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NCT07197736
Heart disease is the leading cause of death in the United States, and echocardiography (or "echo") is the most common way doctors look at the heart. Echo is safe, painless, and can detect major heart problems, including weak heart pumping and valve disease. Valve disease, especially aortic stenosis (narrowing) and mitral regurgitation (leakage), is common in older adults but often goes undiagnosed. While echo is the main tool for finding valve problems, it takes time, requires expert training, and results can vary between readers. Recent advances in artificial intelligence (AI), especially deep learning (DL), have shown promise in automatically analyzing heart images. However, past research hasn't fully tackled key echo techniques-like color Doppler and spectral Doppler-that are crucial for measuring how blood moves through heart valves. AI tools also face challenges in being used in everyday medical practice because of workflow issues, lack of real-world testing, and concerns about how the algorithms make decisions. At Columbia University Irving Medical Center, researchers have built a large database of heart tests over the last six years and developed AI programs to analyze echocardiograms. The current study will test whether providing AI analysis to cardiologists in real time during echo reading can make the process faster and more consistent.
NCT07516145
Valvular heart disease (VHD), caused by abnormalities in heart valves, can lead to severe complications such as heart failure and death, with approximately 220 million affected patients worldwide. The prevalence of VHD continues to grow alongside the aging global population. Transcatheter heart valve interventions have emerged as minimally invasive alternatives, offering benefits like shorter recovery times and reduced discomfort. However, current manual catheter-based techniques are complex, highly dependent on clinicians' expertise, and involve significant physical risk due to prolonged exposure to X-ray radiation and cumbersome protective gear. To address these challenges, a novel, universal intracardiac robotic system is proposed to improve precision, safety, and procedural efficiency. This system integrates a high-dexterity, load-capacity catheter instrument, a modular concentric robotic platform, and an augmented reality (AR) navigation interface. The catheter's design balances flexibility for navigating complex intracardiac paths with the rigidity needed for device deployment. The robotic platform's modular architecture enhances versatility, enabling control across various procedures and anatomical variations, while the AR system facilitates intuitive preoperative planning and real-time intraoperative guidance through multimodal image fusion. The core innovation lies in overcoming existing limitations: balancing catheter flexibility and load capacity, expanding robotic system adaptability for different valve procedures, and improving integration with imaging modalities like computed tomography, transesophageal echocardiogram, and fluoroscopy. The project aims to develop sophisticated models for instrument design, control strategies for multi-instrument coordination, and advanced navigation tools. These technological advancements are intended to elevate the clinical utility of robotic intracardiac interventions, making them safer, more efficient, and easier to adopt widely. By establishing a systematic approach for intelligent, multimodal, robotic-assisted valvular procedures, this work promises significant contributions to minimally invasive cardiology and holds substantial potential for clinical translation.
NCT06902922
Tricuspid regurgitation (TR) is gaining increasing attention within the cardiological community due to its poor prognosis, challenging clinical presentation and difficult treatment. TR causes decreased forward cardiac output and increased intravascular pressure upstream, which lead to peripheral oedema, ascites, hepatic congestion and kidney failure. The microbiota is also getting increasing attention and changes in microbiota have been already associated with cardiovascular disease. The impact of hemodynamic effects of TR on the gut microbiota however is still unknown. Patients affected by TR frequently complain abdominal distension and anorexia. We hypothesize that, due to increased venous congestion, TR may induce impaired gut function with modification in the microbiota and that TR correction may induce reverse changes. This study will enroll patients treated with Transcatheter Tricuspid Edge-to-Edge Repair (T-TEER) at the Valve Center of San Raffaele Hospital due to severe TR. In addition to the standard of care, before T-TEER, for all patients 2 additional blood samples, 1 urine and 1 fecal sample will be collected. 3 months after the procedure, all patients will be re-assessed at the Valve Center outpatient clinic as standard of care. At this stage for all patients 2 blood samples, 1 urine and 1 fecal sample will be collected again for the purpose of the study. The microbiota metabolites of patients after 3 months from the procedure will be compared to those at baseline according to the degrees of residual TR. To assess the reproducibility of the microbiota results and to explore an intrinsic short-term variation in microbiome composition within single patients, a subgroup of 30 patients will undergo a low intervention substudy repeating the measurements (2 blood, 1 urine and 1 fecal) within 10 days, both at baseline and at follow-up.
NCT03700918
The DaVingi™ System is a percutaneous trans-catheter device delivered using right heart catheterization through the right internal jugular vein. The DaVingi™ System is designed for performing ring annuloplasty by using a Ring Delivery System to place a complete, flexible fabric ring around the annulus of the atrial side of the tricuspid valve. Fluoroscopy and echocardiography are used to monitor the ring placement procedure.