Does a digital twin-guided virtual amiodarone test predict 1-year clinical recurrence of atrial fibrillation in patients receiving amiodarone post-ablation?
A patient-specific digital twin model can predict the clinical efficacy of amiodarone, potentially allowing for personalized antiarrhythmic drug selection post-ablation.
It would be clinically valuable if the efficacy of antiarrhythmic drugs could be simulated in advance. We developed a digital twin to predict amiodarone efficacy in high-risk atrial fibrillation (AF) patients post-ablation. Virtual left atrium models were created from computed tomography and electroanatomical maps to simulate AF and evaluate its response to varying amiodarone concentrations. As the amiodarone concentration increased in the virtual setting, action potential duration lengthened, peak upstroke velocities decreased, and virtual AF termination became more frequent. Patients were classified into effective (those with virtually terminated AF at therapeutic doses) and ineffective groups. The one-year clinical outcomes after AF ablation showed significantly better results in the effective group compared to the ineffective group, with AF recurrence rates of 20.8% vs. 45.1% (log-rank p = 0.031, adjusted hazard ratio, 0.37 0.14-0.98; p = 0.046). This study highlights the potential of a digital twin-guided approach in predicting amiodarone's effectiveness and improving personalized AF management. Clinical Trial Registration Name: The Evaluation for Prognostic Factors After Catheter Ablation of Atrial Fibrillation: Cohort Study, Registration number: NCT02138695. The date of registration: 2014-05. URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02138695.
Hwang et al. (Wed,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: