Research Article: Validation of a novel automated signal analysis tool for ablation of Wolff-Parkinson-White Syndrome

Date Published: June 26, 2019

Publisher: Public Library of Science

Author(s): Scott R. Ceresnak, Robert H. Pass, Anne M. Dubin, Lingyao Yang, Kara S. Motonaga, Haley Hedlin, Kishor Avasarala, Anthony Trela, Doff B. McElhinney, Christopher Janson, Lynn Nappo, Xuefeng B. Ling, Gregory J. Gates, Antonio Palazón-Bru.

http://doi.org/10.1371/journal.pone.0217282

Abstract

In 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.

We sought to validate and refine this signal analysis tool in a larger multi-center cohort of children with WPW.

A 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. 347 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%. An 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.

Partial Text

Wolff-Parkinson-White syndrome (WPW) affects 0.1 to 0.3% of all individuals and has become one of the most common indications for invasive electrophysiology study (EPS) and ablation in children [1–5]. Though invasive EPS and ablation has become standard of care for curative treatment of WPW in children, there still remains an ablation failure rate of 5–10%.[6–9] Acute failure of ablation is usually secondary to one of several factors: 1) inability to technically deliver energy to the anatomic substrate, 2) anatomic location of the accessory pathway that makes ablation problematic or perilous, or 3) an inability to identify the precise location of accessory pathway tissue.[10,11] While there have been significant advances in the technology used to deliver ablation lesions effectively in anatomically challenging locations, there has been little advancement in the ability to identify the exact location of the accessory pathway.

The study was a two-center retrospective review of patients with WPW undergoing invasive EPS and ablation at Lucile Packard Children’s Hospital—Stanford University and The Children’s Hospital at Montefiore–Albert Einstein College of Medicine. Institutional Review Board (IRB) approval was obtained at each institution for this investigation (Stanford University Research Compliance Office and IRB and the Children’s Hospital at Montefiore IRB), all data were fully anonymized and the IRB waived the requirement for informed consent for this retrospective study. Patients ≤ 21 years of age undergoing invasive EPS with attempted ablation between 2008 and 2015 were included. Patients with atrio-fasiciaular, nodo-ventricular, fasiculo-ventricular connections, or those patients with WPW requiring ventricular pacing for ablation were excluded. Signals were also excluded if there was poor power delivery (< 20 Watts), poor temperature delivery (< 40 degrees Centigrade), or lesion time less than 5 seconds, as we could not be certain that the etiology of the unsuccessful application was inadequate lesion formation versus signal quality. Data collected for this investigation included patient-specific demographic information, EPS study data, and the raw signal data from the EP laboratory during ablation. Developments in automation have been able to leverage the power of computer processing systems to augment the abilities of the bedside physician in a broad array of medical specialties. Technological growth has dramatically enhanced the practice of electrophysiology and there have been numerous attempts to improve the EP study and ablation process over the past 20 years. The development of RF and cryoablation, the use of automated computer based EP recording systems, and the development of 3-dimensional mapping systems to map and visualize the ablation substrate are examples that have enhanced the technical capabilities of the electrophysiologist.[13–17] While this tremendous progress has revolutionized the ablation process, there has been only minimal work attempting to harness the power of computational systems in signal analytics. In this study, we demonstrate that automated signal analysis tools can provide reasonable diagnosis accuracy for distinguishing the site of a successful ablation in children with WPW. This work demonstrates that the armamentarium of tools available for ablation of WPW may expand to the realm of automated signal analysis and automation of signal analytics may provide an additional tool in the EP laboratory. An automated signal analysis software tool reliably distinguished electrograms at sites that resulted in acutely successful ablation of WPW from unsuccessful ones. This software was effective across two centers, multiple operators, and different ablation catheters. This signal analysis software may be an effective tool for aid in the ablation of WPW in children.   Source: http://doi.org/10.1371/journal.pone.0217282

 

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