Zhou Shijie, Whitaker John, Goldberg Stanislav, AbdelWahab Amir, Sauer William H, Chrispin Jonathan, Berger Ronald D, Tandri Harikrishna, Trayanova Natalia A, Tedrow Usha B, Sapp John L
Department of Chemical, Paper, and Biomedical Engineering, Miami University, Oxford, Ohio, USA; Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts, USA.
Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
JACC Clin Electrophysiol. 2025 May;11(5):907-918. doi: 10.1016/j.jacep.2024.12.003. Epub 2025 Jan 29.
The Automated Arrhythmia Origin Localization (AAOL) algorithm was developed for real-time prediction of early ventricular activation origins on a patient-specific electroanatomic (EAM) surface using a 3-lead electrocardiogram (AAOL-Surface). It has not been evaluated in 3-dimensional (3D) space (AAOL-3D), however, which may be important for predicting the arrhythmia origin from intramural or intracavity sites.
This study sought to assess the accuracy of AAOL for localizing earliest ventricular activation in 3D space.
This was a retrospective study of 3 datasets (BWH [Brigham and Women's Hospital], JHH [Johns Hopkins Hospital], and QEII [Queen Elizabeth II Health Sciences Centre]) involving 47 patients and 48 procedures, with an average of 19 ± 10 pacing sites each. In each patient, individual pacing sites were identified as target sites; the remaining pacing sites served as a training set (including QRS integrals from leads III, V, and V with associated 3D coordinates). The AAOL-3D was then used to predict 3D coordinates of the pacing site. Localization error was assessed as the distance between known and predicted site coordinates, considering different EAM resolutions.
The AAOL-3D achieved a localization accuracy of 7.2 ± 3.1 mm, outperforming the AAOL-Surface (7.2 vs 7.8 mm; P < 0.05), with greater localization error for epicardial than endocardial pacing sites (8.7 vs 7.1 mm; P < 0.05). Cohort-specific analysis consistently favored AAOL-3D over AAOL-Surface in terms of accuracy. Exploration of AAOL-Surface accuracy across varying EAM resolutions showed optimal performance at the original and 75% resolution, with performance declining as resolution decreased.
The AAOL approach accurately identifies early ventricular activation origins in 3D and on EAM surfaces, potentially useful for identifying intramural arrhythmia origins.
自动心律失常起源定位（AAOL）算法旨在利用三导联心电图在患者特异性电解剖（EAM）表面实时预测早期心室激动起源（AAOL-表面法）。然而，该算法尚未在三维（3D）空间中进行评估（AAOL-3D），而这对于预测壁内或心腔内部位的心律失常起源可能很重要。
本研究旨在评估AAOL在三维空间中定位最早心室激动的准确性。
这是一项对3个数据集（布莱根妇女医院[BWH]、约翰霍普金斯医院[JHH]和伊丽莎白二世健康科学中心[QEII]）进行的回顾性研究，涉及47例患者和48次手术，平均每个患者有19±10个起搏部位。在每位患者中，将各个起搏部位确定为目标部位；其余起搏部位作为训练集（包括导联III、V和V的QRS积分以及相关的三维坐标）。然后使用AAOL-3D预测起搏部位的三维坐标。考虑到不同的EAM分辨率，将定位误差评估为已知和预测部位坐标之间的距离。
AAOL-3D实现了7.2±3.1毫米的定位精度，优于AAOL-表面法（7.2对7.8毫米；P<0.05），心外膜起搏部位的定位误差大于心内膜起搏部位（8.7对7.1毫米；P<0.05）。特定队列分析在准确性方面始终支持AAOL-3D优于AAOL-表面法。对不同EAM分辨率下AAOL-表面法准确性的探索表明，在原始分辨率和75%分辨率下性能最佳，随着分辨率降低性能下降。
AAOL方法能够准确识别三维空间和EAM表面的早期心室激动起源，可能有助于识别壁内心律失常起源。
