Wide Complex Tachycardia Differentiation: A Reappraisal of the State-of-the-Art

Aberrancy masquerading as ventricular tachycardia: Importance of invasive electrophysiology study for diagnosis of wide complex tachycardias

BACKGROUND

Differentiation between ventricular tachycardia (VT) and supraventricular tachycardia (SVT) with aberrancy based on the 12‑lead ECG alone can be imprecise. Implantable cardiac defibrillators (ICD) may be inserted for presumed VT, particularly in patients with syncopal presentation or atypical aberrancy patterns. Accurate diagnosis of these patients facilitated by an electrophysiology study (EPS) may alter diagnosis and management.

METHODS

We present a prospective collection of cases across 3 cardiac centers of consecutive patients with WCT presumed to be VT who were referred for consideration of an ICD, and in whom further evaluation including an EPS ultimately demonstrated SVT with aberrancy as the culprit arrhythmia.

RESULTS

22 patients were identified (17 male, mean age 50±13 years. Available rhythm data at the time of referral was presumptively diagnosed as monomorphic VT in 16 patients and polymorphic VT in 6 patients. Underlying structural heart disease was present in 20 (91%). EPS resulted in a diagnosis of SVT with aberrancy in all cases: comprising AV nodal re-entry tachycardia (n=10), orthodromic reciprocating tachycardia (n=3), focal atrial tachycardia (n=3), AF/AFL (n=3) and 'double fire' tachycardia (n=2). 21 (95%) patients underwent successful ablation. All patients remained free of arrhythmia recurrence at a median of 3.4 years of follow-up. ICD insertion was obviated in 18 (82%) patients, with 1 patient proceeding to ICD extraction.

CONCLUSION

SVT with atypical aberrancy may mimic monomorphic or polymorphic VT. Careful examination of all available rhythm data and consideration of an EPS can confirm SVT and obviate the need for ICD therapy.

Mayo Clinic VT calculator: A practical tool for accurate wide complex tachycardia differentiation

The discrimination of ventricular tachycardia (VT) versus supraventricular wide complex tachycardia (SWCT) via 12-lead electrocardiogram (ECG) is crucial for achieving appropriate, high-quality, and cost-effective care in patients presenting with wide QRS complex tachycardia (WCT). Decades of rigorous research have brought forth an expanding arsenal of applicable manual algorithm methods for differentiating WCTs. However, these algorithms are limited by their heavy reliance on the ECG interpreter for their proper execution. Herein, we introduce the Mayo Clinic ventricular tachycardia calculator (MC-VTcalc) as a novel generalizable, accurate, and easy-to-use means to estimate VT probability independent of ECG interpreter competency. The MC-VTcalc, through the use of web-based and mobile device platforms, only requires the entry of computerized measurements (i.e., QRS duration, QRS axis, and T-wave axis) that are routinely displayed on standard 12-lead ECG recordings.

The emergence and destiny of automated methods to differentiate wide QRS complex tachycardias

Accurate differentiation of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) using non-invasive methods such as 12‑lead electrocardiogram (ECG) interpretation is crucial in clinical practice. Recent studies have demonstrated the potential for automated approaches utilizing computerized ECG interpretation software to achieve accurate WCT differentiation. In this review, we provide a comprehensive analysis of contemporary automated methods for VT and SWCT differentiation. Our objectives include: (i) presenting a general overview of the emergence of automated WCT differentiation methods, (ii) examining the role of machine learning techniques in automated WCT differentiation, (iii) reviewing the electrophysiology concepts leveraged existing automated algorithms, (iv) discussing recently developed automated WCT differentiation solutions, and (v) considering future directions that will enable the successful integration of automated methods into computerized ECG interpretation platforms.

Diagnosis and management of ventricular tachycardia

Ventricular tachycardia (VT) describes rapid heart rhythms originating from the ventricles. Accurate diagnosis of VT is important to allow prompt referral to specialist services for ongoing management. The diagnosis of VT is usually made based on electrocardiographic data, most commonly 12-lead echocardiography (ECG), as well as supportive cardiac telemetric monitoring. Distinguishing between VT and supraventricular arrhythmias on ECG can be difficult. However, the VT diagnosis frequently needs to be made rapidly in the acute setting. In this review, we discuss the definition of VT, review features of wide-complex tachycardia (WCT) on ECG that might be helpful in diagnosing VT, discuss the different substrates in which VT can occur and offer brief comments on management considerations for patients found to have VT.

Comparison of two artificial intelligence-augmented ECG approaches: Machine learning and deep learning

BACKGROUND

Artificial intelligence-augmented ECG (AI-ECG) refers to the application of novel AI solutions for complex ECG interpretation tasks. A broad variety of AI-ECG approaches exist, each having differing advantages and limitations relating to their creation and application.

PURPOSE

To provide illustrative comparison of two general AI-ECG modeling approaches: machine learning (ML) and deep learning (DL).

METHOD COMPARISON

Two AI-ECG algorithms were developed to carry out two separate tasks using ML and DL, respectively. ML modeling techniques were used to create algorithms designed for automatic wide QRS complex tachycardia differentiation into ventricular tachycardia and supraventricular tachycardia. A DL algorithm was formulated for the task of comprehensive 12‑lead ECG interpretation. First, we describe the ML models for WCT differentiation, which rely upon expert domain knowledge to identify and formulate ECG features (e.g., percent monophasic time-voltage area [PMonoTVA]) that enable strong diagnostic performance. Second, we describe the DL method for comprehensive 12‑lead ECG interpretation, which relies upon the independent recognition and analysis of a virtually incalculable number of ECG features from a vast collection of standard 12‑lead ECGs.

CONCLUSION

We have showcased two different AI-ECG methods, namely ML and DL respectively. In doing so, we highlighted the strengths and weaknesses of each approach. It is essential for investigators to understand these differences when attempting to create and apply novel AI-ECG solutions.

Computerized electrocardiogram data transformation enables effective algorithmic differentiation of wide QRS complex tachycardias

BACKGROUND

Accurate automated wide QRS complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) can be accomplished using calculations derived from computerized electrocardiogram (ECG) data of paired WCT and baseline ECGs.

OBJECTIVE

Develop and trial novel WCT differentiation approaches for patients with and without a corresponding baseline ECG.

METHODS

We developed and trialed WCT differentiation models comprised of novel and previously described parameters derived from WCT and baseline ECG data. In Part 1, a derivation cohort was used to evaluate five different classification models: logistic regression (LR), artificial neural network (ANN), Random Forests [RF], support vector machine (SVM), and ensemble learning (EL). In Part 2, a separate validation cohort was used to prospectively evaluate the performance of two LR models using parameters generated from the WCT ECG alone (Solo Model) and paired WCT and baseline ECGs (Paired Model).

RESULTS

Of the 421 patients of the derivation cohort (Part 1), a favorable area under the receiver operating characteristic curve (AUC) by all modeling subtypes: LR (0.96), ANN (0.96), RF (0.96), SVM (0.96), and EL (0.97). Of the 235 patients of the validation cohort (Part 2), the Solo Model and Paired Model achieved a favorable AUC for 103 patients with (Solo Model 0.87; Paired Model 0.95) and 132 patients without (Solo Model 0.84; Paired Model 0.95) a corroborating electrophysiology procedure or intracardiac device recording.

CONCLUSION

Accurate WCT differentiation may be accomplished using computerized data of (i) the WCT ECG alone and (ii) paired WCT and baseline ECGs.

Wide complex tachycardia discrimination tool improves physicians' diagnostic accuracy

BACKGROUND

Timely and accurate discrimination of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular WCT (SWCT) is critically important. Previously we developed and validated an automated VT Prediction Model that provides a VT probability estimate using the paired WCT and baseline 12-lead ECGs. Whether this model improves physicians' diagnostic accuracy has not been evaluated.

OBJECTIVE

We sought to determine whether the VT Prediction Model improves physicians' WCT differentiation accuracy.

METHODS

Over four consecutive days, nine physicians independently interpreted fifty WCT ECGs (25 VTs and 25 SWCTs confirmed by electrophysiological study) as either VT or SWCT. Day 1 used the WCT ECG only, Day 2 used the WCT and baseline ECG, Day 3 used the WCT ECG and the VT Prediction Model's estimation of VT probability, and Day 4 used the WCT ECG, baseline ECG, and the VT Prediction Model's estimation of VT probability.

RESULTS

Inclusion of the VT Prediction Model data increased diagnostic accuracy versus the WCT ECG alone (Day 3: 84.2% vs. Day 1: 68.7%, p 0.009) and WCT and baseline ECGs together (Day 3: 84.2% vs. Day 2: 76.4%, p 0.003). There was no further improvement of accuracy with addition of the baseline ECG comparison to the VT Prediction Model (Day 3: 84.2% vs. Day 4: 84.0%, p 0.928). Overall sensitivity (Day 3: 78.2% vs. Day 1: 67.6%, p 0.005) and specificity (Day 3: 90.2% vs. Day 1: 69.8%, p 0.016) for VT were superior after the addition of the VT Prediction Model.

CONCLUSION

The VT Prediction Model improves physician ECG diagnostic accuracy for discriminating WCTs.

Ventricular ectopy following accessory pathway ablation in WPW syndrome

INTRODUCTION

Many Authors already described automatic activity arising from accessory pathways, but the underlying mechanism has not been clarified yet. They may be due to embryogenetic features of myocardium or may be related to specific excitability during radiofrequency ablation.

CASE

Our report shows that ventricular accelerated rhythm may transiently arise from the ventricular edge of a common myocardium made accessory pathway right after the ablation. No further action were required in our experience, since the phenomenon self extinguished in approximately hour.

CONCLUSIONS

If this manifestation represents the effect of thermal injury or if it is a real intrinsic automaticity is not fully documented and may need further reporting and investigation.

Automatic wide complex tachycardia differentiation using mathematically synthesized vectorcardiogram signals

BACKGROUND

Automated wide complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) may be accomplished using novel calculations that quantify the extent of mean electrical vector changes between the WCT and baseline electrocardiogram (ECG). At present, it is unknown whether quantifying mean electrical vector changes within three orthogonal vectorcardiogram (VCG) leads (X, Y, and Z leads) can improve automated VT and SWCT classification.

METHODS

A derivation cohort of paired WCT and baseline ECGs was used to derive five logistic regression models: (i) one novel WCT differentiation model (i.e., VCG Model), (ii) three previously developed WCT differentiation models (i.e., WCT Formula, VT Prediction Model, and WCT Formula II), and (iii) one "all-inclusive" model (i.e., Hybrid Model). A separate validation cohort of paired WCT and baseline ECGs was used to trial and compare each model's performance.

RESULTS

The VCG Model, composed of WCT QRS duration, baseline QRS duration, absolute change in QRS duration, X-lead QRS amplitude change, Y-lead QRS amplitude change, and Z-lead QRS amplitude change, demonstrated effective WCT differentiation (area under the curve [AUC] 0.94) for the derivation cohort. For the validation cohort, the diagnostic performance of the VCG Model (AUC 0.94) was similar to that achieved by the WCT Formula (AUC 0.95), VT Prediction Model (AUC 0.91), WCT Formula II (AUC 0.94), and Hybrid Model (AUC 0.95).

CONCLUSION

Custom calculations derived from mathematically synthesized VCG signals may be used to formulate an effective means to differentiate WCTs automatically.

Singapore Advanced Cardiac Life Support Guidelines 2021

Advanced cardiac life support (ACLS) emphasises the use of advanced airway management and ventilation, circulatory support and the appropriate use of drugs in resuscitation, as well as the identification of reversible causes of cardiac arrest. Extracorporeal cardiopulmonary resuscitation and organ donation, as well as special circumstances including drowning, pulmonary embolism and pregnancy are addressed. Resuscitation does not end with ACLS but must continue in post-resuscitation care. ACLS also covers the recognition and management of unstable pre-arrest tachy- and bradydysrhythmias that may deteriorate further.

Integrating Electrocardiography and Vectorcardiography in the Differential Diagnosis of Wide Complex Tachycardia in a Patient with Left Ventricular Noncompaction: A Case Report and Brief Literature Review

A 69-year-old woman with a history of hypertension and obesity, hospitalized with atypical chest pain, was diagnosed with left ventricular noncompaction. In-hospital monitoring of the cardiac rhythm revealed multiple episodes of atrial tachycardia and one episode of wide complex tachycardia (WCT) with left bundle branch block-like morphology and a right superior QRS axis. The electrocardiographic criteria were suggestive of a supraventricular origin of the WCT. Given the importance of reaching the correct diagnosis when dealing with a WCT, we tried to further define the pattern of ventricular activation using vectorcardiography (VCG). We analyzed the QRS loops during WCT in comparison to a sinus beat, a narrow complex tachycardia beat, and a premature ventricular contraction. The fast initial activation seen in the efferent limb of the QRS loop during the WCT was thought to be reflective of the fast initial activation via the conduction system seen in SVT with aberrancy, which was our final diagnosis for the WCT episode. This case illustrates a novel use of vectorcardiography as an additional diagnostic tool in the differential diagnosis of WCT.

Removing the complexity from wide complex tachycardia

Correctly diagnosing the cause of wide QRS tachycardias remain an area of difficulty for many clinicians. The authors provide a concise update to the different ECG algorithms that have been developed as well as caveats in their application.

Conceptual and literature basis for wide complex tachycardia and baseline ECG comparison

Accurate wide QRS complex tachycardia (WCT) differentiation into either ventricular tachycardia or supraventricular wide complex tachycardia using 12‑lead electrocardiogram (ECG) interpretation is essential for diagnostic, therapeutic, and prognostic reasons. There is an ever-expanding variety of WCT differentiation methods and criteria available to clinicians. However, only a few make use of the diagnostic value of comparing the ECG during WCT to that of the patient's baseline ECG. Therefore, we highlight the conceptual rationale and scientific literature supporting the diagnostic value of WCT and baseline ECG comparison.

Utility of Inferior Lead Q-waveforms in diagnosing Ventricular Tachycardia

Background

Electrocardiogram (ECG) differentiation of wide complex tachycardia (WCT) into ventricular tachycardia (VT) and supraventricular tachycardia with aberration (SVT-A) is often challenging.

Objective

To determine if the presence of Q-waveforms (QS, Qr, QRs) in the inferior leads (II, III, aVF) can differentiate VT from SVT-A in a WCT compared to Brugada algorithm. We studied 2 inferior lead criteria namely QWC-A where all the inferior leads had a similar Q wave pattern and QWC-B where only lead aVF had a Q-waveform.

Methods

A total of 181 consecutive cases of WCT were identified, digitally separated into precordial leads and inferior leads and independently reviewed by 2 electrophysiologists. An electrocardiographic diagnosis of VT or SVT-A was assigned based on Brugada and inferior lead algorithms. Results were compared to the final clinical diagnosis.

Results

VT was the final clinical diagnosis in 24.9% of ECG cohort (45/181); 75.1% (136/181) were SVT-A. QWC-A and QWC-B had a high specificity (93.3% and 82.8%) and accuracy (78.2% and 71.0%), but low sensitivity (33.3% and 35.6%) in differentiating VT from SVT-A. The Brugada algorithm yielded a sensitivity of 82.2% and specificity of 68.4%. Area under the curve in ROC analysis was highest with Brugada algorithm (0.75, 95% CI 0.69-0.81) followed by QWC-A (0.63, 95% CI 0.56-0.70) and QWC-B (0.59, 95% CI 0.52-0.67).

Conclusion

QWC-A and QWC-B criteria had poor sensitivity but high specificity in diagnosing VT in patients presenting with WCT. Further research combining this simple criterion with other newer diagnostic algorithms can potentially improve the accuracy of the overall diagnostic algorithm.

Wide Complex Tachycardia Differentiation: A Reappraisal of the State-of-the-Art

The primary goal of the initial ECG evaluation of every wide complex tachycardia is to determine whether the tachyarrhythmia has a ventricular or supraventricular origin. The answer to this question drives immediate patient care decisions, ensuing clinical workup, and long‐term management strategies. Thus, the importance of arriving at the correct diagnosis cannot be understated and has naturally spurred rigorous research, which has brought forth an ever‐expanding abundance of manually applied and automated methods to differentiate wide complex tachycardias. In this review, we provide an in‐depth analysis of traditional and more contemporary methods to differentiate ventricular tachycardia and supraventricular wide complex tachycardia. In doing so, we: (1) review hallmark wide complex tachycardia differentiation criteria, (2) examine the conceptual and structural design of standard wide complex tachycardia differentiation methods, (3) discuss practical limitations of manually applied ECG interpretation approaches, and (4) highlight recently formulated methods designed to differentiate ventricular tachycardia and supraventricular wide complex tachycardia automatically.