Combining clinical and left atrial electromechanical remodelling data: potential to improve atrial fibrillation ablation outcome prediction

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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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