Insulin Resistance as a Predictor of Pulsed Field Ablation Success in Atrial Fibrillation (HOMA-PULSE)

Rövid összefoglaló (regiszter, angolul)

This study investigates whether insulin resistance, a metabolic condition where the body's cells respond poorly to insulin, can predict the success of atrial fibrillation (AF) ablation using pulsed field ablation (PFA) technology. Atrial fibrillation is the most common heart rhythm disorder, affecting 2-4% of adults. Catheter ablation is an effective treatment, but 20-40% of patients require a repeat procedure. Identifying patients at higher risk of ablation failure could improve treatment planning and outcomes. Scientific evidence suggests that insulin resistance - which can exist for years before diabetes develops - may contribute to electrical and structural changes in the heart that promote AF. However, no prospective study has systematically examined whether insulin resistance measured by the HOMA-IR index predicts ablation outcomes, particularly with the newest pulsed field ablation technology. HOMA-PULSE is a prospective observational study enrolling at least 120 non-diabetic patients undergoing their first AF ablation using pulsed field ablation at the Cardiocentrum, AGEL Hospital Trinec-Podlesi, Czech Republic. On the day of ablation, fasting blood samples are collected as part of routine preoperative care. A portion of these samples is used to measure insulin resistance (HOMA-IR index, calculated from fasting glucose and insulin levels) along with additional biomarkers including GDF-15, hs-CRP, NT-proBNP, IL-6, and IL-1beta. Detailed procedural and clinical data are recorded. Patients attend a single follow-up visit at 4-5 months post-ablation - a standard part of clinical care after AF ablation. The primary outcome is the clinical decision regarding need for repeat ablation (reablation), made by the treating physician blinded to the HOMA-IR result. The study does not involve any additional procedures, visits, or interventions beyond standard clinical care. The only research-specific element is the additional laboratory analysis of biomarkers from blood samples that would be drawn regardless of study participation. Additionally, intracardiac electrograms recorded during the ablation procedure will be analyzed using deep learning neural network models to extract electrophysiological features and evaluate whether insulin resistance has a detectable electrophysiological signature that can be captured by artificial intelligence. If a significant association between insulin resistance and ablation outcomes is confirmed, this could lead to new strategies combining ablation with metabolic optimization to improve success rates.

Mit vállalsz

• Vizsgálat típusa: Megfigyeléses
• Tervezett résztvevők: 120
• 2026. ápr. 1. és az elsődleges befejezés (2028. dec.) között
• 1 helyszín, 1 országban
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