Why the study?
Reliable outcome prediction after AF catheter ablation is important for shared decision-making, and combining left atrial electromechanical remodelling data with clinical data could improve prediction accuracy.
Does a predictive model combining clinical, left atrial electromechanical remodelling (MRI), and electroanatomic mapping data improve the prediction of arrhythmia recurrence in patients undergoing AF ablation compared to pre-existing clinical risk scores?
Cohort (n=123)
No
Does a predictive model combining clinical, left atrial electromechanical remodelling (MRI), and electroanatomic mapping data improve the prediction of arrhythmia recurrence in patients undergoing AF ablation compared to pre-existing clinical risk scores?
Effect estimate: c-statistic 0.733 (95% CI 0.545-0.894)
Absolute Event Rate: 0.733% vs 0.653%
Integrating clinical data with MRI-derived left atrial ejection fraction and electroanatomic mapping-derived mean left atrial voltage improves the prediction of atrial fibrillation recurrence following catheter ablation compared to existing clinical risk scores.
May enhance post-ablation recurrence prediction beyond clinical scores; hypothesis-generating and requires prospective validation before practice change.
BACKGROUND: Reliable outcome prediction following atrial fibrillation (AF) catheter ablation is important to inform shared decision-making. The extent of atrial electromechanical remodelling can be determined through magnetic resonance imaging and electroanatomic mapping data analysis. Combining these data with clinical data could improve the accuracy of outcome prediction models. OBJECTIVE: To investigate how left atrial electromechanical remodelling data can be utilised to predict outcomes for first-time and repeat AF ablation. METHODS: A retrospective analysis of 123 patients undergoing first-time ablation was conducted. Clinical, imaging and electroanatomic mapping variables associated with arrhythmia recurrence were identified using univariable logistic regression and combined into a multivariable model. Predictive ability for treatment response was examined using receiver-operator characteristic curve, time-to-event analyses and compared to pre-existing clinical risk scores. RESULTS: A multivariable model comprising age, weight, hypertension, left atrial ejection fraction and mean left atrial voltage attained a c-statistic of 0.733 (95% CI 0.545–0.894) for predicting arrhythmia recurrence after one procedure, and 0.680 (95% CI 0.509–0.852) for repeat ablation. Kaplan-Meier analysis demonstrated a higher rate of arrhythmia recurrence amongst patients identified as high-risk (log-rank p = 0.010). Amongst pre-existing risk scores, CAAP-AF had the highest predictive value for predicting index procedure response (AUC 0.653, 95% CI 0.527–0.779). CONCLUSION: The model developed in this study demonstrated the potential for improved index AF ablation outcome prediction accuracy compared to pre-existing risk scores. Combined models integrating data measuring the extent of atrial electromechanical remodelling could optimise patient selection for repeat ablation, offering the potential to improve outcomes and reduce the volume of unnecessary procedures.
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Bodagh et al. (2025) conducted a cohort in Atrial fibrillation (n=123). Multivariable predictive model (clinical, MRI, and electroanatomic mapping data) vs. Pre-existing clinical risk scores (e.g., CAAP-AF) was evaluated on Prediction of arrhythmia recurrence after one procedure (c-statistic/AUC) (c-statistic 0.733, 95% CI 0.545-0.894).
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