Application of computer models on atrial fibrillation research

• Published in: Minerva cardioangiologica Vol. 65; no. 4; p. 398
• Main Author: Masè, Michela
• Format: Journal Article
• Language: English
• Published: Italy 01-08-2017
• Subjects: Anti-Arrhythmia Agents - therapeutic use; Atrial Fibrillation - physiopathology; Atrial Fibrillation - therapy; Catheter Ablation - methods; Computer Simulation; Heart Atria - physiopathology; Humans; Models, Cardiovascular
• ISSN: 1827-1618
• DOI: 10.23736/S0026-4725.17.04363-8

Atrial fibrillation (AF) is a multifactorial and multiscale disease, where electrical, structural, anatomical and genetic factors contribute to the emergence of a complex and dynamic macroscopic phenotype. The incomplete understanding of AF mechanisms and the diversity and multiplicity of factors promoting the arrhythmia in humans have hampered the development of effective therapeutic approaches. Computer models and simulation of atrial arrhythmias may represent a unique ally to investigate AF mechanisms and direct therapeutic strategies, being able to merge and provide interpretation for multiscale data from cellular to entire organ scale levels. This review presents a broad overview of the principal modeling approaches applied in AF research to model atrial activity and ventricular response. The description of methodological aspects is followed by representative contributions of modeling to the dissection of AF mechanisms at both the atrial and atrioventricular level. A specific focus is given to controversial themes in AF research, such as calcium dynamics, fibrosis, multiple wavelets versus rotors propagation patterns, and AF heritability. Following modeling mechanistic insights, the review showcases modeling contributions in the domain of AF management and therapy, including the development of antiarrhythmic agents for rate and rhythm control, the optimization of ablation strategy, and the validation of mapping techniques and signal processing tools for the investigation of AF. A summary of current challenges and future developments necessary to improve the model capability at different scales and to transfer modeling results into clinical practice is finally presented.