Dataset Information

Open-window mapping and the extended early-meets-late algorithm for the Wolff-Parkinson-White syndrome.

ABSTRACT: This is the first report describing open-window mapping with the extended early-meets-late algorithm to enhance visualization of the location and width of an atrioventricular accessory pathway. This may be particularly useful when linear or cluster ablation lesion sets are required for wide pathways.

SUBMITTER: Wang NC

PROVIDER: S-EPMC9347199 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Publications

Open-window mapping and the extended early-meets-late algorithm for the Wolff-Parkinson-White syndrome.

Wang Norman C NC

Journal of arrhythmia 20220509 4

This is the first report describing open-window mapping with the extended early-meets-late algorithm to enhance visualization of the location and width of an atrioventricular accessory pathway. This may be particularly useful when linear or cluster ablation lesion sets are required for wide pathways. ...[more]

PMID: 35936047

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Project description:While Drs Wolff, Parkinson, and White fully described the syndrome in 1930, prior case reports had described the essentials. Over the ensuing century this syndrome has captivated the interest of anatomists, clinical cardiologists, and cardiac surgeons. Stanley Kent described lateral muscular connections over the atrioventricular (AV) groove which he felt were the normal AV connections. The normal AV connections were, however, clearly described by His and Tawara. True right-sided AV connections were initially described by Wood et al., while Öhnell first described left free wall pathways. David Scherf is thought to be the first to describe our current understanding of the pathogenesis of the WPW syndrome in terms of a re-entrant circuit involving both the AV node-His axis as well as the accessory pathway. This hypothesis was not universally accepted, and many theories were applied to explain the clinical findings. The basics of our understanding were established by the brilliant work of Pick, Langendorf, and Katz who by using careful deductive analysis of ECGs were able to define the basic pathophysiological processes. Subsequently, Wellens and Durrer applied invasive electrical stimulation to the heart in order to confirm the pathophysiological processes. Sealy and his colleagues at Duke University Medical Center were the first to successfully surgically divide an accessory pathway and ushered in the modern era of therapy for these patients. Morady and Scheinman were the first to successfully ablate an accessory pathway (posteroseptal) using high-energy direct-current shocks. Subsequently Jackman, Kuck, Morady, and a number of groups proved the remarkable safety and efficiency of catheter ablation for pathways in all locations using radiofrequency energy. More recently, Gollob et al. first described the gene responsible for a familial form of WPW. The current ability to cure patients with WPW is due to the splendid contributions of individuals from diverse disciplines throughout the world.

Project description:BackgroundIn previous pilot work we demonstrated that a novel automated signal analysis tool could accurately identify successful ablation sites during Wolff-Parkinson-White (WPW) ablation at a single center.ObjectiveWe sought to validate and refine this signal analysis tool in a larger multi-center cohort of children with WPW.MethodsA retrospective review was performed of signal data from children with WPW who underwent ablation at two pediatric arrhythmia centers from 2008-2015. All patients with WPW ≤ 21 years who underwent invasive electrophysiology study and ablation with ablation signals available for review were included. Signals were excluded if temperature or power delivery was inadequate or lesion time was < 5 seconds. Ablation lesions were reviewed for each patient. Signals were classified as successful if there was loss of antegrade and retrograde accessory pathway (AP) conduction or unsuccessful if ablation did not eliminate AP conduction. Custom signal analysis software analyzed intracardiac electrograms for amplitudes, high and low frequency components, integrated area, and signal timing components to create a signal score. We validated the previously published signal score threshold 3.1 in this larger, more diverse cohort and explored additional scoring options. Logistic regression with lasso regularization using Youden's index criterion and a cost-benefit criterion to identify thresholds was considered as a refinement to this score.Results347 signals (141 successful, 206 unsuccessful) in 144 pts were analyzed [mean age 13.2 ± 3.9 years, 96 (67%) male, 66 (45%) left sided APs]. The software correctly identified the signals as successful or unsuccessful in 276/347 (80%) at a threshold of 3.1. The performance of other thresholds did not significantly improve the predictive ability. A signal score threshold of 3.1 provided the following diagnostic accuracy for distinguishing a successful from unsuccessful signal: sensitivity 83%, specificity 77%, PPV 71%, NPV 87%.ConclusionsAn automated signal analysis software tool reliably distinguished successful versus unsuccessful ablation electrograms in children with WPW when validated in a large, diverse cohort. Refining the tools using an alternative threshold and statistical method did not improve the original signal score at a threshold of 3.1. This software was effective across two centers and multiple operators and may be an effective tool for ablation of WPW.

Dataset Information

Open-window mapping and the extended early-meets-late algorithm for the Wolff-Parkinson-White syndrome.

Publications

Open-window mapping and the extended early-meets-late algorithm for the Wolff-Parkinson-White syndrome.

Similar Datasets

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