Integrated Diagnostics for Atrial Fibrillation Recurrence: Exploratory Results from the PLACEBO Trial - Aristi Boulmpou; Theodoros Moysiadis; Georgios Zormpas; Eleftherios Teperikidis; Konstantina Tsioni; Maria Toumpourleka; Maria Zidrou; Georgios Giannakoulas; Vassilios Vassilikos; Christodoulos Papadopoulos (Diagnostics)
Background: Atrial fibrillation is a prevalent arrhythmia with significant morbidity and recurrence challenges. Paroxysmal atrial fibrillation (PAF) is characterized by episodic occurrences and unpredictable recurrences; therefore, it demands innovative diagnostic approaches to predict relapses and guide management. Objectives: This pilot, exploratory study evaluates the feasibility and prognostic value of integrating cardiopulmonary exercise testing (CPET), echocardiographic indices, and plasma biomarkers for predicting PAF recurrence. Methods: The PLACEBO trial is a single-center, prospective observational study of 73 adults with PAF in sinus rhythm at baseline. Comprehensive assessments included CPET, transthoracic echocardiography, 24 h electrocardiographic Holter monitoring with heart rate variability (HRV) metrics, and plasma biomarkers, such as galectin-3 (GAL3). Recurrence was defined as any documented AF episode lasting ≥30 s within 12 months of follow-up. Results: Binary logistic regression revealed that the standard deviation of RR intervals (SDRR) and GAL3 were significant predictors of recurrence. Particularly, higher SDRR [odds ratio (OR): 1.061, p = 0.021] and GAL3 > 10.95 ng/mL (OR: 5.206, p = 0.006) were associated with recurrence. Moreover, lower right ventricular fractional area change (RV FAC) exhibited a marginally significant association with recurrence (OR: 0.927, p = 0.062). CPET parameters demonstrated limited prognostic value in this cohort. Conclusion: This pilot study demonstrates that integrating novel echocardiographic indices, biomarkers, and HRV metrics is feasible and may provide valuable prognostic insights for PAF recurrence. Larger multicenter studies are needed to validate these findings and optimize personalized risk stratification strategies.