JACC. Clinical electrophysiology · 2025
Assessment of Intraprocedural Automated Arrhythmia Origin Localization System for Localizing Pacing Sites in 3D Space.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
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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
Аннотация
BACKGROUND: 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.
OBJECTIVES: This study sought to assess the accuracy of AAOL for localizing earliest ventricular activation in 3D space.
METHODS: 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, V2, and V6 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.
RESULTS: 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.
CONCLUSIONS: The AAOL approach accurately identifies early ventricular activation origins in 3D and on EAM surfaces, potentially useful for identifying intramural arrhythmia origins.
OBJECTIVES: This study sought to assess the accuracy of AAOL for localizing earliest ventricular activation in 3D space.
METHODS: 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, V2, and V6 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.
RESULTS: 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.
CONCLUSIONS: The AAOL approach accurately identifies early ventricular activation origins in 3D and on EAM surfaces, potentially useful for identifying intramural arrhythmia origins.