Electrocardiographic differentiation of typical atrioventricular node reentrant tachycardia from atrioventricular reciprocating tachycardia mediated by concealed accessory pathway in children

doi:10.1016/S0002-9149(03)00153-X

The American Journal of Cardiology

Volume 91, Issue 9, 1 May 2003, Pages 1084-1089

https://doi.org/10.1016/S0002-9149(03)00153-X Get rights and content

Abstract

The value of the electrocardiogram (ECG) in children with supraventricular tachycardia (SVT) is unclear. The noninvasive differentiation of typical atrioventricular node reentrant tachycardia (AVNRT) and atrioventricular reciprocating tachycardia (AVRT) mediated by concealed accessory pathway conduction is clinically important, as it helps in counseling and potentially facilitates ablation procedures. One hundred forty-eight ECGs showing narrow QRS complex SVT were obtained from children before successful radiofrequency catheter ablation. An initial 102 ECGs were analyzed by 3 blinded observers to assess the utility of various electrocardiographic findings. No electrocardiographic criteria were found to discriminate between SVT mechanisms on 1- to 3-channel Holter/event recorder tracings (n = 32); their interpretation mainly (55%) resulted in an incorrect SVT diagnosis. On 12-lead ECGs (n = 70), the 2 arrhythmias were accurately diagnosed in 76% of patients; 5 findings were found to be discriminators of tachycardia mechanism. Predictors of AVRT were visible P waves in 74% of cases (sensitivity 92%; specificity 64%), RP intervals of ≥100 ms in 91% (sensitivity 84%; specificity 91%), and ST-segment depression of ≥2 mm in 73% of cases (sensitivity 52%; specificity 82%). Pseudo r′ waves in lead V₁ and pseudo S waves in the inferior leads during tachycardia predicted AVNRT in 100% of cases (sensitivity 55% and 20%, respectively; specificity 100% for both). Based on these results, we developed a new diagnostic 12-lead electrocardiographic algorithm for pseudo r′/S waves, RP duration, and ST-segment depression during tachycardia. Two observers tested the algorithm in 46 (21 AVNRT; 25 AVRT) additional cases; they correctly diagnosed the SVT mechanism in 91% and 87%, respectively. Thus, the stepwise use of diagnostically relevant 12-lead electrocardiographic parameters helps to more accurately differentiate mechanisms of reentrant SVT.

Section snippets

Electrocardiograph selection

Electrocardiographic recordings of otherwise healthy children with paroxysmal SVT (n = 148; mean age ± SD 10.6 ± 3.9 years) were evaluated. The arrhythmias occurred either spontaneously or were induced during an invasive electrophysiologic study. The 12-lead tracings were recorded at a paper speed of 25 or 50 mm/s with a gain setting of 10 mm/mV. The 2-channel Holter and 1- or 3-channel event were recorded at a paper speed of 25 mm/s. In all cases, the ECG was also recorded during sinus rhythm.

Part 1

Of the initial 102 ECGs, 54 (53%) were AVNRT and 48 (47%) were AVRT (Table 1). The 2 arrhythmias were documented at comparable patient ages (10.8 ± 3.1 vs 9.4 ± 4.8 years).

Inter- and intraobserver concordance

Table 2 illustrates the degree of agreement in interpreting electrocardiographic variables. There was only modest interobserver concordance on the suspected SVT mechanism after analysis of Holter/event electrocardiographic recordings and 12-lead ECGs.

Holter/event recorder electrocardiographic analyses (table 3)

The investigators were unable to correctly identify SVT mechanisms based on

Discussion

The present study demonstrates the utility of 12-lead electrocardiographic parameters for the differentiation of orthodromic AVRT from typical AVNRT in the pediatric population. Some findings have previously been reported to be of differential diagnostic value in adults.2, 3, 4, 5, 6, 7, 8, 9, 10 For example, ventricular preexcitation during sinus rhythm strongly suggests the diagnosis of orthodromic AVRT in any narrow QRS complex tachycardia.6 For the purpose of this study, we focused on the

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Investigation of clinical significance of ST-segment depression during paroxysmal supraventricular tachycardia
2024, Journal of Electrocardiology
There are great differences in ST-segment depression during PSVT episodes. The aim of this study is to investigate the clinical significance of ST segment depression during PSVT.
The study enrolled 333 consecutive patients who were diagnosed with PSVT by electrophysiological test from Jan 1, 2021 to July 31, 2022. The range, magnitude and morphology of ST-segment depression were described. The correlation between ST-segment depression and symptoms of chest tightness, chest pain or hypotension, the correlation between ST-segment depression and coronary stenosis, and the possible influencing factors were analyzed. In addition, the diagnostic efficacy of ST-segment depression for AVRT was determined.
ST-segment depression was present in 85% of patients, in 70% of which the depression range was more than six leads. The magnitude of the depression was more significant in precordial leads (P < 0.001). ST-segment depression of >1 mm in limb leads and precordial leads was found in 36.0% and 49.8% of the patients, respectively, while >3 mm was found in 2.4% and 9.6%, respectively. The morphology of ST-segment depression in limb leads was different from that in precordial leads (P < 0.001). Downsloping ST-segment depression was more common in limb leads (limb vs. precordial: 40.5% vs. 12.6%), whereas upsloping depression was more common in precordial leads (limb vs. precordial: 3.0% vs. 23.1%). Correlation analysis showed that ST-segment depression was not correlated with symptoms of chest tightness and pain, nor was it correlated with coronary artery stenosis. The most important influencing factor is the type of PSVT, especially affecting the morphology of depression in limb leads (OR = 10.27 [5.93–17.79], P < 0.001). The sensitivity and specificity of downsloping ST-segment depression in limb leads for diagnosis of AVRT were 75.5% and 76.7%.
ST-segment depression is a common ECG change during PSVT episodes, and it's not associated with severe coronary stenosis. The type of PSVT has a significant effect on the manifestation of ST-segment depression. The downslope morphology of ST-segment depression in limb leads is helpful in differentiating AVRT from AVNRT.
Identification of supraventricular tachycardia mechanisms with surface electrocardiograms using a convolutional neural network
2023, Heart Rhythm O2
It remains difficult to definitively distinguish supraventricular tachycardia (SVT) mechanisms using a 12-lead electrocardiogram (ECG) alone. Machine learning may identify visually imperceptible changes on 12-lead ECGs and may improve ability to determine SVT mechanisms.
We sought to develop a convolutional neural network (CNN) that identifies the SVT mechanism according to the gold standard of SVT ablation and to compare CNN performance against experienced electrophysiologists among patients with atrioventricular nodal re-entrant tachycardia (AVNRT), atrioventricular reciprocating tachycardia (AVRT), and atrial tachycardia (AT).
All patients with 12-lead surface ECG during sinus rhythm and SVT and had successful SVT ablation from 2013 to 2020 were included. A CNN was trained using data from 1505 surface ECGs that were split into 1287 training and 218 test ECG datasets. We compared the CNN performance against independent adjudication by 2 experienced cardiac electrophysiologists on the test dataset.
Our dataset comprised 1505 ECGs (368 AVNRT, 304 AVRT, 95 AT, and 738 sinus rhythm) from 725 patients. The CNN areas under the receiver-operating characteristic curve for AVNRT, AVRT, and AT were 0.909, 0.867, and 0.817, respectively. When fixing the specificity of the CNN to the electrophysiologist adjudicators’ specificity, the CNN identified all SVT classes with higher sensitivity: (1) AVNRT (91.7% vs 65.9%), (2) AVRT (78.4% vs 63.6%), and (3) AT (61.5% vs 50.0%).
A CNN can be trained to differentiate SVT mechanisms from surface 12-lead ECGs with high overall performance, achieving similar performance to experienced electrophysiologists at fixed specificities.
Artificial intelligence–enabled electrocardiogram to distinguish atrioventricular re-entrant tachycardia from atrioventricular nodal re-entrant tachycardia
2023, Cardiovascular Digital Health Journal
Accurately determining arrhythmia mechanism from a 12-lead electrocardiogram (ECG) of supraventricular tachycardia can be challenging. We hypothesized a convolutional neural network (CNN) can be trained to classify atrioventricular re-entrant tachycardia (AVRT) vs atrioventricular nodal re-entrant tachycardia (AVNRT) from the 12-lead ECG, when using findings from the invasive electrophysiology (EP) study as the gold standard.
We trained a CNN on data from 124 patients undergoing EP studies with a final diagnosis of AVRT or AVNRT. A total of 4962 5-second 12-lead ECG segments were used for training. Each case was labeled AVRT or AVNRT based on the findings of the EP study. The model performance was evaluated against a hold-out test set of 31 patients and compared to an existing manual algorithm.
The model had an accuracy of 77.4% in distinguishing between AVRT and AVNRT. The area under the receiver operating characteristic curve was 0.80. In comparison, the existing manual algorithm achieved an accuracy of 67.7% on the same test set. Saliency mapping demonstrated the network used the expected sections of the ECGs for diagnoses; these were the QRS complexes that may contain retrograde P waves.
We describe the first neural network trained to differentiate AVRT from AVNRT. Accurate diagnosis of arrhythmia mechanism from a 12-lead ECG could aid preprocedural counseling, consent, and procedure planning. The current accuracy from our neural network is modest but may be improved with a larger training dataset.
Supraventricular Arrhythmias: Clinical Framework and Common Scenarios for the Internist
2018, Mayo Clinic Proceedings
Citation Excerpt :
This is because of prolongation in the PR interval, and thus the subsequent P wave is closer to the QRS complex because of the delay. Although short, the RP interval in orthodromic AVRT (Figure 1) is almost never less than 90 milliseconds because of the necessary time it takes for electrical activation to occur in series from the ventricular tissue to the accessory pathway and from the accessory pathway to the atrium.29,30 Atrial tachycardias or sinus tachycardias are classified as long RP tachycardias.
Supraventricular arrhythmias can cause uncomfortable symptoms for patients. Often, the first point of contact is in the primary care setting, and thus, it is imperative for the general internist to have a clinical framework in place to recognize this cluster of cardiac arrhythmias, be familiar with immediate and long-term management of supraventricular tachycardias, and understand when cardiac electrophysiologic consultation is necessary. The electrocardiographic characteristics can have subtle but important clues to the diagnosis and initial management. An understanding of the mechanisms of these arrhythmias is essential to provide proper therapy to the patient. In addition, there are common practice strategies that should be emphasized to avoid common misperceptions that could pose risk to the patient. In this review, we provide a framework to more easily recognize and classify these arrhythmias. We also illustrate the mechanism for these arrhythmias to provide an understanding of the interventions generally used.
Diagnostic Accuracy of Several Electrocardiographic Criteria for the Prediction of Atrioventricular Nodal Reentrant Tachycardia
2016, Archives of Medical Research
Atrioventricular nodal reentrant tachycardia (AVNRT) is the most common form of paroxysmal supraventricular tachycardia (SVT) whose diagnosis can be strongly suspected based on the surface eletrocardiogram alone. The purpose of this study is to determine the diagnostic accuracy of several electrocardiographic (ECG) criteria for the prediction of AVNRT.
Between November 2010 and January 2014, a total of 256 patients who underwent electrophysiological testing (EP) with regular, paroxysmal and narrow QRS complex tachycardia were prospectively enrolled. We classified the ECG recordings during tachycardia for the presence of the following criteria: a) classical ECG findings of pseudo S wave in inferior leads and/or pseudo r′ wave in lead V1, b) notch in lead aVL, c) no retrograde P waves visible during tachycardia; d) pseudo r′ wave in lead aVR, e) notch in lead D1, f) any deflection after 100 ms of the QRS complex during tachycardia.
On multivariate analysis, independent predictors of AVNRT diagnosis were female sex (OR 4.17; 95% CI [2.11–8.24]; p <0.001), age >60 years (OR 3.53; 95% CI [1.25–9.96]; p = 0.017) and the classical ECG criteria (OR 7.41; 95% CI [3.62–15.17]; p <0.001).
Female, age >60 years and the classical ECG criteria were the independent predictors of AVNRT diagnosis. Although several of the ECG criteria for AVNRT diagnosis showed acceptable sensitivities and specificities, they do not improve its accuracy.
The importance of utilizing 24-h Holter monitoring as a non-invasive method of predicting the mechanism of supraventricular tachycardia
2011, Journal of the Saudi Heart Association
Despite the emergence of advanced invasive technology in identifying the various types of arrhythmia mechanisms, 24-h ambulatory electrocardiogram monitoring as a non-invasive method remains an invaluable informative tool in delineating such mechanisms. Furthermore, one observational study has supported the utilization of 24-h Holter monitoring in exploring AV Node (AVN) characteristics sufficiently in correlation with invasive studies when limited to patients without Wolf Parkinson White syndrome showing a positive predictive value of 98% in their supraventricular tachycardia (SVT) assessment (Fukuda et al., 2005).
We describe in this report suspected tachycardia initiation mechanism in three SVT cases based on 24-h Holter recordings. Premature atrial contraction with subsequent AVN fast pathway conduction block initiated the common type AVN re-entrant tachycardia (AVNRT). Dual AVN physiology was documented during the electrophysiological studies in all three cases and a definitive therapy was achieved by the AVN slow pathway modification.

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Regular paperElectrocardiographic differentiation of typical atrioventricular node reentrant tachycardia from atrioventricular reciprocating tachycardia mediated by concealed accessory pathway in children

Abstract

Section snippets

Electrocardiograph selection

Part 1

Inter- and intraobserver concordance

Holter/event recorder electrocardiographic analyses (table 3)

Discussion

Am J Cardiol

J Am Coll Cardiol

Am J Cardiol

J Am Coll Cardiol

Am Heart J

J Am Coll Cardiol

Regular paper
Electrocardiographic differentiation of typical atrioventricular node reentrant tachycardia from atrioventricular reciprocating tachycardia mediated by concealed accessory pathway in children