who need a CT scan for heart disease. Design: Participants will have a normal CT scan. A substance will be injected through a tube in their arm. They will lie on a table in a large, donut-shaped machine. An X-ray tube will move around their body, taking many pictures. Researchers will use the normal CT scans to create super high-res images. They may do this at the NIH. They may also send the images to the company that made the CT scanner. Participants personal information will be removed before images are sent to the company. The personal information will be replaced by a code. The super high-res images will be returned to the NIH. Some information will be collected from participants medical records. Researchers will compare the normal scans to the super high-res images. Participants' own doctors will also have a chance to see the super high-res images. Participants' CT pictures will be stored and used for future NIH research.

Biventricular Remodeling in Transcatheter Tricuspid Valve Replacement - Acute Hemodynamic Instability Study

TTVR-AHI is a multicenter, retrospective registry including heart failure patients displaying a severe and symptomatic tricuspid regurgitation (TR), deemed non-eligible to cardiac surgery and therefore treated with transcatheter tricuspid valve replacement (TTVR) devices. This substudy of the main registry will focus on those with post-procedural acute hemodynamic instability (AHI).

Optimised Decrement Evoked Potential (DeEP) Mapping to Guide Ventricular Tachycardia (VT) Ablation in Patients With Structural Heart Disease VT

Ventricular tachycardia (VT) is a life-threatening heart rhythm disorder. Special pacemakers called implantable cardiac defibrillators (ICDs) help treat VT episodes, however they do not prevent the VT episodes from occurring. Catheter ablation for VT is a minimally-invasive and established procedure for preventing VT recurrence. This involves placing wires in the heart to find the diseased areas that are responsible for the VT episodes. The diseased areas are shown on computer-generated maps and are later removed via controlled tissue heating (ablation). A major challenge during the VT ablation procedure is locating the diseased area responsible for the VT episodes. Several methods have been described to locate the diseased heart area, however these methods are not always effective. In this study, we aim to improve the identification of the diseased heart areas responsible for the VT episodes using a novel method. Our research group have developed, tested and peer-reviewed this improved method of locating diseased areas by looking for signals called decrementing evoked potentials (DeEPs). Ablation will target DeEPs shown on the computer-generated maps. We will assess if VT can be triggered at the end of the procedure. Patients will be monitored over 12 months to see if ablation of DeEPs leads to a reduction in VT episodes. We aim to recruit 77 patients with established heart disease of any cause, who have suffered VT episodes with ICDs. Suitable patients will be identified and recruited from inpatient and outpatient settings. A quality-of-life questionnaire will be completed by patients before and after the ablation procedure. The procedure will be performed as routine standard of care, in the cardiac catheter laboratory across multiple recruiting cardiac centres in the UK providing well established VT ablation service. Overall, this study will contribute towards developing refined VT ablation techniques, aiming to improve patient outcomes.

The Asia-Pacific Mitral & Tricuspid Valve-in-Valve/Valve-in-Ring Registry

A significant number of patients with severe mitral and tricuspid valve disease have been previously treated with either valve repair with annuloplasty rings or valve replacement with bioprosthetic/mechanical valves. Over time, these bioprosthetic valves and rings may fail resulting in recurrence of the valvular disease. The technical aspects of a re-do operation are complex and these patients are often times at high risk for repeat open surgery. The emergence of transcatheter options may provide a safer and less invasive alternative to open surgery. Majority of the data currently exist for Western cohorts with limited longer-term outcomes. Data on this therapy is particularly lacking in the Asia-Pacific region, especially important in the light of known differences in body habitus and size.

Improvement of an Algorithm to Detect Structural Heart Murmurs in Adult Patients Using Electronic Stethoscopes

The main objective of this study is to evaluate a machine learning model's ability to detect murmurs indicative of structural heart disease ("structural murmur") by analyzing phonocardiogram waveforms-and simultaneous electrocardiogram waveforms when available-in multiple auscultatory positions per subject. Diagnosis of structural murmur will be confirmed by gold-standard echocardiography and reviewed by an expert panel of cardiologists.

Deployment and Evaluation of Artificial Intelligence Software for Electrocardiogram Analysis and Management in Primary Care

The DAISEA-ECG project aims to improve the diagnosis of heart diseases in primary care through the DeepECG platform, which combines ECG-AI and ECHONeXT algorithms. This study uses a stepped wedge design, where each Family Medicine Group acts as its own control. The FMGs will gradually transition from the control period (without AI recommendations) to the intervention period (with AI recommendations activated) in a randomized sequence. The primary objective is to compare the sensitivity of family physicians in detecting cardiac pathologies, with and without the assistance of the DeepECG platform. Sensitivity is defined as the proportion of patients correctly referred to cardiology or for transthoracic echocardiography (TTE) among those who indeed required cardiovascular evaluation, as confirmed by an independent adjudication committee.

Pressure-Volume Loop Assessment in Valvular Heart Disease

The VHD PV loop study intends to assess invasive right ventricular to pulmonary artery (RV-PA) coupling in patients with valvular heart disease (VHD). Invasive RV-PA coupling is measured by using conductance catheters, the gold standard assessment for ventricular physiology. Several non-invasive parameters have been reported as surrogates for this complex physiological entity, but none of them has been tested against the gold-standard in this population. Based on this, our main objective is to assess the correlation of imaging derived RV-PA coupling in comparison to the invasive measurement.

Harnessing ECG Artificial Intelligence for Rapid Treatment and Accurate Identification of Structural Heart Disease

The HEART-AI (Harnessing ECG Artificial Intelligence for Rapid Treatment and Accurate Interpretation) is an open-label, single-center, randomized controlled trial, that aims to deploy a platform called DeepECG at point-of-care for AI-analysis of 12-lead ECGs. The platform will be tested among healthcare professionals (medical students, residents, doctors, nurse practitioners) who read 12-lead ECGs. In the intervention group, the platform will display the ECHONeXT structural heart disease (SHD) scores in randomized patients to help doctors prioritize transthoracic echocardiography (TTEs) or magnetic resonance imaging (MRI) and reduce the time to diagnosis of structural heart disease. Also, this platform will display the DeepECG-AI interpretation which detects problems such as ischemic conditions, arrhythmias or chamber enlargements and acts an improved alternative to commercially available ECG interpretation systems such as MUSE. Our primary objective is to assess the impact of displaying the ECHONeXT interpretation on 12-lead ECGs on the time to diagnosis of Structural Heart Disease (SHD) among newly referred patients at MHI. We will compare the time interval from the initial ECG to SHD diagnosis by transthoracic echocardiogram (TTE) or magnetic resonance imaging (MRI) between patients in the intervention arm (where ECHONeXT prediction of SHD and TTE priority recommendation are displayed) and patients in the control arm (where ECHONeXT prediction and recommendation are hidden). The main secondary objective is to evaluate the rate of SHD detection on TTE or MRI among newly referred patients. We also aim to assess the delay between the time of the first ECG opened in the platform and the TTE or MRI evaluation among newly referred patients at high or intermediate risk of SHD. By integrating an AI-analysis platform at the point of care and evaluating its impact on ECG interpretation accuracy and prioritization of incremental tests, the HEART-AI study aims to provide valuable insights into the potential of AI in improving cardiac care and patient outcomes.

Artificial Intelligence-assisted Diagnosis

This study aims to investigate whether an AI-assisted decision support can improve the diagnosis of congenital heart diseases on Chest X-ray.

Interventional Structural Registry - LuEbeck

The aim of the registry is to investigate the effect of catheter-assisted procedures in the context of structural heart disease on clinical morbidity and mortality. Furthermore, the long-term prognosis of patients after a catheter-based procedure is to be evaluated. In particular, the following parameters will be documented and the following questions discussed: * Underlying and concomitant diseases of these patients * Evaluation of the methods in relation to the respective disease * Safety - acute and long-term * Effectiveness - periprocedural, hospital and long-term course * Concomitant therapies

WHEN DO WE HAVE to PERFORM CARDIAC MAGNETIC RESONANCE in PATIENTS REFERRED for PREMATURE VENTRICULAR COMPLEXES by THEIR CARDIOLOGISTS

Premature ventricular complexes (PVC) are a common entity affecting approximatively 20% of the general population. It can be discovered incidentally on electrocardiogram (ECG) or associated with symptoms with a wide spectrum from palpitations, chest pain, to syncope. The initial and non invasive assessment includes holter ECG monitoring, a transthoracic echocardiography (TTE) and an exercise stress test to rule out structural heart disease (SHD), and referred to as benign or "idiopathic" ventricular arrhythmias (IVA). However, these exams may fail to identify subtle myocardial abnormalities such as arrhythmogenic right ventricle dysplasia (ARVD), apical hypertrophic cardiomyopathy, healed myocarditis, ischemic or non-ischemic cardiomyopathies. Cardiac magnetic resonance (CMR) imaging is the gold standard modality to assess regional and global ventricular function. It is also a unique modality to non-invasively detect myocardial edema, myocardial fatty replacement, focal and diffuse fibrosis and could potentially identify SHD in patients with PVC. However, the role of CMR is uncertain, recommended in case of atypical presentation or when the initial assessment can't exclude a cardiomyopathy (recommendations class IIa). This study sought to determine whether and when CMR can be performed to provide diagnosis or prognostic information complementary to initial assessment in patients referred for PVC by their cardiologists.

Electrocardiogram-based Artificial Intelligence-assisted Detection of Heart Disease

Atrial fibrillation is an abnormal beating of the heart that can lead to stroke or heart failure. Structural heart diseases are conditions that affect the heart valves or heart muscle and can cause permanent heart damage if left untreated. Sometimes people have atrial fibrillation or structural heart disease and do not know it. The purpose of this study is to evaluate two devices that can predict who has or may develop atrial fibrillation or structural heart disease based on the results of an electrocardiogram.

Prognosis Prediction System of Patients With Cardiovascular and Cerebrovascular Diseases Based on Multi-omics

The etiology and specific pathogenesis of many cardiovascular diseases such as coronary atherosclerosis, cardiomyopathy, atrial fibrillation, and stroke are still unclear. Improving diagnosis and treatment, clarifying the pathogenesis, and providing scientific basis for the prevention and treatment are hot research topics in the study of cardiovascular and cerebrovascular diseases. This study intends to collect clinical data and biological specimen data of patients with cardiovascular and cerebrovascular diseases who meet the inclusion and exclusion criteria, and use multi-omics technology to deeply understand the pathogenic mechanisms of cardiovascular and cerebrovascular diseases and provide new ideas for specific and individualized treatment of patients with cardiovascular and cerebrovascular diseases, to construct early predictive prognostic models and provide a basis for effective treatment of clinical practice in patients with cardiovascular and cerebrovascular diseases.