Automatic Echocardiographic Assessment of Left Atrial Function for Prediction of Low-Voltage Areas in Non-Valvular Atrial Fibrillation.

PURPOSE

Left atrial low-voltage areas (LA-LVAs) identified by 3D-electroanatomical mapping are crucial for determining treatment strategies and prognosis in patients with atrial fibrillation (AF). However, convenient and accurate prediction of LA-LVAs remains challenging. This study aimed to assess the viability of utilizing automatically obtained echocardiographic parameters to predict the presence of LA-LVAs in patients with non-valvular atrial fibrillation (NVAF).

PATIENTS AND METHODS

This retrospective study included 190 NVAF patients who underwent initial catheter ablation. Before ablation, echocardiographic data were obtained, left atrial volume and strain were automatically calculated using advanced software (Dynamic-HeartModel and AutoStrain). Electroanatomic mapping (EAM) was also performed. Results were compared between patients with LA-LVAs ≥5% (LVAs group) and <5% (non-LVAs group).

RESULTS

LA-LVAs were observed in 81 patients (42.6%), with a significantly higher incidence in those with persistent AF than paroxysmal AF (55.6% vs 19.3%, 0.001). Compared with the non-LVAs group, the LVAs group included significantly older patients, lower left ventricular ejection fraction, higher heart rate, and higher E/e' ratio ( <0.05). The LVAs group exhibited higher left atrial volume index (LAVi) and lower left atrial reservoir strain (LASr) ( <0.001). In multivariate analysis, both LAVi and LASr emerged as independent indicators of LVAs (OR 0.85; 95% CI 0.80-0.90, <0.001) and (OR 1.15, 95% CI 1.02-1.29, =0.021). ROC analysis demonstrated good predictive capacity for LA-LVAs, with an AUC of 0.733 (95% CI 0.650-0.794, <0.001) for LAVi and 0.839 (95% CI 0.779-0.898, <0.001) for LASr.

CONCLUSION

Automatic assessment of LAVi and LASr presents a promising non-invasive modality for predicting the presence of LA-LVAs and evaluating significant atrial remodeling in NVAF patients. This approach holds potential for aiding in risk stratification and treatment decision-making, ultimately improving clinical outcomes in patients.