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.

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.

Inclusion Criteria:

• Age >= 18 years
• Undergoing first catheter ablation for atrial fibrillation using pulsed field ablation (PFA)
• No known diagnosis of diabetes mellitus
• Signed written informed consent

Exclusion Criteria:

• Known diabetes mellitus or active antidiabetic treatment
• Previous atrial fibrillation ablation by any method
• Pregnancy
• Severe renal insufficiency (eGFR < 30 mL/min)
• Other severe comorbidities limiting follow-up
• Inability to provide informed consent

Non-diabetic adults with atrial fibrillation (paroxysmal or persistent) indicated for first catheter ablation using pulsed field ablation at a single tertiary cardiology center (Cardiocentrum, AGEL Hospital Trinec-Podlesi, Czech Republic).

Anonymized dataset of metabolic, demographic, and electrophysiological parameters may be made available after study completion and primary publication, upon reasonable request to the Principal Investigator.