Vila Muhamed, Rivolta Massimo Walter, Luongo Giorgio, Unger Laura Anna, Luik Armin, Gigli Lorenzo, Lombardi Federico, Loewe Axel, Sassi Roberto
Dipartimento di Informatica, Università degli Studi di Milano, Milan, Italy.
Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany.
Front Physiol. 2021 Oct 26;12:749635. doi: 10.3389/fphys.2021.749635. eCollection 2021.
Atrial flutter (AFL) is a common atrial arrhythmia typically characterized by electrical activity propagating around specific anatomical regions. It is usually treated with catheter ablation. However, the identification of rotational activities is not straightforward, and requires an intense effort during the first phase of the electrophysiological (EP) study, i.e., the mapping phase, in which an anatomical 3D model is built and electrograms (EGMs) are recorded. In this study, we modeled the electrical propagation pattern of AFL (measured during mapping) using network theory (NT), a well-known field of research from the computer science domain. The main advantage of NT is the large number of available algorithms that can efficiently analyze the network. Using directed network mapping, we employed a cycle-finding algorithm to detect all cycles in the network, resembling the main propagation pattern of AFL. The method was tested on two subjects in sinus rhythm, six in an experimental model of simulations, and 10 subjects diagnosed with AFL who underwent a catheter ablation. The algorithm correctly detected the electrical propagation of both sinus rhythm cases and simulations. Regarding the AFL cases, arrhythmia mechanisms were either totally or partially identified in most of the cases (8 out of 10), i.e., cycles around the mitral valve, tricuspid valve and figure-of-eight reentries. The other two cases presented a poor mapping quality or a major complexity related to previous ablations, large areas of fibrotic tissue, etc. Directed network mapping represents an innovative tool that showed promising results in identifying AFL mechanisms in an automatic fashion. Further investigations are needed to assess the reliability of the method in different clinical scenarios.
心房扑动（AFL）是一种常见的房性心律失常，其典型特征是电活动围绕特定解剖区域传播。通常采用导管消融治疗。然而，旋转活动的识别并非易事，在电生理（EP）研究的第一阶段，即标测阶段需要付出巨大努力，在此阶段构建解剖三维模型并记录心内电图（EGM）。在本研究中，我们使用网络理论（NT）对AFL的电传播模式（在标测期间测量）进行建模，NT是计算机科学领域一个著名的研究方向。NT的主要优势在于有大量可用算法能够高效分析网络。通过有向网络标测，我们采用一种寻环算法来检测网络中的所有环，这类似于AFL的主要传播模式。该方法在两名窦性心律受试者、六个模拟实验模型以及十名接受导管消融治疗且被诊断为AFL的受试者身上进行了测试。该算法正确检测出了窦性心律病例和模拟实验的电传播情况。对于AFL病例，在大多数病例（10例中的8例）中，心律失常机制要么被完全识别，要么被部分识别，即围绕二尖瓣、三尖瓣的环以及8字形折返。另外两例呈现出较差的标测质量或与既往消融、大面积纤维化组织等相关的主要复杂性。有向网络标测是一种创新工具，在以自动方式识别AFL机制方面显示出了有前景的结果。需要进一步研究以评估该方法在不同临床场景下的可靠性。
