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Characterization of the electrophysiological substrate in patients with atrial fibrillation - Role of the restitution of atrial conduction velocity and of the voltage for the development of atrial fibrillation

Subject Area Medical Physics, Biomedical Technology
Cardiology, Angiology
Term from 2011 to 2022
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 183027722
 
Final Report Year 2023

Final Report Abstract

According to current guidelines, the classification of atrial fibrillation (AF) is (1) first diagnosed, (2) paroxysmal, (3) persistent, (4) long-standing persistent, and (5) permanent. This is a symptom-based clinical classification. Recent studies suggest that this classification does inadequately define transitions and does not correlate with the actual AF burden - and thus with the actual progression of the disease process. Nevertheless, this classification dominates clinical practice and plays an essential role in individual therapy planning (rhythm vs. rate control). However, since the success rates of interventional or drug therapy depend on the individual disease stage, a classification correlating with the severity of electrophysiological remodeling would be important to improve the currently limited AF therapy success rates. In this project, an individualized characterization of the heterogeneous tissue substrate in AF was performed. For this purpose, invasively acquired data on the effective refractory period (ERP) of the atrial tissue as well as the heart rate dependence (restitution) of propagation velocity and signal amplitude (voltage) were assimilated using novel methods that allow for optimal estimation of parameters that cannot be measured directly by using mechanistic computer models. Atrial measurement data and routine clinical parameters were acquired in 42 AF patients and processed using newly developed tailored open source software. Atrial propagation velocity as well as its anisotropy could be determined more precisely and locally than before using newly developed simulation-based estimation methods. Cluster analysis of patient-specific ERP and restitution of signal amplitude and propagation velocity did not yield statistically robust classification of patients beyond established AF stages. By combining signal processing and computer modeling/simulation, we were for example able to identify electrogram characteristics of arrhythmogenic sources in the atrium, quantify local fibrosis content and local tissue ERP, provide an in silico test bed for ablation strategies, predict the likelihood of success of pulmonary vein isolation and the risk for AF-associated heart failure completely noninvasively by machine learning on the 12-lead ECG, and mechanistically underpin the pharmacological mode of action of novel TASK-1 antiarrhythmic agents. The developed methods, data, and findings were published in 16 peer-reviewed publications and 12 additional research outputs.

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