Validation of a novel automated signal analysis tool for ablation of Wolff-Parkinson-White Syndrome.
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2019
2019
Historique:
received:
21
08
2018
accepted:
09
05
2019
entrez:
27
6
2019
pubmed:
27
6
2019
medline:
26
2
2020
Statut:
epublish
Résumé
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.
Sections du résumé
BACKGROUND
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.
OBJECTIVE
We sought to validate and refine this signal analysis tool in a larger multi-center cohort of children with WPW.
METHODS
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.
RESULTS
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%.
CONCLUSIONS
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.
Identifiants
pubmed: 31242221
doi: 10.1371/journal.pone.0217282
pii: PONE-D-18-24689
pmc: PMC6594583
doi:
Types de publication
Journal Article
Multicenter Study
Research Support, Non-U.S. Gov't
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0217282Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
Références
Heart Rhythm. 2004 Jul;1(2):188-96
pubmed: 15851152
Circulation. 2014 Sep 2;130(10):811-9
pubmed: 25052405
J Cardiovasc Electrophysiol. 2015 Apr;26(4):412-416
pubmed: 25600208
J Cardiovasc Electrophysiol. 2002 Apr;13(4):336-41
pubmed: 12033349
J Cardiovasc Electrophysiol. 1997 Nov;8(11):1320-6
pubmed: 9395176
Pediatrics. 2003 Mar;111(3):e245-7
pubmed: 12612279
J Am Coll Cardiol. 1996 Mar 1;27(3):683-9
pubmed: 8606282
Pacing Clin Electrophysiol. 2011 Oct;34(10):1288-396
pubmed: 21851369
Pacing Clin Electrophysiol. 2014 Sep;37(9):1095-9
pubmed: 25039285
N Engl J Med. 2003 Nov 6;349(19):1803-11
pubmed: 14602878
Heart Rhythm. 2004 Jul;1(2):129-38
pubmed: 15851143
J Am Heart Assoc. 2015 Sep 21;4(9):e002476
pubmed: 26391136
Pacing Clin Electrophysiol. 2008 Mar;31(3):283-9
pubmed: 18307622
Heart Rhythm. 2012 Jun;9(6):1006-24
pubmed: 22579340
J Interv Card Electrophysiol. 2014 Jun;40(1):93-8
pubmed: 24752794
J Cardiovasc Electrophysiol. 2004 Jul;15(7):759-70
pubmed: 15250858
Heart Rhythm. 2012 Jan;9(1):2-7
pubmed: 21872561
Europace. 2010 Jun;12(6):835-41
pubmed: 20223787
Am J Cardiol. 1997 Dec 1;80(11):1438-43
pubmed: 9399718
Circulation. 1993 Mar;87(3):866-73
pubmed: 8443907