Digital Twin Technology Predicts Amiodarone Effectiveness in Atrial Fibrillation Patients
Key Insights
A digital twin of the left atrium, created by merging CT images with electroanatomical mapping data, can simulate the effects of amiodarone (AMD) on atrial fibrillation (search) (AF).
The digital twin accurately predicted the effectiveness of amiodarone in maintaining sinus rhythm in patients post-AF ablation, identifying those likely to benefit from the drug.
Virtual amiodarone tests using the digital twin platform showed dose-dependent electrophysiological changes, offering insights into optimal drug concentrations.
Digital twin technology is showing promise in predicting the effectiveness of amiodarone (AMD) treatment for atrial fibrillation (search) (AF) patients, potentially revolutionizing personalized medicine in cardiology. Researchers have developed a digital twin of the left atrium (LA) that can simulate the effects of AMD and predict patient outcomes following AF ablation (AFCA).
The study, approved by the Institutional Review Board of the Severance Cardiovascular Hospital, Yonsei University Health System, enrolled 115 patients who underwent AFCA and were subsequently treated with AMD for recurrent or symptomatic atrial tachycardia (search) (AT). The digital twin was created by merging pre-ablation CT images with electroanatomical mapping (EAM) data obtained during AFCA. This allowed researchers to construct a 3D model of the LA, incorporating electrophysiological characteristics such as voltage, fibrosis state, fiber orientation, and conductivity.
Creating the Digital Twin
The structural components of the geometry were represented as triangular meshes, with each node forming a human atrial myocyte. The fibrotic state was determined by applying the obtained virtual voltage values to a probability function, to distinguish fibrotic cells from normal cells. Conductivity was set based on orientation, differentiation between the longitudinal and transverse directions, and fibrotic and non-fibrotic tissues. The model was based on a modified Courtemanche-Ramirez-Nattel (mCRN) model to characterize the system, which mathematically represents the various ion currents within human atrial myocytes.
Virtual Amiodarone Intervention
Using the CUVIA digital twin, circular lesions of 2 mm width were created on both sides of the PVs. To conceptualize the conditions under which AMD acts within the body at subtoxic ranges, low, high, and toxic doses of AMD were defined as 1.6 μM (minimal effective concentration), 3.9 μM (maximal effective concentration), and 8.0 μM (toxic concentration) respectively, based on the therapeutic range of AMD. The degree of functional blockade was evaluated using Hill’s equation.
Predictive Accuracy and Clinical Implications
The digital twin accurately predicted the effectiveness of AMD in maintaining sinus rhythm in patients post-AFCA. Patients were categorized into Effective and Ineffective groups based on virtual AMD test results. The Effective group experienced AF termination at least once at therapeutic concentrations in the simulation. Kaplan-Meier analysis demonstrated significant differences in AF recurrence-free survival between the two groups (P < 0.05).
Electrophysiological Insights
Virtual AMD tests revealed dose-dependent electrophysiological changes. Ramp pacing was used to induce AF under baseline, low-, high-, and toxic-dose AMD conditions. Measurements of action potential duration (APD90) and peak upstroke velocity showed a clear trend with increasing AMD concentrations. Regional analysis of Smax and dominant frequency (DF) values further elucidated the drug's impact on atrial electrophysiology.
Future Directions
These findings suggest that digital twin technology holds great potential for personalizing AF treatment strategies. By simulating the effects of antiarrhythmic drugs like amiodarone, clinicians can identify patients most likely to benefit from specific therapies, potentially reducing recurrence rates and improving overall outcomes. Further research is needed to validate these results in larger, multi-center studies and to explore the application of digital twins to other cardiovascular conditions.