Clinical Research Directory

Drug-Coated Balloon Primary PCI in ST-Segment Elevation Myocardial Infarction

Drug-eluting stent (DES)-based primary percutaneous intervention (pPCI) has been established as the standard of care for patients presenting with ST-segment elevation myocardial infarction (STEMI), having demonstrated superiority over thrombolysis, plain balloon angioplasty, and bare-metal stents. Recently, the use of drug-coated balloons (DCB) has expanded dramatically across a variety of anatomical and clinical settings, including de novo coronary lesions. A DCB-based pPCI strategy may simplify the procedure and mitigate the risks of inadequate stent sizing due to spasm or large thrombus burden, acute stent thrombosis, distal embolization, no reflow, and the relatively higher incidence of late stent-related adverse events compared with elective PCI. Despite these theoretical advantages, data on the safety and efficacy of DCB-based pPCI in STEMI remains limited. The aim of this registry is to explore procedural and clinical outcomes of patients with STEMI treated with a DCB-based pPCI strategy.

Educational Video to Improve Understanding and Reduce Anxiety in Heart Attack Patients

Patients who are diagnosed with a heart attack are often confronted with an unexpected hospital admission and an urgent heart catheterization. In this stressful situation, anxiety is common and understanding of the procedure may be limited, even after standard verbal and written explanations. This study investigates whether a short educational video, shown in addition to standard medical information, can help patients better understand the heart catheterization procedure and reduce anxiety before the intervention. Patients will be randomly assigned to receive either standard information alone or standard information plus the educational video. The study will assess patients' understanding of the procedure, their level of anxiety, and their satisfaction with the information provided. The results may help improve patient education and support in acute cardiac care settings.

Artificial Intelligence Based Timing, Infarct Size and Outcomes in Acute Coronary Occlusion Myocardial Infarction

The present study is practice-driven and merely observational and prospective. In clinical routine, patients who suffer from suspected ACS and do not show ST elevation in the ECG, different timing proposals in the guidelines and logistically driven differences lead to considerably variable timings in invasive coronary anatomy assessments. This handling may lead to larger infarct sizes when OMI is overseen. Therefore, the present study aims to observe a) whether an AI model is capable of correctly identify OMI in eligible patients and b) if in these patients troponin peak levels vary depending on the elapsed time between OMI diagnosis and coronary intervention. As the model has not been established yet clinically and in the guidelines, it is safe to assume the usual pathway from first medical contact to specialist's attention is undertaken. When a patient presents in an emergency department or places an emergency call, the physicians assess the situation as usal and as stated in the current guidelines1. If no STEMI is confirmed, the NSTE-ACS protocol is started. The patients who are ruled out for ACS are excluded from the final analysis (screening). In this case, the AI model is tested on their ECG in order to assess whether there are false positives. The patients which are in the ACS "rule-in" trail and undergo final coronary angiography will naturally be divided in patients which were classified as OMI and as non-OMI by the AI model. Furthermore, they will present a different "Time from OMI diagnosis to PCI) and variable troponin peak levels. By leveraging this natural variability, a practical distinction and multiple analyses can be done: 1. The feasibility of AI-powered ECG interpretation in the care of patients with suspected ACS and without clear ST-elevation infarction 2. The accuracy of AI-powered ECG interpretation in detecting OMI compared to the classical STEMI criteria 3. How infarct size correlates with different ECG readings by AI and (hypothesis generating) if changing the clinical practice could lead to a benefit in patients with suspected OMI.

Prospective Validation of the STOPSHOCK Score - Artificial Intelligence Based Predictive Scoring System to Identify the Risk of Developing Cardiogenic Shock (CS) in Patients Suffering From Acute Coronary Syndrome (ACS)

Cardiogenic shock (CS) is a severe complication of acute coronary syndrome (ACS) with mortality approaching 50% despite the use of percutaneous mechanical circulatory support devices (pMCS). Identifying high-risk patients prior to the development of CS could allow pre-emptive use of pMCS possibly preventing CS. For this purpose, we derived and externally validated a machine learning score to predict in-hospital CS in patients with ACS with c-statistics: 0.844 (95% confidence interval, 0.841-0.847). STOPSCHOCK score is available as a web or smartphone application. The aim of this study is to prospectively validate the STOPSHOCK score on a large cohort of ACS patients in a real- world clinical environment.

Genotype-Guided Abbreviated DAPT Versus Un-Guided De-escalation Therapy in Patients With ACS and HBR

The aim of this study is to assess the safety and efficacy of the CYP2C19 genotype-guided abbreviated dual antiplatelet therapy (DAPT) strategy versus the un-guided stepwise intensity de-escalation of DAPT strategy in patients with acute coronary syndrome (ACS) and high bleeding risk (HBR) undergoing percutaneous coronary intervention (PCI).

Efficacy and Safety Study of Vicagrel in Patients With Acute Coronary Syndrome (ACS) Undergoing Percutaneous Coronary Intervention (PCI)

This is a multi-center, randomized, double-blind, double-dummy, parallel-controled, phase III trial, aiming to evaluate the effectiveness and safety of long-term continuous administration of Vicagrel capsules in ACS patients undergoing PCI.