Computational modeling: What does it tell us about atrial fibrillation therapy?

Area of Science:

• Cardiac Electrophysiology
• Computational Biology
• Medical Technology

Background:

• Atrial fibrillation (AF) is a complex arrhythmia affecting millions, with diverse causes and suboptimal treatment efficacy.
• Current understanding of AF pathogenesis and antiarrhythmic drug effects is incomplete, hindering personalized medicine.
• Technological advancements offer new insights into AF mechanisms and progression.

Purpose of the Study:

• To review recent advances in quantitative understanding of AF using mathematical and computational models.
• To explore how multi-scale cardiac modeling can elucidate AF mechanisms and guide therapy.
• To highlight the application of computational models in improving AF patient stratification and treatment.

Main Methods:

• Utilizing multi-scale cardiac modeling and simulation to integrate structural and functional data.
• Employing detailed, mechanistic cell/tissue-level models to study AF mechanisms and therapy.
• Incorporating patient-specific variability into computational models for personalized insights.
• Developing whole-atria and patient-level models for improved understanding and treatment strategies.

Main Results:

• Mathematical models provide quantitative insights into AF pathogenesis and dynamics.
• Computational models help identify predominant AF mechanisms in specific patient subgroups.
• Whole-atria models show promise for optimizing catheter ablation strategies.
• Patient-level models are emerging for clinical application in AF management.

Conclusions:

• Computational modeling offers an integrative framework to address challenges in understanding AF.
• Multi-scale modeling enhances comprehension of AF mechanisms and facilitates the development of targeted therapies.
• Future extensions of patient-level models hold significant potential for personalized AF treatment.