Estimation of the origin of ventricular outflow tract arrhythmia using synthesized right-sided chest leads

Risk stratification for the occurrence of ventricular fibrillation in patients with early repolarization syndrome

BACKGROUND

Several signs of malignant early repolarizations have been proposed in patients with early repolarization syndrome (ERS). However, reports have challenged the efficacy of these signs in predicting future ventricular fibrillation (VF) in patients with ERS.

OBJECTIVE

This study aimed to assess the predictive value of various electrocardiogram (ECG) markers for future VF events in patients with ERS.

METHODS

We retrospectively evaluated the clinical characteristics of 44 patients with ERS to identify risk factors for VF during follow-up.

RESULTS

After the initial event, 16 patients experienced VF (VF group), whereas 28 did not (non-VF group). The VF group had a longer QRS interval, more fragmented QRS (fQRS), and a higher T/R voltage ratio than the non-VF group. Wide J waves were more prevalent in the VF group; however, other J-wave markers did not differ between the groups. Positive late potentials recorded on signal-averaged ECGs were more frequent in the VF group. Whereas none of the patients showed spontaneous Brugada syndrome on ECG, the VF group frequently exhibited pilsicainide-induced ST-segment elevation. These ECG markers were significantly associated with the occurrence of VF during follow-up. Patients with multiple ECG factors, including QRS abnormalities (wide QRS or fQRS), wide J waves, and a high T/R ratio, had a worse prognosis than patients without multiple factors, effectively stratifying patient risk.

CONCLUSION

The occurrence of VF in patients with ERS may be associated with conduction abnormalities such as QRS widening, fQRS, high T/R ratio, positive late potentials, and pilsicainide test results. Therefore, ECG factors could be useful in identifying high-risk patients.

Clinical characteristics of electrical storm in patients with early repolarization syndrome

BACKGROUND

Early repolarization syndrome (ERS) is an idiopathic ventricular fibrillation (VF) associated with inferolateral J waves. While electrical storm (ES) in ERS is not rare, their characteristics and risk factors are not fully understood.

OBJECTIVE

This study aimed to clarify the significance of ES in ERS.

METHODS

We evaluated 44 patients with ERS who experienced VF/sudden cardiac death or arrhythmic syncope. We assessed clinical characteristics to identify the risk factors for ES.

RESULTS

In total, 13 patients (30%) experienced ES (ES group). Of these, 11 patients (85%) experienced ES during the acute phase of initial VF episodes and 2 patients (2%) experienced ES during follow-up. VF associated with ES occurred during therapeutic hypothermia in 6 of 13 patients (46%). The J-wave voltage during therapeutic hypothermia was higher in the ES group than that in the patients without ES. Isoproterenol was used in 5 patients (38%), which decreased J-wave voltage and relieved ES. Among the clinical markers, shorter QT and QTp intervals (the interval from QRS onset to the peak of T wave), pilsicainide-induced ST elevation, and high scores on the Shanghai Score System were associated with ES. Although pilsicainide induced ST elevation in 6 of 34 patients (18%), spontaneous Brugada electrocardiographic patterns did not appear to be associated with VF. Therapeutic hypothermia was also a risk factor for acute phase ES.

CONCLUSION

Patients with ERS in the ES group frequently had short QT and QTp intervals, pilsicainide-induced ST elevations, and high Shanghai Score System scores. Therapeutic hypothermia was also associated with acute phase ES.

Significance of left posterior extension of early repolarization in patients with J-wave syndrome

BACKGROUND

J waves in the inferior or lateral leads are characteristic electrocardiographic (ECG) changes in patients with early repolarization syndrome (ERS). However, the presence of J waves in the left posterior region has not yet been evaluated.

OBJECTIVE

The purpose of this study was to clarify the significance of J waves in the posterior left ventricle using leads V7-V9 and a body surface mapping (BSM) system.

METHODS

Forty patients diagnosed with ERS were included. All patients exhibited J waves in either the contiguous inferior, lateral, or posterior leads. We evaluated the incidence of J waves in the inferolateral and posterior leads using a 15-lead ECG with synthesized V7-V9 and an 87-lead BSM. Additionally, we assessed the arrhythmogenicity of the posterior regions based on the morphology of the premature ventricular complexes (PVCs) associated with ventricular fibrillation (VF).

RESULTS

J waves were observed in the lateral, inferior, and posterior leads of 26 (65%), 31 (78%), and 39 (97%) patients, respectively. J waves were found only in the posterior leads of 5 patients. BSM was evaluated in 9 patients, all of whom exhibited a positive area on the posterior region. PVCs associated with VF were recorded in 5 patients. Among patients with inferolateral and posterior J waves, all except 1 patient who displayed left bundle branch block morphology showed PVCs originating from the posterior left ventricular region.

CONCLUSION

Posterior J waves are common in ERS patients. This abnormality can be detected using leads V7-V9 and the BSM system and may be associated with arrhythmogenesis.

Electrocardiographic Characteristics, Identification, and Management of Frequent Premature Ventricular Contractions

Premature ventricular complexes (PVCs) are frequently encountered in clinical practice. The association of PVCs with adverse cardiovascular outcomes is well established in the context of structural heart disease, yet not so much in the absence of structural heart disease. However, cardiac magnetic resonance (CMR) seems to contribute prognostically in the latter subgroup. PVC-induced myocardial dysfunction refers to the impairment of ventricular function due to PVCs and is mostly associated with a PVC burden > 10%. Surface 12-lead ECG has long been used to localize the anatomic site of origin and multiple algorithms have been developed to differentiate between right ventricular and left ventricular outflow tract (RVOT and LVOT, respectively) origin. Novel algorithms include alternative ECG lead configurations and, lately, sophisticated artificial intelligence methods have been utilized to determine the origins of outflow tract arrhythmias. The decision to therapeutically address PVCs should be made upon the presence of symptoms or the development of PVC-induced myocardial dysfunction. Therapeutic modalities include pharmacological therapy (I-C antiarrhythmic drugs and beta blockers), as well as catheter ablation, which has demonstrated superior efficacy and safety.

A High Precision Machine Learning-Enabled System for Predicting Idiopathic Ventricular Arrhythmia Origins

Background

Radiofrequency catheter ablation (CA) is an efficient antiarrhythmic treatment with a class I indication for idiopathic ventricular arrhythmia (IVA), only when drugs are ineffective or have unacceptable side effects. The accurate prediction of the origins of IVA can significantly increase the operation success rate, reduce operation duration and decrease the risk of complications. The present work proposes an artificial intelligence-enabled ECG analysis algorithm to estimate possible origins of idiopathic ventricular arrhythmia at a clinical-grade level accuracy.

Method

A total of 18,612 ECG recordings extracted from 545 patients who underwent successful CA to treat IVA were proportionally sampled into training, validation and testing cohorts. We designed four classification schemes responding to different hierarchical levels of the possible IVA origins. For every classification scheme, we compared 98 distinct machine learning models with optimized hyperparameter values obtained through extensive grid search and reported an optimal algorithm with the highest accuracy scores attained on the testing cohorts.

Results

For classification scheme 4, our pioneering study designs and implements a machine learning-based ECG algorithm to predict 21 possible sites of IVA origin with an accuracy of 98.24% on a testing cohort. The accuracy and F1-score for the left three schemes surpassed 99%.

Conclusion

In this work, we developed an algorithm that precisely predicts the correct origins of IVA (out of 21 possible sites) and outperforms the accuracy of all prior studies and human experts.

Evaluation of five algorithms in predicting the sublocalisation of right ventricular outflow tract arrhythmia (RVOTA) when compared to 3D electroanatomical mapping origin

Purpose

To compare the predictive accuracy of five different algorithms as verified by successful ablation site using 3D electroanatomical non-contact mapping in patients with symptomatic and asymptomatic but high ventricular burden RVOT tachycardias.

Methods

28 Consecutive patients admitted for radiofrequency catheter ablation for symptomatic and asymptomatic, but high ventricular burden idiopathic VPC were recruited for this study. All patients had previous failed or intolerant to beta-blocker and/or at least one class IC anti-arrhythmic agents, and they had normal left ventricular ejection fraction. All patients had documented monomorphic VPC with left bundle branch block morphology and an inferior axis. Concordance of the arrhythmia origin based on ECG algorithm and 3D mapping system site were further evaluated. Of the five algorithms, two algorithms with easy‐applicability and having a memorable design (Dixit and Joshi) and three algorithms with more complex and detailed design (Ito, Zhang, Pytkowski) were selected for comparisons.

Results

Assessment of the diagnostic accuracy showed that each of the five algorithms had only moderate accuracy, and the greatest accuracy was observed in the algorithm proposed by Pytkowski algorithm when assessed by a general cardiologist and Dixit algorithm when evaluated by the electrophysiologist. However, when the algorithms were compared for their accuracy, specificity, sensitivity, no significant differences were found (p = 0.99).

Conclusions

The ECG based algorithms for precise localising RVOTA origin simplify the mapping process, reduce the procedural and fluoroscopic time, and improve clinical outcomes, resulting in greater clinical utility. All the five published 12-lead ECG algorithms for ROTVA differentiation were similar in terms of the diagnostic accuracy, specificity, sensitivity and LRs.

A High-Precision Machine Learning Algorithm to Classify Left and Right Outflow Tract Ventricular Tachycardia

Introduction

Multiple algorithms based on 12-lead ECG measurements have been proposed to identify the right ventricular outflow tract (RVOT) and left ventricular outflow tract (LVOT) locations from which ventricular tachycardia (VT) and frequent premature ventricular complex (PVC) originate. However, a clinical-grade machine learning algorithm that automatically analyzes characteristics of 12-lead ECGs and predicts RVOT or LVOT origins of VT and PVC is not currently available. The effective ablation sites of RVOT and LVOT, confirmed by a successful ablation procedure, provide evidence to create RVOT and LVOT labels for the machine learning model.

Methods

We randomly sampled training, validation, and testing data sets from 420 patients who underwent successful catheter ablation (CA) to treat VT or PVC, containing 340 (81%), 38 (9%), and 42 (10%) patients, respectively. We iteratively trained a machine learning algorithm supplied with 1,600,800 features extracted via our proprietary algorithm from 12-lead ECGs of the patients in the training cohort. The area under the curve (AUC) of the receiver operating characteristic curve was calculated from the internal validation data set to choose an optimal discretization cutoff threshold.

Results

The proposed approach attained the following performance: accuracy (ACC) of 97.62 (87.44–99.99), weighted F1-score of 98.46 (90–100), AUC of 98.99 (96.89–100), sensitivity (SE) of 96.97 (82.54–99.89), and specificity (SP) of 100 (62.97–100).

Conclusions

The proposed multistage diagnostic scheme attained clinical-grade precision of prediction for LVOT and RVOT locations of VT origin with fewer applicability restrictions than prior studies.

A High-Precision Machine Learning Algorithm to Classify Left and Right Outflow Tract Ventricular Tachycardia

Introduction

Multiple algorithms based on 12-lead ECG measurements have been proposed to identify the right ventricular outflow tract (RVOT) and left ventricular outflow tract (LVOT) locations from which ventricular tachycardia (VT) and frequent premature ventricular complex (PVC) originate. However, a clinical-grade machine learning algorithm that automatically analyzes characteristics of 12-lead ECGs and predicts RVOT or LVOT origins of VT and PVC is not currently available. The effective ablation sites of RVOT and LVOT, confirmed by a successful ablation procedure, provide evidence to create RVOT and LVOT labels for the machine learning model.

Methods

We randomly sampled training, validation, and testing data sets from 420 patients who underwent successful catheter ablation (CA) to treat VT or PVC, containing 340 (81%), 38 (9%), and 42 (10%) patients, respectively. We iteratively trained a machine learning algorithm supplied with 1,600,800 features extracted via our proprietary algorithm from 12-lead ECGs of the patients in the training cohort. The area under the curve (AUC) of the receiver operating characteristic curve was calculated from the internal validation data set to choose an optimal discretization cutoff threshold.

Results

The proposed approach attained the following performance: accuracy (ACC) of 97.62 (87.44-99.99), weighted F1-score of 98.46 (90-100), AUC of 98.99 (96.89-100), sensitivity (SE) of 96.97 (82.54-99.89), and specificity (SP) of 100 (62.97-100).

Conclusions

The proposed multistage diagnostic scheme attained clinical-grade precision of prediction for LVOT and RVOT locations of VT origin with fewer applicability restrictions than prior studies.

Comparative analysis of electrocardiographic imaging and ECG in predicting the origin of outflow tract ventricular arrhythmias

Objectives

The aim of this study was to investigate the accuracy of electrocardiographic imaging (ECGI) in localizing the origin of outflow tract ventricular arrhythmias (OTVAs) and compare its performance with that of seven published 12-lead electrocardiography (ECG) algorithms.

Methods

Patients with OTVAs who were undergoing catheter ablation were prospectively investigated. The OVTA origins were localized using both ECGI and seven 12-lead ECG algorithms, with the successful ablation site set as the gold standard. The performance of the ECGI and 12-lead ECG algorithms were compared.

Results

Twenty-seven patients were enrolled into the study. The ECGI system correctly identified the chamber of OTVA origin in 27/27 (100%) patients and the sublocalization within the right ventricular outflow tract (RVOT) in 21/22 (95.5%) patients. However, the ECG algorithms correctly diagnosed the chamber and sublocalization in only 21/27 (77.8%) patients and 13/22 (59.1%) patients, respectively, which was significantly lower compared with the ECGI system.

Conclusions

Non-invasive ECGI can accurately predict the origin of OTVAs in a manner that is superior to that of conventional 12-lead ECGs in differentiating the RVOT from the left ventricular outflow tract (LVOT) and septum from free wall in the RVOT. This provides a useful tool to guide catheter ablation.

This trial has been registered in the Chinese Clinical Trial Registry (Registration number: ChiCTR1900025527).

Man vs machine: Performance of manual vs automated electrocardiogram analysis for predicting the chamber of origin of idiopathic ventricular arrhythmia

BACKGROUND

Radiofrequency catheter ablation of idiopathic ventricular arrhythmias (VAs) is performed to eliminate symptoms and to prevent or reverse arrhythmia-induced cardiomyopathy. Preprocedural prediction of the chamber of VA origin is critical for patient counseling, procedure planning, and guidance of invasive mapping.

OBJECTIVE

We aimed to assess the performance of manual expert versus automated 12-lead electrocardiogram (ECG) analysis in the prediction of VA origin.

METHODS

Patients with ablation of idiopathic VA and sustained success were included. The VA origin was defined as the site where ablation caused arrhythmia suppression. Standard baseline 12-lead ECGs with documentation of the VA were analyzed manually in a blinded fashion by three electrophysiologists and three electrophysiology (EP) fellows. In addition, the same standard 12-lead ECG was analyzed by an automated computer algorithm using a vectorcardiographic approach.

RESULTS

Thirty-eight patients (median age, 47 [interquartile range, 37-58]; 68% female) were enrolled. The VA originated from the right ventricle in 24 (63%) and the left ventricle in 14 (37%) patients. The electrophysiologists and EP fellows identified the VA chamber of origin with a similar accuracy of 73% and 72% (P = .72). The automated algorithm showed a higher accuracy of 89% (P = .03 compared with electrophysiologists and EP fellows). This resulted in a sensitivity of 95% and specificity of 86%.

CONCLUSION

While the manual ECG analysis of the standard 12-lead ECG by both electrophysiologists and EP fellows correctly identified the chamber of VA origin in around 75% of cases, an automated vectorcardiographic computer algorithm achieved an accuracy of 89% with clinically acceptable diagnostic parameters.

Estimation of the accessory pathway location of the manifest Wolf-Parkinson-White syndrome using synthesized right-sided chest leads

PURPOSE

The classification using QRS morphology of V1 lead is a useful simple predictor of accessory pathway location (type A, R or Rs pattern; type B, rS pattern; type C, QS or Qr pattern), but often leads to misdiagnosis of accessory pathway location, especially in types B and C. The synthesized 18-lead electrocardiography (ECG) derived from standard 12-lead ECG can provide virtual waveforms of right-sided chest leads. This study aimed to evaluate the usefulness of the right-sided chest lead ECG for prediction of accessory pathway location.

METHODS

This retrospective study included 44 patients in whom successful ablation of manifest Wolff-Parkinson-White (WPW) syndrome was performed. Synthesized ECG waveforms were automatically generated, and ECG data obtained before the procedure.

RESULTS

There were 26, 4, and 14 patients with left, right, and septal accessory pathways, respectively. All left accessory pathway cases have type A in V1 and syn-V4R leads. Of the 4 right accessory pathway cases, 2 have type B in V1 and syn-V4R leads. Other 2 of 4 cases have type C. In V1 lead, 5 of 14 septal accessory pathway cases have type C, 7 of 14 cases have type B, and 2 of 14cases have type A. In syn-V4R lead, all 14 septal accessory pathway cases have type C. The QRS morphology of V1 and syn-V4 leads could predict the site of accessory pathway with overall accuracy of 79% and 95%, respectively.

CONCLUSIONS

QRS morphology of syn-V4R lead may be useful for predicting accessory pathway location of manifest WPW syndrome.

V3R/V7 Index: A Novel Electrocardiographic Criterion for Differentiating Left From Right Ventricular Outflow Tract Arrhythmias Origins

BACKGROUND

Several algorithms have been proposed to predict the origin of outflow tract (OT) ventricular arrhythmias (VAs) using standard 12-lead ECG. However, the additive value of right precordial and posterior leads is unknown.

METHODS

Standard 12-lead ECG, right precordial leads ECG (V₃R, V₄R, V₅R) and posterior leads ECG (V₇, V₈, V₉) were recorded and analyzed in a development cohort of consecutive patients undergoing OT-VAs ablation at a single center. These findings informed the development of a novel algorithm incorporating right precordial and posterior leads to discriminate between left ventricular OT (LVOT) and right ventricular OT (RVOT) foci. The performance of this novel algorithm which includes the V₃R/V₇ index was prospectively tested in a validation cohort of consecutive patients undergoing OT-VA ablation at 4 centers and compared with published algorithms. The location of the foci was determined by the successful ablation site.

RESULTS

One hundred ninety-one patients were recruited, of which 94 formed the validation cohort (mean age of 45.7±15.6, 39% male, 79% RVOT foci). During OT-VAs, a QS pattern in lead V₃R and an S wave in lead V₇ were exclusively recorded in RVOT and LVOT foci, respectively. The V₃R/V₇ index of LVOT origin was significantly greater than that of RVOT (1.05±0.83 versus 0.28±0.23, P<0.001). The V₃R/V₇ index ≥0.85 predicted an LVOT origin with 87% sensitivity and 96% specificity. In the prospective evaluation, when the V₃R/V₇ index ≥0.85, an RVOT origin could be excluded with 98.6% accuracy. The area under the curve of V₃R/V₇ index (0.954) was larger than that of previously reported ECG criteria, including V₂S/V₃R (0.896), V₂ transition ratio (0.792), and transition zone index (0.666). This novel index was also accurate in both patients without obvious LVOT or RVOT origins and subgroups with cardiac rotation or lead V₃ R/S transition.

CONCLUSIONS

The V₃R/V₇ index is a novel and accurate ECG criterion that predicts OT-VAs origin.

The P wave morphology in lead V7 on the synthesized 18-lead ECG is a useful parameter for identifying arrhythmias originating from the right inferior pulmonary vein

Atrial tachyarrhythmias often originate from the superior vena cava (SVC), and right superior (RSPV) and inferior pulmonary veins (RIPV). However, a precise differentiation of those origins is challenging using the standard 12-lead electrocardiogram (ECG) P-wave morphology due to the anatomical proximity. The recently developed synthesized 18-lead ECG provides virtual waveforms of the right-sided chest and back leads. This study evaluated the utility of the synthesized 18-lead ECG to differentiate atrial arrhythmias originating from 3 adjacent structures. Synthesized 18-lead ECGs were obtained during SVC-, RSPV-, and RIPV-pacing in 20 patients with lone paroxysmal atrial fibrillation to develop an algorithm. The P-wave morphologies were classified into 4 patterns: positive, negative, biphasic, and isoelectric. Subsequently, the algorithm's accuracy was validated prospectively in another 40 patients. In retrospective analyses, isoelectric P-waves in synthesized V7 distinguished RIPV-pacing from the others (sensitivity = 81%, specificity = 92%) (first criteria). The P wave morphologies in Leads II (sensitivity = 83%, specificity = 94%) and V1 (sensitivity = 84%, specificity = 80%) distinguished SVC- and RSPV-pacing (second criteria). In a prospective evaluation, the sensitivity, specificity, positive predictive value [PPV], negative predictive value [NPV], and accuracy of the first criteria for identifying RIPV-pacing was 97%, 90%, 78%, 99%, and 92%, respectively. The sensitivity, specificity, RPV, NPV, and accuracy of the second criteria (amplitudes > 1 mV in lead II or biphasic P-waves in lead V1) for discriminating SVC- and RSPV-pacing was 66%, 95%, 98%, 50%, and 74%, respectively. The P wave morphology pattern in lead V7 in synthesized 18-lead ECGs is useful for differentiating RIPV origins from RSPV/SVC origins.

Characteristics of synthesized right-sided chest electrocardiograms in patients with acute pulmonary embolism

BACKGROUND

The significance of right-sided chest lead electrocardiogram (ECG) abnormalities in acute pulmonary embolism (APE) is unclear. This study evaluated the characteristics of such abnormalities in APE patients.

METHODS

This retrospective study included consecutive patients who were diagnosed with APE by contrast-enhanced computed tomography or pulmonary artery angiography. A standard 12-lead ECG and a synthesized right-sided chest ECG were obtained from these patients. Waveform differences were noted between the acute and post-treatment phases.

RESULTS

In total, 56 APE patients (18 men and 38 women, mean age 66.7±13.3 years) were included. Traditional ECG findings, such as right-axis deviation, the S₁Q₃T₃ pattern, and clockwise rotation, were found in relatively few patients (14.3%, 32.1%, and 21.4%, respectively). In some cases, a negative T wave in standard 12-lead ECGs was observed in leads III, V1, and V2 (46.4%, 60.7%, and 39.9%, respectively). Syn-V3R ECG showed a higher frequency of negative T waves (66.1%) at the onset and significantly (p<0.01) decreased at the follow-up. Multiple logistic regression analyses for differentiating APE revealed that the negative T waves only in lead syn-V3R were significantly related (odds ratio: 6.95, 95% confidence interval: 2.50-19.32, p<0.001).

CONCLUSIONS

The presence of a negative T wave in a synthesized right-sided chest ECG, particularly in the V3R lead, is a new and distinctive finding denoting pulmonary embolism. To confirm the utility of this characteristic using synthesized right-sided chest ECGs for the diagnosis of APE, further studies with larger populations will be required.

Detection and Evaluation of Pulmonary Hypertension by a Synthesized Right-Sided Chest Electrocardiogram

BACKGROUND

Current standard 12-lead electrocardiogram (ECG) criteria for diagnosing pulmonary hypertension (PH) have a low sensitivity. Although the right-sided chest ECG (V3R-V5R) increases the diagnostic accuracy, these additional leads are not routinely recorded. The aim of the present study was to assess the usefulness of the synthesized right-sided chest ECG (Syn-ECG), generated from 12-lead ECG information, in the detection and evaluation of PH.

PATIENTS AND METHODS

The Syn-ECG waveforms in 30 patients with PH, defined as an estimated pulmonary arterial systolic pressure (PASP) >35 mmHg, were compared to those in 30 age- and gender-matched normal subjects.

RESULTS

The R wave amplitude and R/S ratio in the Syn-ECGs were significantly (P<0.01) greater in patients with PH than in the controls. The R wave amplitude in the Syn-ECGs exhibited a significant and better correlation (correlation coefficient 0.513-0.596, P<0.001) with the PASP than lead V1 (correlation coefficient 0.375, P=0.02). A receiver-operating characteristic curve analysis showed that the R wave amplitude (AUC 0.802, P<0.001) and R/S ratio (AUC 0.823, P<0.001) in the synthesized V5R was a good predictor of PH. New criteria, including 1) an R in V5R>0.12 mV, and 2) R/S ratio in V5R>0.42, had an improved sensitivity (0.63 and 0.73, respectively) and comparable specificity (0.93 and 0.87, respectively) to the conventional criteria (sensitivity 0.10-0.43, specificity 0.90-1.00).

CONCLUSION

The diagnostic criteria derived from the Syn-ECG provided better diagnostic accuracy than the known conventional criteria from the standard 12-lead ECG. This technique described in the present study may be useful for diagnosing and evaluating PH.

A potential pitfall of the modified 12 lead electrocardiogram (Mason-Likar modification) in catheter ablation of idiopathic ventricular arrhythmias originating from the outflow tract

AIMS

The Mason-Likar modified electrocardiogram (ML-ECG) can be interchanged with standard 12 lead ECG electrode positions (standard ECG) without affecting the diagnostic interpretation during sinus rhythm, but the morphological differences during ventricular arrhythmias have not been sufficiently evaluated. This study aimed to elucidate the morphological changes in the ML-ECG precordial leads.

METHODS AND RESULTS

In 53 consecutive patients with premature ventricular contractions predicted to originate from the outflow tract (OT-PVCs), the arrhythmias were analysed by those two ECG methods. The OT-PVC origin sites, which were predicted by currently published criteria with the respective ECG methods prior to catheter ablation, were compared with the successful ablation sites. Compared with the standard-ECG, S-waves in the ML-ECG became shallower in leads V1-4 (P < 0.05 in lead V1; P < 0.001 in leads V2-4), and pseudo-R-waves in lead V1 appeared in seven patients. The precordial leads transition zone shifted counter-clockwise in 18 patients in the ML-ECG. In leads I and aVL, the negative deflection amplitudes of the ML-ECG were greater than those of the standard ECG (P < 0.001), and polarity reversals in lead I appeared in 18 patients. The R-wave amplitudes in all ML-ECG inferior leads were greater than those in the standard-ECG leads (all for P < 0.001). Those changes had an effect on the diagnostic indexes for the localization, and the specificity of the criteria for the ML-ECG was poorer than that for the standard-ECG.

CONCLUSION

Great differences were found between those two ECG methods. Predicting OT-PVC origins by diagnostic criteria with the ML-ECG might result in a misdiagnosis and inefficient ablation.

Differentiating the origin of outflow tract ventricular arrhythmia using a simple, novel approach

BACKGROUND

Numerous electrocardiographic (ECG) criteria have been proposed to identify localization of outflow tract ventricular arrhythmias (OT-VAs); however, in some cases, it is difficult to accurately localize the origin of OT-VA using the surface ECG.

OBJECTIVE

The purpose of this study was to assess a simple criterion for localization of OT-VAs during electrophysiology study.

METHODS

We measured the interval from the onset of the earliest QRS complex of premature ventricular contractions (PVCs) to the distal right ventricular apical signal (the QRS-RVA interval) in 66 patients (31 men aged 53.3 ± 14.0 years; right ventricular outflow tract [RVOT] origin in 37) referred for ablation of symptomatic outflow tract PVCs. We prospectively validated this criterion in 39 patients (22 men aged 52 ± 15 years; RVOT origin in 19).

RESULTS

Compared with patients with RVOT PVCs, the QRS-RVA interval was significantly longer in patients with left ventricular outflow tract (LVOT) PVCs (70 ± 14 vs 33.4±10 ms, P < .001). Receiver operating characteristic analysis showed that a QRS-RVA interval ≥49 ms had sensitivity, specificity, and positive and negative predictive values of 100%, 94.6%, 93.5%, and 100%, respectively, for prediction of an LVOT origin. The same analysis in the validation cohort showed sensitivity, specificity, and positive and negative predictive values of 94.7%, 95%, 95%, and 94.7%, respectively. When these data were combined, a QRS-RVA interval ≥49 ms had sensitivity, specificity, and positive and negative predictive values of 98%, 94.6%, 94.1%, and 98.1%, respectively, for prediction of an LVOT origin.

CONCLUSION

A QRS-RVA interval ≥49 ms suggests an LVOT origin. The QRS-RVA interval is a simple and accurate criterion for differentiating the origin of outflow tract arrhythmia during electrophysiology study; however, the accuracy of this criterion in identifying OT-VA from the right coronary cusp is limited.

Utility of automated template matching for the interpretation of pace mapping in patients ablated due to outflow tract ventricular arrhythmias

AIMS

One of the disadvantages of classic pace mapping (PM) is the operator's subjective interpretation. The aim of this single-centre retrospective study was to evaluate the value of automated template matching (AMT) in patients ablated due to ventricular outflow tract arrhythmias (OTAs).

METHODS AND RESULTS

From an overall group of 105 patients with OTA who were scheduled for transcatheter ablation (TA), AMT was accessible in 42 patients [21 right ventricular outflow tract (RVOT), 21 left ventricular outflow tract (LVOT), 28 women, aged 51.5 ± 12.7 years]. We used AMT to compare spontaneous arrhythmia ORS (spontQRS) with paced QRS complexes during PM in sites where radiofrequency (RF) applications were successful and in sites where RF applications were unsuccessful. The concordance was presented in per cents as objective matching scores (OMS). Then, at the successful ablation sites, we examined the relationship between OMS and the visual interpretation of PM was presented as electrophysiologists matching scores (EMS). The OMS of PM at sites of successful ablation varied from 78 to 99% (mean 94.1 ± 3.8) and from 47 to 95% (mean 80.2 ± 12.6%) at sites of unsuccessful ablation. Pace mapping in unsuccessful RF sites was significantly less similar to spontQRS morphologies than in successful RF sites (P = 0.0001). There was a significant correlation between OMS and EMS (r = 0.82; P < 0.0001). The OMS that indicated optimal ablation site was 89% (sensitivity = 95%; specificity = 80%). The mean OMS for successful sites at RVOT (95.1 ± 1.8%) and LVOT (93.1 ± 4.9%) were not different (P = 0.0551).

CONCLUSION

This analysis revealed that AMT is a valuable technique for the interpretation of PM and for the identification of successful ablation sites in OTA.

The QRS morphology pattern in V5R is a novel and simple parameter for differentiating the origin of idiopathic outflow tract ventricular arrhythmias

AIMS

There are many reports on the ECG characteristics of idiopathic outflow tract ventricular arrhythmias (OT-VAs) to predict their origin. However, differentiating near regions using 12-lead ECGs is still complicated. The synthesized 18-lead ECG derived from the 12-lead ECG can provide virtual waveforms of the right-sided chest leads (V3R, V4R, and V5R) and back leads (V7, V8, and V9). The aim of this study was to develop a simple and useful parameter for differentiating OT-VA origins using the 18-lead ECG.

METHODS AND RESULTS

We studied 28 and 73 patients with idiopathic VAs in a pacemapping study and validation cohort, respectively. In the pacemapping study, several sites out of five different sites were paced in each patient: the anterior and posterior right ventricular OT (RVOT-ant and RVOT-post), right and left coronary cusps (RCC and LCC), and junction of both cusps (RLJ). The 18-lead ECGs during pacemapping among the five sites were compared for establishing a simple parameter to predict VA origins. A novel parameter using 18-lead ECGs was tested prospectively in 73 patients. In the pacemapping study, the dominant QRS morphology pattern in the synthesized V5R significantly differed among those sites (RVOT-ant:Rs, RVOT-post:rS, RCC:QS, RLJ:qR, and LCC:R). The patients in the validation cohort were divided into five groups depending on those QRS morphology patterns during VAs in the synthesized V5R. Each V5R QRS morphology pattern could predict a precise origin of the OT-VAs with an overall accuracy of 75%.

CONCLUSION

The QRS morphology pattern in V5R was a simple and useful parameter for differentiating detailed OT-VA origins.