98%
921
2 minutes
20
Background: Atrial fibrillation (AF) is a common arrhythmia causing serious health complications. Catheter ablation is used to treat AF, but the risk of recurrence is high. The DR-FLASH score, which predicts recurrence, has limited sensitivity and specificity. Left atrial mechanical dispersion (LAMD) is an effective index that reflects the synchronisation of left atrial mechanical movement and the degree of early remodelling.
Objective: This study aimed to assess the predictive value of LAMD and the DR-FLASH score for late recurrence after ablation.
Methods: The general clinical data, structural, and functional parameters of both the recurrence group and the non-recurrence group were collected. Univariate and multivariate logistic regression analysis explored the independent predictors of late recurrence after AF ablation. The ROC curve was used to evaluate the effectiveness of LAMD and DR-FLASH score on recurrence after AF ablation.
Results: The study included 102 AF patients and 31 healthy controls. LAMD was found to be an independent predictor of recurrence, and combining it with the DR-FLASH score improved predictive efficiency, with an area under the curve (AUC) of 0.875, sensitivity of 77.8%, and specificity of 88.0%.
Conclusion: LAMD combined with the DR-FLASH score can help identify high-risk patients and potentially reduce recurrence rates.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1080/00015385.2025.2516944 | DOI Listing |
Acta Cardiol
August 2025
Department of Ultrasound, The First Clinical Medical Science College of China Three Gorges University & Yichang Central People's Hospital, Yichang, Hubei, China.
Background: Atrial fibrillation (AF) is a common arrhythmia causing serious health complications. Catheter ablation is used to treat AF, but the risk of recurrence is high. The DR-FLASH score, which predicts recurrence, has limited sensitivity and specificity.
View Article and Find Full Text PDFEur Heart J Digit Health
March 2025
Department of Cardiology, Beijing Chaoyang Hospital, Capital Medical University, No. 8 Workers's Stadium South Road, Chaoyang District, Beijing 100020, China.
Aims: We aimed to develop an artificial intelligence (AI) algorithm capable of accurately predicting the presence of left atrial low-voltage areas (LVAs) based on sinus rhythm electrocardiograms (ECGs) in patients with atrial fibrillation (AF).
Methods And Results: The study included 1133 patients with AF who underwent catheter ablation procedures, with a total of 1787 12-lead ECG images analysed. Artificial intelligence-based algorithms were used to construct models for predicting the presence of LVAs.
J Cardiovasc Electrophysiol
September 2024
Department of Cardiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Objectives: We aimed to construct an artificial intelligence-enabled electrocardiogram (ECG) algorithm that can accurately predict the presence of left atrial low-voltage areas (LVAs) in patients with persistent atrial fibrillation.
Methods: The study included 587 patients with persistent atrial fibrillation who underwent catheter ablation procedures between March 2012 and December 2023 and 942 scanned images of 12-lead ECGs obtained before the ablation procedures were performed. Artificial intelligence-based algorithms were used to construct models for predicting the presence of LVAs.
Clin Res Cardiol
January 2025
Translational Cardiology Group, Health Research Institute of Santiago de Compostela (IDIS), University Clinical Hospital of Santiago de Compostela, University of Santiago de Compostela (USC), Travesía da Choupana S/N, 15706, Santiago de Compostela, A Coruña, Spain.
Am J Cardiol
February 2024
Division of Cardiology, Osaka Rosai Hospital, Sakai, Osaka, Japan.