Atrial fibrillation (AF) is the most common heart rhythm disturbance and is associated with substantial morbidity and mortality. The overall prevalence of AF varies from 2% to 5% of the general population worldwide, and it is projected to more than double in the following couple of decades, becoming a global epidemic.
However, the current clinical treatment of AF is suboptimal. There are three types of clinical treatment for patients with AF: 1) pharmacological approaches for rate and rhythm control; 2) electrical cardioversion; and 3) cryoablation/catheter ablation/surgical (maze) ablation. Recent population-based studies suggest that all three treatment options often lose their effectiveness and have side effects, especially for patients with persistent or permanent AF or AF patients with concurrent diseases. The high-profile clinical trials, including the Substrate and Trigger Ablation for Reduction of Atrial Fibrillation Part 2 (STAR-AF II), Focal Impulse and Rotor Modulation (FIRM) trial and subsequent studies using the FIRM approach by different international groups, have generated mixed outcomes from ablation treatment using existing ablation strategies.
The main reasons for the poor performance of current clinical treatment for AF are due to 1) lack of basic understanding of the underlying patient-specific atrial substrate which sustains AF directly; 2) incomplete knowledge of potential risk factors of AF and nonexistence of effective upstream approaches for AF prevention; 3) need for quantitative tools to investigate effective strategies which are impossible under clinical/experimental settings.
Computational approaches are the driving force behind our advancing understanding of AF. For example, computer models of atrial electrical activation provide a powerful analysis framework primarily for three purposes, 1) to illustrate the basic electrical and structural mechanisms behind cardiac arrhythmias; 2) to test the dynamic impact of antiarrhythmic drugs from cell to organ level; and 3) to investigate optimal ablation lesions and ablation treatment strategies. Other typical computational approaches include the forward/inverse computing approach used in body surface mapping, phase singularity analysis adapted in the FIRM studies and more recent machine learning approach in facilitating automatic analysis of ECG data.
The aim of this research topic is to collect a series of original studies, review and meta-analysis research articles that would present most recent advances towards better understanding and treatment of AF in the development or use of:
1) computer models/simulations (such as patient specific modeling),
2) novel signal processing approaches (such as Fast Fourier Transform or phase mapping),
3) body surface mapping (such as forward/inverse approach),
4) machine learning (such as an application of deep neural network),
5) population-based statistical approach (such as meta-analysis),
6) computer software/languages (such as CellML, developed by the Auckland Bioengineering Institute at the University of Auckland, for storing and exchanging computer-based cardiac cellular mathematical models).
7) complexity analysis,
8) cardiac imaging and associated analysis.
Note that we do not restrict ourselves to the six potential approaches aforementioned, and we also welcome any other original/review/meta-analysis research articles using computational approaches to improve our understanding and treatment of patients with AF, especially for persistent or permanent AF.
Atrial fibrillation (AF) is the most common heart rhythm disturbance and is associated with substantial morbidity and mortality. The overall prevalence of AF varies from 2% to 5% of the general population worldwide, and it is projected to more than double in the following couple of decades, becoming a global epidemic.
However, the current clinical treatment of AF is suboptimal. There are three types of clinical treatment for patients with AF: 1) pharmacological approaches for rate and rhythm control; 2) electrical cardioversion; and 3) cryoablation/catheter ablation/surgical (maze) ablation. Recent population-based studies suggest that all three treatment options often lose their effectiveness and have side effects, especially for patients with persistent or permanent AF or AF patients with concurrent diseases. The high-profile clinical trials, including the Substrate and Trigger Ablation for Reduction of Atrial Fibrillation Part 2 (STAR-AF II), Focal Impulse and Rotor Modulation (FIRM) trial and subsequent studies using the FIRM approach by different international groups, have generated mixed outcomes from ablation treatment using existing ablation strategies.
The main reasons for the poor performance of current clinical treatment for AF are due to 1) lack of basic understanding of the underlying patient-specific atrial substrate which sustains AF directly; 2) incomplete knowledge of potential risk factors of AF and nonexistence of effective upstream approaches for AF prevention; 3) need for quantitative tools to investigate effective strategies which are impossible under clinical/experimental settings.
Computational approaches are the driving force behind our advancing understanding of AF. For example, computer models of atrial electrical activation provide a powerful analysis framework primarily for three purposes, 1) to illustrate the basic electrical and structural mechanisms behind cardiac arrhythmias; 2) to test the dynamic impact of antiarrhythmic drugs from cell to organ level; and 3) to investigate optimal ablation lesions and ablation treatment strategies. Other typical computational approaches include the forward/inverse computing approach used in body surface mapping, phase singularity analysis adapted in the FIRM studies and more recent machine learning approach in facilitating automatic analysis of ECG data.
The aim of this research topic is to collect a series of original studies, review and meta-analysis research articles that would present most recent advances towards better understanding and treatment of AF in the development or use of:
1) computer models/simulations (such as patient specific modeling),
2) novel signal processing approaches (such as Fast Fourier Transform or phase mapping),
3) body surface mapping (such as forward/inverse approach),
4) machine learning (such as an application of deep neural network),
5) population-based statistical approach (such as meta-analysis),
6) computer software/languages (such as CellML, developed by the Auckland Bioengineering Institute at the University of Auckland, for storing and exchanging computer-based cardiac cellular mathematical models).
7) complexity analysis,
8) cardiac imaging and associated analysis.
Note that we do not restrict ourselves to the six potential approaches aforementioned, and we also welcome any other original/review/meta-analysis research articles using computational approaches to improve our understanding and treatment of patients with AF, especially for persistent or permanent AF.
