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，类别之间的转换是模糊的，并且与房颤的实际负担以及潜在的房颤疾病过程的进展缺乏足够的相关性。然而，这种分类在临床实践中被使用，例如用于个体化治疗计划(节律与心率控制)。由于介入或药物治疗的成功率取决于单个疾病的进展阶段，因此需要更好地与电生理重塑的严重程度相关的分类，以提高目前不满意的房颤治疗成功率。在先前的授权(SE 1758/3-1，Scho 1350/2-1)的背景下，已经开发了用于组织特性和心律失常机制的电生理表征的技术。一方面，计算模型已经被调整，以使用实验数据重现硅胶中的基因突变。由此，对房颤的发病机制进行了初步探讨。另一方面，我们发现不同的抗心律失常药物在突变模型和对照模型中的作用模式和疗效有显著差异。在这个项目中，开发的技术将被应用和扩展来对房颤的不同组织底物进行个性化的表征。为此，通过使用一种新的侵入性检测(在患者中)有效不应期以及信号幅度(电压)和传导速度的心率依赖(恢复)的方法来扩展模型。个人和患者组特定的模型将从多达70名大多为持续性房颤的患者的心房数据中产生。然后，将通过对不同组的模拟在模型中以数值方式确定心律失常潜力(临床前心律失常标记物)。数据中簇的识别将用于定义更好地反映个体疾病进展的新的房颤分类。然后将对不同阶段的房颤进行最优的内科和介入治疗。最有希望的结果将在第一次实验研究中进行评估。通过将每个患者的分期与额外的非侵入性测量参数(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增加，治疗理念的益处和风险之间的关系将更加有利。

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.