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

BACKGROUND:

Atrial fibrillation (AF) is the most common cardiac arrhythmia, with a prevalence of 2-4% in the adult population that is increasing exponentially. Catheter ablation is an established treatment for symptomatic AF, but primary ablation success rates range from 60-80%, with a significant proportion of patients requiring reablation. Identification of clinically relevant predictors of ablation success remains a key research challenge.

Diabetes mellitus increases AF risk 1.5-2-fold and is associated with faster progression from paroxysmal to persistent AF. Experimental data suggest that insulin resistance (IR) preceding manifest diabetes may contribute to electrical and structural atrial remodeling through oxidative stress, microvascular dysfunction, and local fibrosis. Despite robust experimental evidence, no prospectively designed study has systematically evaluated the association between HOMA-IR and AF ablation outcomes, particularly in non-diabetic patients where early detection of metabolic abnormalities could have the greatest clinical benefit.

Pulsed field ablation (PFA) represents the newest generation of catheter ablation technology, using irreversible electroporation to achieve selective myocardial ablation with minimal collateral damage. While studies have evaluated the impact of diabetes on radiofrequency ablation outcomes, data on the predictive value of subclinical glucose metabolism disorders in patients undergoing PFA are virtually absent.

The HOMA-IR index (Homeostatic Model Assessment of Insulin Resistance) is a validated, clinically available tool for quantifying insulin resistance, calculated from fasting glucose and insulin levels.

GDF-15 (Growth Differentiation Factor 15) has been identified as a potential link between metabolic and cardiovascular phenotypes, associated with both insulin resistance and AF recurrence risk. Its measurement alongside HOMA-IR may provide deeper insight into pathophysiological mechanisms connecting metabolic abnormalities with atrial electrical instability.

STUDY DESIGN:

Single-arm prospective observational monocentric study at the Cardiocentrum, AGEL Hospital Trinec-Podlesi, Czech Republic, 2026-2028.

ASSESSMENT SCHEDULE:

* Baseline (day of ablation): Fasting blood samples collected during routine preoperative preparation for HOMA-IR calculation (fasting glucose + insulinemia), complete lipid profile, inflammatory markers (hs-CRP, fibrinogen, leukocytes, IL-6, IL-1beta), cardiovascular biomarkers (NT-proBNP), and specific biomarkers (GDF-15). Demographic, clinical, echocardiographic, and ECG parameters recorded. Standardized PFA ablation performed with detailed procedural documentation including recording of intracardiac electrograms (EGMs) for subsequent neural network analysis. * Follow-up (4-5 months post-ablation): Single standard clinical visit including symptom assessment (EHRA score), 12-lead ECG, Holter monitoring (minimum 24 hours), and clinical decision regarding reablation indication.

BLINDING:

The treating physician making the reablation decision is blinded to the HOMA-IR value and other study-specific biomarker results.

STATISTICAL ANALYSIS:

Sample size: \>=120 patients, providing adequate statistical power (beta=0.8, alpha=0.05) to detect a 2-fold relative risk of reablation in patients with elevated HOMA-IR, assuming 10-15% reablation frequency and 40% prevalence of abnormal insulin resistance in non-diabetic cardiovascular patients. Patients stratified by HOMA-IR tertiles. Continuous variables expressed as mean +/- SD or median (IQR) and compared using t-test or Mann-Whitney U test. Categorical variables compared using chi-square or Fisher's exact test. Multivariable logistic regression with adjustment for relevant clinical covariates used to identify independent predictors of reablation. ROC analysis to determine optimal HOMA-IR cut-off value. Final scoring system integrating HOMA-IR with other significant predictors. Deep learning neural network models (CNN/RNN) will be applied to intracardiac electrogram data recorded during ablation to extract electrophysiological features and evaluate their correlation with metabolic status and ablation outcomes. Analysis performed using SPSS v.30 and Python (TensorFlow/PyTorch).

DATA MANAGEMENT:

All data anonymized and processed in compliance with GDPR. Electronic data stored in password-protected secure database. Patient identification codes maintained separately from research data. Access restricted to authorized research team members only.

• Optimal HOMA-IR Cut-Off Value for Predicting Reablation [Срок оценки: Baseline HOMA-IR to reablation decision at 4-5 months]
• Association Between GDF-15 and Ablation Outcomes [Срок оценки: Baseline GDF-15 to reablation decision at 4-5 months]
• Area Under ROC Curve (AUC) of Multiparametric Predictive Model for Reablation Indication [Срок оценки: Baseline parameters to reablation decision at 4-5 months]
• Area Under ROC Curve (AUC) of Neural Network Intracardiac Electrogram Model for Predicting Reablation [Срок оценки: Intracardiac signals recorded during ablation procedure (baseline) to reablation decision at 4-5 months]