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

根据目前的指南，房颤（AF）分为五种类型：(1)首次发现，(2)阵发性，(3)持续性，(4)长期持续性和(5)永久性。这是一种基于症状的临床分类。然而，最近的研究表明，类别之间的转换定义不清，与房颤的实际负担以及潜在房颤疾病进程的相关性不足。然而，这种分类在临床实践中被使用，例如用于个体治疗计划（节奏与速率控制）。由于介入治疗或药物治疗的成功率取决于个体疾病进展阶段，因此需要更好地与电生理重构的严重程度相关联的分类，以改善目前令人不满意的房颤治疗成功率。在之前的授权（SE 1758/3-1, SCHO 1350/2-1）的背景下，已经开发了组织特性和心律失常机制的电生理表征技术。一方面，计算模型已经被调整为利用实验数据在计算机上再现基因突变。由此，确定了导致房颤的病理机制。另一方面，我们发现不同抗心律失常药物的作用方式和疗效在突变模型和对照模型之间存在显著差异。在本项目中，所开发的技术将被应用和扩展，以对AF的异质组织基质进行个性化表征。为此，通过使用一种新的方法对有效不应期（在患者中）进行有创检测，以及信号幅度（电压）和传导速度的心率依赖（恢复）进行扩展。个体和患者组特异性模型将从最多70例持续性房颤患者的心房数据中生成。然后通过对不同组的模拟，在模型中确定心律失常电位（临床前心律失常标志物）。识别数据中的聚类将有助于定义一种新的房颤分类，更好地反映个体疾病进展。然后对不同阶段的房颤进行最佳的医学和介入治疗。大多数有希望的结果将在第一次实验研究中进行评估。通过将分期与每位患者的其他非侵入性测量参数（例如心电图p波形状和超声测量）相关联，从长远来看，改进的非侵入性个体化治疗建议应该是可能的。因此，房颤复律的成功率将会增加，同时治疗理念的获益与风险之间的关系也会更有利。

According to current guidelines, five types of atrial fibrillation (AF) are distinguished: (1) first detected, (2) paroxysmal, (3) persistent, (4) long-standing persistent and (5) permanent. This is a symptom-based clinical classification. Recent studies indicate, however, that the transition between the classes is ill-defined and insufficiently correlated with the actual burden of AF and, thus, the progress of the underlying AF disease process. Nevertheless, this classification is used in clinical practice, such as for individual therapy planning (rhythm vs. rate control). Since the success rates of interventional or medical therapy are depending on the individual disease progression stage, a classification is required which better correlates with the severity of electrophysiological remodeling to improve the currently unsatisfactory success rates of AF treatment.In the context of the previous grant (SE 1758/3-1, SCHO 1350/2-1), techniques for an electrophysiological characterization of tissue properties and arrhythmia mechanisms have been developed. On the one hand, computational models have been adjusted to reproduce genetic mutations in silico using experimental data. By this, patho-mechanisms leading to AF have been identified. On the other hand, we showed that the mode of action and the efficacy of different antiarrhythmic agents differ significantly between mutation and control models.In this project, the developed techniques will be applied and extended to perform an individualized characterization of the heterogeneous tissue substrate of AF. For this purpose, the models are expanded by using a new method for invasive detection (in patients) of the effective refractory period as well as heart-rate dependence (restitution) of signal amplitude (voltage) and conduction velocity. Both individual and patient group-specific models will be generated from the atrial data measured in up to 70 patients with mostly persistant AF. The arrhythmic potential (preclinical arrhythmia markers) will then be determined numerically in the models by simulations for the different groups. Identification of clusters in the data will serve for defining a novel AF classification that better reflects individual disease progression. Optimal medical and interventional therapy for AF will then be numerically calculated for the various stages. Most promising results will be evaluated in first experimental studies. By correlating the stages with additional, non-invasively measured parameters on each patient (e.g. ECG P-wave shape and ultrasound measures), an improved non-invasive, individual therapy recommendation should be possible in the long run. Thereby, success rates of AF cardioversion will be increased, combined with a more favorable relation between the benefits and the risks of therapy concepts.