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）首次检测到，（2）阵发性，（3）持续性，（4）长期持续性和（5）永久性。这是一种基于神经网络的临床分类。然而，最近的研究表明，类别之间的转换定义不清，与AF的实际负担以及潜在AF疾病进程的进展相关性不足。然而，这种分类在临床实践中使用，例如用于个体治疗计划（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的分类，以改善目前不令人满意的AF治疗成功率。（SE 1758/3-1，SCHO 1350/2-1），已经开发了用于组织特性和心律失常机制的电生理学表征的技术。一方面，计算模型已被调整，以使用实验数据在计算机上再现基因突变。由此，已经确定了导致AF的病理机制。另一方面，我们发现不同抗肿瘤药物的作用方式和疗效在突变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，以进行AF异质组织基质的个体化表征。为此，通过使用用于有效不应期以及信号幅度（电压）和传导速度的心率依赖性（恢复）的侵入性检测（在患者中）的新方法来扩展模型。个体和患者组特异性模型将从最多70例持续性AF患者的心房测量数据中生成。然后将通过模拟不同组在模型中以数值方式确定心律失常电位（临床前心律失常标志物）。识别数据中的聚类将用于定义更好地反映个体疾病进展的新型AF分类。然后将对各个阶段的AF最佳药物和介入治疗进行数值计算。最有希望的结果将在第一次实验研究中进行评估。通过将这些阶段与每个患者的额外非侵入性测量参数（例如ECG 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治疗建议应该是可能的。因此，AF心脏复律的成功率将增加，同时治疗概念的受益和风险之间的关系更加有利。

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.