Spatial Changes in the Atrial Fibrillation Wave-Dynamics After Using Antiarrhythmic Drugs: A Computational Modeling Study

Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation

The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.

Clinical Usefulness of Virtual Ablation Guided Catheter Ablation of Atrial Fibrillation Targeting Restitution Parameter-Guided Catheter Ablation: CUVIA-REGAB Prospective Randomized Study

BACKGROUND AND OBJECTIVES

We investigated whether extra-pulmonary vein (PV) ablation targeting a high maximal slope of the action potential duration restitution curve (Smax) improves the rhythm outcome of persistent atrial fibrillation (PeAF) ablation.

METHODS

In this open-label, multi-center, randomized, and controlled trial, 178 PeAF patients were randomized with 1:1 ratio to computational modeling-guided virtual Smax ablation (V-Smax) or empirical ablation (E-ABL) groups. Smax maps were generated by computational modeling based on atrial substrate maps acquired during clinical procedures in sinus rhythm. Smax maps were generated during the clinical PV isolation (PVI). The V-Smax group underwent an additional extra-PV ablation after PVI targeting the virtual high Smax sites.

RESULTS

After a mean follow-up period of 12.3±5.2 months, the clinical recurrence rates (25.6% vs. 23.9% in the V-Smax and the E-ABL group, p=0.880) or recurrence appearing as atrial tachycardia (11.1% vs. 5.7%, p=0.169) did not differ between the 2 groups. The post-ablation cardioversion rate was higher in the V-Smax group than E-ABL group (14.4% vs. 5.7%, p=0.027). Among antiarrhythmic drug-free patients (n=129), the AF freedom rate was 78.7% in the V-Smax group and 80.9% in the E-ABL group (p=0.776). The total procedure time was longer in the V-Smax group (p=0.008), but no significant difference was found in the major complication rates (p=0.497) between the groups.

CONCLUSIONS

Unlike a dominant frequency ablation, the computational modeling-guided V-Smax ablation did not improve the rhythm outcome of the PeAF ablation and had a longer procedure time.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02558699.

Network Pharmacology and Pharmacological Mechanism of CV-3 in Atrial Fibrillation

The high fatality and disability rate of atrial fibrillation (AF) strongly promote the development of pathogenesis and treatment of AF that is of great value. The present research attempted to clarify potential mechanisms of Mujiangzi oil (CV-3) in treating AF by constructing an AF cardiomyocytes model and using a network pharmacology approach. The experiment was divided into 4 groups: control, an AF model, AF + CV-3-treated, and the AF + verapamil group. Flow cytometry and the MTT assay were employed to detect cell apoptosis and cell viability, respectively. The main active components of CV-3 and predicted targets were obtained firstly, and molecular docking was performed. In the AF model, the cell apoptosis was aggravated, but inhibited in the CV-3-treated group. In addition, the cell viability was recovered after CV-3 treatment compared with the model group. Five potential active compounds of CV-3 were collected, including effective ingredients N-decanoic acid, spathulenol, copaene, β-panasinsene, and eucalyptol. Among them, N-decanoic acid and spathulenol was demonstrated to bind to PTGS2 with binding energy of −4.08 and −7.09 kcal/mol, respectively, and hydrogen bonds interaction were found. The present study indicated that CV-3 could alleviate AF cardiomyocytes apoptosis and improve cardiomyocytes viability, and N-decanoic acid and spathulenol may be the key components of CV-3 in treatment of AF by regulating PTGS2. This study provided the possible target PTGS2 and the understanding of molecular mechanisms of CV-3 in treating AF.