Can we improve the accuracy of electrocardiographic algorithms for accessory pathway location in children?

Abstract

Background: Predicting an accessory pathway location is extremely important in pediatric patients. Aims:We designed a study to compare previously published algorithms by Arruda, Boersma, and Chiang.Methods: This multicenter study included patients who had undergone successful ablation of one accessory pathway. Analysis of resting 12-lead electrocardiograms was carried out. An aggregated prediction score was constructed on the basis of algorithm agreement, and a structured workflow approach was proposed. Results:The total population was 120 patients (mean age, 12.7 [± 3.6] years). The algorithm by Boersma had the highest accuracy (71.7%). The inter-rater agreement among the 3 reference algorithms, according to left-sided accessory pathway (AP) identification, was good between Boersma and Chiang (κ = 0.611; 95% confidence interval [CI], 0.468-0.753) but moderate between Arruda and Chiang and between Arruda and Boersma (κ = 0.566; 95% CI, 0.419-0.713 and κ = 0.582; 95% CI, 0.438-0.727, respectively). Regarding locations at risk of atrioventricular (AV) block, agreement was fair between Arruda and Chiang and between Boersma and Chiang (κ = 0.358; 95% CI, 95% CI, respectively) but moderate between Arruda and Boersma (κ = 0.45; 95% CI, 0.304-0.597). On applying a first-step diagnostic evaluation, when concordance was achieved, we were able to correctly identify left-sided or non-left-sided ablation sites in 96.4% (n = 80) of cases. When concordance was achieved, correct prediction of risk/no risk of AV block was achieved in 92.2% (n = 59) of cases.Conclusions: An aggregated prediction score based on 3 reference algorithms proved able to predict an accessory pathway location very precisely and could be used to plan safely invasive procedures.

Accessory pathway localization with probabilistic density maps generated by a mobile application: Assessment of a full pre‐excitation net‐vector method

“…These steps were developed on the basis of the classic rules of ECG interpretation and the personal experience of over 1000 APs ablated by the first author. Most of these criteria, in various modifications, can be found in the literature, [12][13][14][15][16][17][18][19] the only authorial criterion is QRS notch/slur/plateau (Figure 2) to differentiate right free wall pathways from septal pathways. The steps for ECG analysis were optimized by post-hoc assessment-if a step did not result in an expected differentiating change of AP distribution pattern-it was abandoned and other were explored.…”

Accessory pathway location algorithms: Planning invasive procedures in the electrophysiology laboratory

“…Ferrari et al [7] allow us to draw conclusions that are aligned with our recently published approach [6]. Our Simplified Topographic Algorithm (STA) aimed to provide a perspective on ECG location in the context of today's improved procedural tools to assist with EPS and ablations.…”

“…In a multicenter retrospective cohort from Italy, Ferrari et al [7] proposed a novel approach to AP location in children, aiming to assess and compare the accuracy of the algorithms proposed by Arruda [8], Boersma [9], and Chiang [10]. The authors enrolled 120 patients younger than 18 years of age who underwent a successful ablation of one AP between January 2013 and June 2019.…”

“…for cryoablation)? Similar to the approach discussed in the work of Ferrari et al [7], the STA considers 3 anatomical regions for potential AP location: the left lateral wall, the posteroseptal wall, and the right anterior to the free wall. For the implementation of this simplified approach, six leads (V1, I, aVL, II, III, and aVF) are used in a stepwise manner:…”

“…AP location through ECG algorithms remains of significant interest to clinical and interventional cardiologists to better characterize procedural risks and enhance the success of the ablation. The approach designed by Ferrari et al [7] is a major contribution to planning ablation procedures in children. A detailed analysis of these data and integration of this algorithm into our clinical practice may help to reduce time in the electrophysiology laboratory, minimize complications, and provide patients and families with appropriate outcomes.…”