Clinical trials

The aim of this study is to enhance the predictability of therapeutic success in transcatheter tricuspid valve intervention (TTVI) for patients with severe tricuspid regurgitation (TR). This will be achieved through automated analyses of pre-interventional computed tomography (CT) scans. Severe tricuspid regurgitation is associated with poor patient outcomes. In advanced stages, pharmacological therapy becomes ineffective, and surgical intervention carries a high mortality risk. Given this clinical challenge, catheter-based treatment of the tricuspid valve has become a focal point of research. One well-established treatment strategy is percutaneous tricuspid valve intervention, which aims to reduce regurgitation either through annuloplasty, leaflet-based edge-to-edge repair or valve replacement. This approach has been shown to significantly decrease the severity of regurgitation, leading to a dramatic reduction in symptom burden and a marked improvement in quality of life. However, predicting which patients will benefit most from TTVI and determining the optimal technique for each individual remain largely unresolved challenges. Artificial intelligence (AI)-powered software, such as heart.ai by LARALAB (Munich), enables automated measurement of anatomical structures captured via CT imaging. This technology already allows for rapid and precise assessment of cardiac chambers and the tricuspid annulus throughout the entire cardiac cycle, facilitating a comprehensive three-dimensional evaluation of right heart anatomy. To refine patient selection and optimize procedural strategies for TR treatment, the researcher work a multi-center collaboration to analyze treatment outcomes and patient response to specific therapeutic approaches.

CASTLE-VT is a randomized evaluation of prophylactic ablative treatment of arrhythmogenic ventricular scar in patients referred for HTx evaluation and diagnosed with ICM. Ablation will be performed with the use of a substrate-based approach in which the myocardial scar is mapped and ablated while the heart remains predominantly in sinus rhythm. The primary end point is the composite of all-cause mortality, worsening of HF requiring prioritized transplantation or LVAD implantation. The main secondary study end points are all-cause mortality, cardiovascular mortality, incidence of implantable cardioverter-defibrillator (ICD) therapy, hospitalizations, Quality of life, time to first ICD therapy, number of device-detected ventricular tachycardia/ventricular fibrillation episodes, LV function, and exercise tolerance. CASTLE-VT will randomize 160 patients with a follow up period of 2 years.

The aim of this trial is to evaluate if an enhanced information by using a mobile App can increase subjective information level in patients with severe aortic stenosis and may therefore improve patient-reported outcomes.

Aortic valve stenosis (AS) affects 2-7% of persons \> 65 years, symptoms include: angina and dyspnea, dizziness and syncope. Coincidence of coronary artery disease (CAD), also presenting with angina and dyspnea, in patients with AS between 40 - 65% . Angina in AS caused by significant reduction of coronary flow reserve (CFR). CFR is the ratio of maximal flow increase in the coronary vessel bed during maximal hyperaemia (medically or exercise induced). FFR (fractional flow reserve) use in patients with AS potentially invalid due to dysfunctional CFR leading to potential undertreatment of CAD in these patients.CFR disturbance in AS mainly due to myocardial overload causing concentric hypertrophy, increased oxygen consumption and neurohormonal activation leading to increased vascular resistance. Current studies are investigating the validity of FFR and iFR in AS patients. Recent data demonstrate very good correlation between FFR and iFR derived values to PET myocardial perfusion imaging values in patients with no evidence of AS. Our study aims to investigate the diagnostic performance of FFR and iFR in intermediate-grade coronary stenosis in patients with severe aortic valve disease and correlate FFR- and iFR derived values with those extracted from PET-perfusion Imaging.