Department of Electrical Engineering and Information Technology, Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Fritz-Haber, Weg 1 30.33, 76131, Karlsruhe, Germany.
Department of Computer Science, University of Oxford, 7 Parks Rd, OX13QG, Oxford, England, UK.
Europace. 2024 Oct 3;26(10). doi: 10.1093/europace/euae215.
The effective refractory period (ERP) is one of the main electrophysiological properties governing arrhythmia, yet ERP personalization is rarely performed when creating patient-specific computer models of the atria to inform clinical decision-making. This study evaluates the impact of integrating clinical ERP measurements into personalized in silico models on arrhythmia vulnerability.
Clinical ERP measurements were obtained in seven patients from multiple locations in the atria. Atrial geometries from the electroanatomical mapping system were used to generate personalized anatomical atrial models. The Courtemanche M. et al. cellular model was adjusted to reproduce patient-specific ERP. Four modeling approaches were compared: homogeneous (A), heterogeneous (B), regional (C), and continuous (D) ERP distributions. Non-personalized approaches (A and B) were based on literature data, while personalized approaches (C and D) were based on patient measurements. Modeling effects were assessed on arrhythmia vulnerability and tachycardia cycle length, with sensitivity analysis on ERP measurement uncertainty. Mean vulnerability was 3.4 ± 4.0%, 7.7 ± 3.4%, 9.0 ± 5.1%, and 7.0 ± 3.6% for scenarios A-D, respectively. Mean tachycardia cycle length was 167.1 ± 12.6 ms, 158.4 ± 27.5 ms, 265.2 ± 39.9 ms, and 285.9 ± 77.3 ms for scenarios A-D, respectively. Incorporating perturbations to the measured ERP in the range of 2, 5, 10, 20, and 50 ms changed the vulnerability of the model to 5.8 ± 2.7%, 6.1 ± 3.5%, 6.9 ± 3.7%, 5.2 ± 3.5%, and 9.7 ± 10.0%, respectively.
Increased ERP dispersion had a greater effect on re-entry dynamics than on vulnerability. Inducibility was higher in personalized scenarios compared with scenarios with uniformly reduced ERP; however, this effect was reversed when incorporating fibrosis informed by low-voltage areas. Effective refractory period measurement uncertainty up to 20 ms slightly influenced vulnerability. Electrophysiological personalization of atrial in silico models appears essential and requires confirmation in larger cohorts.
有效不应期（ERP）是控制心律失常的主要电生理特性之一，但在创建告知临床决策的心房特定于患者的计算机模型时，很少进行 ERP 个性化。本研究评估了将临床 ERP 测量值整合到个性化的计算机模型中对心律失常易感性的影响。
从 7 名患者的多个心房部位获得临床 ERP 测量值。使用来自电解剖映射系统的心房几何形状生成个性化解剖心房模型。调整 Courtemanche M. 等人的细胞模型以再现患者特异性 ERP。比较了四种建模方法：均质（A）、异质（B）、区域性（C）和连续（D）ERP 分布。非个性化方法（A 和 B）基于文献数据，而个性化方法（C 和 D）基于患者测量值。使用敏感性分析对 ERP 测量不确定性进行评估，评估心律失常易感性和心动过速周期长度的建模效果。场景 A-D 的平均易感性分别为 3.4±4.0%、7.7±3.4%、9.0±5.1%和 7.0±3.6%。平均心动过速周期长度分别为 167.1±12.6 ms、158.4±27.5 ms、265.2±39.9 ms 和 285.9±77.3 ms 场景 A-D。将测量的 ERP 中的干扰值在 2、5、10、20 和 50 ms 的范围内变化，模型的易感性分别变为 5.8±2.7%、6.1±3.5%、6.9±3.7%、5.2±3.5%和 9.7±10.0%。
ERP 离散度的增加对折返动力学的影响大于对易感性的影响。与具有均匀降低 ERP 的情况相比，个性化场景中的可诱导性更高；然而，当将由低电压区域提供的纤维化信息纳入考虑时，这种效果会逆转。ERP 测量不确定性高达 20 ms 对易感性的影响很小。心房计算机模型的电生理个性化似乎是必不可少的，需要在更大的队列中进行确认。
