A simple algorithm for localizing accessory pathways in patients with Wolff-Parkinson-White syndrome using only the R/S ratio

EPM algorithm: A stepwise approach to accessory pathway localization in ventricular pre-excitation

BACKGROUND

Accurate estimation of accessory pathway (AP) localization in patients with ventricular pre-excitation or Wolff-Parkinson-White (WPW) syndrome remains a diagnostic challenge. Existing algorithms have contributed significantly to this area, but alternative algorithms can offer additional perspectives and approaches to AP localization.

OBJECTIVE

This study introduces and evaluates the diagnostic accuracy of the EPM algorithm in AP localization, comparing it with established algorithms Arruda and EASY.

METHODS

A retrospective analysis was conducted on 138 patients from Hospital São Paulo who underwent catheter ablation. Three blinded examiners assessed the EPM algorithm's diagnostic accuracy against the Arruda and EASY algorithms. The gold standard for comparison was the radioscopic position of the AP where radiofrequency ablation led to pre-excitation disappearance on the ECG.

RESULTS

EPM showed a diagnostic accuracy of 51.45%, closely aligning with Arruda (53.29%) and EASY (44.69%). Adjacency accuracy for EPM was 70.67%, with Arruda at 66.18% and EASY at 72.22%. Sensitivity for EPM in distinguishing left vs. right APs was 95.73%, with a specificity of 74.33%. For identifying septal vs. lateral right APs, EPM sensitivity was 82.79% with a specificity of 46.15%. These measures were comparable to those of Arruda and EASY. Inter-observer variability was excellent for EPM, with Kappa statistics over 0.9.

CONCLUSION

The EPM algorithm emerges as a reliable tool for AP localization, offering a systematic approach beneficial for therapeutic decision-making in electrophysiology. Its comparable diagnostic accuracy and excellent inter-observer variability underscore its potential clinical applicability. Future research may further validate its efficacy in a broader clinical setting.

Wolf-Parkinson-White Syndrome: Diagnosis, Risk Assessment, and Therapy-An Update

Wolf-Parkinson-White (WPW) syndrome is a disorder characterized by the presence of at least one accessory pathway (AP) that can predispose people to atrial/ventricular tachyarrhythmias and even sudden cardiac death. It is the second most common cause of paroxysmal supraventricular tachycardia in most parts of the world, affecting about 0.1-0.3% of the general population. Most patients with WPW syndrome have normal anatomy, but it may be associated with concomitant congenital heart disease or systemic diseases. Although many individuals are asymptomatic, during supraventricular arrhythmia episodes, they may experience severe symptoms, including syncope or even sudden cardiac death (mainly due to pre-excited atrial fibrillation over rapidly conducting AP). In addition to arrhythmia-related symptoms, for some specific locations of the APs with overt anterograde conduction, there might be a reduction in exercise capacity mediated by a reduction in LV systolic performance due to anomalous LV depolarization. Although it is typically diagnosed through electrocardiography (ECG), additional tests are necessary for risk assessment. Management of WPW syndrome may be quite challenging and can vary from only acknowledging the presence of the accessory pathway to pharmacological treatment or radiofrequency ablation. Early diagnosis, risk assessment, and appropriate treatment are critical steps in the management of WPW syndrome, aiming to improve the quality of life and reduce the risk of life-threatening arrhythmias.

The Wolff-Parkinson-White pattern in neonates: results from a large population-based cohort study

AIMS

Wolff-Parkinson-White (WPW) syndrome is a conduction disorder characterized by an accessory electrical pathway between the atria and ventricles, which may predispose to supraventricular tachycardia (SVT) and sudden cardiac death. It can be seen as an isolated finding or associated with structural heart disease. Our aims were to determine the prevalence of a WPW pattern in a large and unselected cohort of neonates and to describe the electro- and echocardiographic characteristics as well as the natural history during early childhood.

METHODS AND RESULTS

Electrocardiograms and echocardiograms of neonates (aged 0-30 days) from a large, prospective, population-based cohort study were included. Neonates with a WPW pattern were identified and matched 1:4 to controls. Localization of the accessory pathway was assessed by different algorithms. Among 17 489 neonates, we identified 17 (76% boys) with a WPW pattern consistent with a prevalence of 0.1%. One neonate had moderate mitral regurgitation while other echocardiographic parameters were similar between cases and controls (all P > 0.05). The accessory pathways were primarily predicted to be left-sided. At follow-up (available in 14/17 children; mean age 3.2 years) the pre-excitation pattern persisted in only four of the children and none of the children had experienced any episodes of SVT.

CONCLUSION

The prevalence of a WPW pattern in our cohort of unselected neonates was 0.1%. The WPW pattern was more frequent in boys and generally not associated with structural heart disease, and the accessory pathways were primarily left-sided. At follow-up, the WPW pattern had disappeared in most of the children suggesting either an intermittent nature or that normalization occurs.

CLINICAL TRIAL REGISTRATION

Copenhagen Baby Heart, NCT02753348.

Accuracy of Algorithms Predicting Accessory Pathway Localization in Pediatric Patients with Wolff-Parkinson-White Syndrome

We aimed to assess the accuracy of determining accessory pathway (AP) localization from 12 lead ECG tracings by applying 12 different algorithms in pediatric patients diagnosed with Wolff-Parkinson-White syndrome. We compared algorithm accuracy in electrophysiologic study ECG tracings with full preexcitation and resting ECG tracings. The assessing pediatric cardiologists were blinded regarding EP study results on AP localization. For exact AP location, the algorithms published by D'Avila et al. and Boersma et al. yielded the highest accuracy (58%). Distinguishing laterality, the median accuracy for predicting left or right-sided APs was 74%, while for septal APs it was 68%. We conclude that algorithms predicting AP location in pediatric patients with Wolff-Parkinson-White syndrome show low accuracy for exact AP localization. For laterality, however, accuracy was significantly higher.

Is there a need for a novel algorithm for accessory pathways localization?

Wolff Parkinson White Syndrome (WPW) affects between 0.1% and 0.2% of the population, causes morbidity due to supraventricular tachycardia (SVT) and can lead to sudden cardiac arrest [1-3]. This article is protected by copyright. All rights reserved.

Accessory pathway analysis using a multimodal deep learning model

Cardiac accessory pathways (APs) in Wolff-Parkinson-White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.

Management of asymptomatic ventricular preexcitation

An incidental discovery of Wolff Parkinson White (WPW) pattern on the electrocardiogram (ECG) is not an infrequent finding facing the physician. Most patients discovered incidentally are asymptomatic and it is hard to justify further management of such patients given the time-honored adage to “first do no harm.” However, this finding does have implications. This article is an attempt to guide clinicians about this important issue that is often faced in the office.