A novel way to prospectively evaluate of AI-enhanced ECG algorithms.

Significant strides will be made in the field of computerized electrocardiology through the development of artificial intelligence (AI)-enhanced ECG (AI-ECG) algorithms. Yet, the scientific discourse has primarily relied upon on retrospective analyses for deriving and externally validating AI-ECG classification algorithms, an approach that fails to fully judge their real-world effectiveness or reveal potential unintended consequences. Prospective trials and analyses of AI-ECG algorithms will be crucial for assessing real-world diagnostic scenarios and understanding their practical utility and degree influence they confer onto clinicians. However, conducting such studies is challenging due to their resource-intensive nature and associated technical and logistical hurdles. To overcome these challenges, we propose an innovative approach to assess AI-ECG algorithms using a virtual testing environment. This strategy can yield critical insights into the practical utility and clinical implications of novel AI-ECG algorithms. Moreover, such an approach can enable an assessment of the influence of AI-ECG algorithms have their users. Herein, we outline a proposed randomized control trial for evaluating the diagnostic efficacy of new AI-ECG algorithm(s) specifically designed to differentiate between wide complex tachycardias into ventricular tachycardia and supraventricular wide complex tachycardia.

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.

A wobble tachycardia supraventricular, ventricular or both?

We present the case of a 77-year-old man with ischemic heart disease and baseline right bundle branch block having a well-tolerated and regular wide complex tachycardia with right bundle branch block morphology. A wide complex tachycardia (WCT) could be of supraventricular or ventricular origin. In this setting, we discuss the differential diagnosis of WCT, the usefulness and limitations of the diagnostic criteria for ventricular tachycardia.

Common ECG interpretation software mistakes Part III: Computer errors that should never be missed.

Electrocardiogram interpretation software mistakes can lead to incorrect diagnoses and inappropriate treatments. Occasionally, the consequences of not recognizing such mistakes are disastrous. This final chapter on software mistakes describes three relatively common computer errors that should never be missed because not recognizing them can result in stroke, cardiac arrest, and even death. In each of the scenarios covered, we describe the clinical background, and provide simple recommendations on how such mistakes can be easily identified and corrected.

Wide complex tachycardia in a patient with non-ischemic cardiomyopathy: What is the diagnosis?

A 55-year-old male presented with acute heart failure and incessant wide complex tachycardia resembling an outflow tract ventricular tachycardia. Meticulous analysis of the electrocardiograms established the diagnosis of pre-excitation with prolonged atrio-ventricular (A-V) conduction over a decrementally conducting accessory A-V pathway. "Linking" between the accessory A-V pathway and normal A-V conduction system resulted in sustained maximal pre-excitation as well as periodic transition to normal A-V conduction without appreciable change in heart rate. Successful radiofrequency ablation of this unusual accessory A-V pathway was performed at the aortic-mitral junction. This ameliorated the mechanical dysynchrony, allowed discontinuation of hemodynamic/inotropic support, and resulted in sustained symptomatic improvement.

Implications of "slower" ventricular tachycardia on clinical management and detection and therapy function of implantable cardioverter defibrillators.

Sophisticated dual-chamber atrioventricular and rate-responsive pacing therapies, cardiac resynchronization therapy (CRT), detection and therapies for ventricular tachycardia and fibrillation (VT/VF) form some major components of multitask functions of current implantable cardioverter defibrillators (ICDs). Appropriate programming of these devices is necessary for them to perform all such tasks precisely. In this report, we describe a case of a patient with Chagas cardiomyopathy with marked cardiomegaly, scarred ventricles, prior epicardial and endocardial ablations and on antiarrhythmic pharmacotherapy for VT, who presented with symptomatic wide complex tachycardia at a slower rate than definition of VT, and in whom programming for detection and therapy for "slow" VT could not be performed due to default technological limitation of the CRT-D.

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.

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.

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 12­lead 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.

The VT Prediction Model: A simplified means to differentiate wide complex tachycardias.

BACKGROUND: The accurate separation of undifferentiated wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) using conventional, manually-applied 12-lead electrocardiogram (ECG) interpretation methods is difficult. PURPOSE: We sought to devise a new WCT differentiation method that operates solely on automated measurements routinely provided by computerized ECG interpretation software. METHODS: In a two-part analysis, we developed and validated a logistic regression model (ie, VT Prediction Model) that utilizes routinely available computerized measurements derived from patients' paired WCT and baseline ECGs. RESULTS: The derivation cohort consisted of 601 paired WCT (273 VT, 328 SWCT) and baseline ECGs from 421 patients. The VT Prediction Model, composed of WCT QRS duration (ms) (P < .0001), QRS duration change (ms) (P < .0001), QRS axis change (°) (P < .0001) and T axis change (°) (P < .0001), yielded effective VT and SWCT differentiation (area under the curve [AUC]: 0.924; confidence interval [CI]: 0.903-0.944) for the derivation cohort. The validation cohort comprised 241 paired WCT (97 VT, 144 SWCT) and baseline ECGs from 177 patients. The VT Prediction Model's implementation on the validation cohort yielded effective WCT differentiation (AUC: 0.900; CI: 0.862-0.939) with overall accuracy, sensitivity, and specificity of 85.0%, 80.4%, and 88.2%, respectively. CONCLUSION: The VT Prediction Model is an example of how readily available ECG measurements may be used to distinguish VT and SWCT effectively. Further study is needed to develop and refine newer WCT differentiation approaches that utilize computerized measurements provided by ECG interpretation software.

Wide complex tachycardia differentiation: An examination of traditional and contemporary approaches.

Despite many technological advances in the field of cardiology, accurate differentiation of wide complex tachycardias into ventricular tachycardia or supraventricular wide complex tachycardia continues to be challenging. After decades of rigorous clinical research, a wide variety of electrocardiographic criteria and algorithms have been developed to provide an accurate means to distinguish these two entities as accurately as possible. Recently, promising automated differentiation methods that utilize computerized electrocardiographic interpretation software have emerged. In this review, we aim to (1) highlight the clinical importance of accurate wide complex tachycardia differentiation, (2) provide an overview of the conventional manually-applied differentiation algorithms, and (3) describe novel automated approaches to differentiate wide complex tachycardia.

Initial evaluation and management of wide-complex tachycardia: A simplified and practical approach.

The evaluation and treatment of wide QRS-complex tachycardia remains a challenge, and mismanagement is quite common. Diagnostic aids such as wide-complex tachycardia algorithms perform poorly in the real-life setting. The purpose of this review is to offer a simple clinical-electrocardiographic approach for the initial evaluation and management of the adult patient with stable wide-complex tachycardia that does not require recollection of complex guidelines or algorithms.

The WCT Formula: A novel algorithm designed to automatically differentiate wide-complex tachycardias.

BACKGROUND: The accurate differentiation of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) remains problematic despite numerous manually-operated electrocardiogram (ECG) interpretation methods. We sought to create a new WCT differentiation method that could be automatically implemented by computerized ECG interpretation (CEI) software. METHODS: In a two-part study, we developed and validated a logistic regression model (i.e. WCT Formula) that utilizes computerized measurements and computations derived from patients' paired WCT and subsequent baseline ECGs. In Part 1, a derivation cohort of paired WCT and baseline ECGs was examined to identify independent VT predictors to be incorporated into the WCT Formula. In Part 2, a separate validation cohort of paired WCT and baseline ECGs was used to prospectively evaluate the WCT Formula's diagnostic performance. RESULTS: The derivation cohort was comprised of 317 paired WCT (157 VT, 160 SWCT) and baseline ECGs. A logistic regression model (i.e. WCT Formula) incorporating WCT QRS duration (ms) (p < 0.001), frontal percent amplitude change (%) (p < 0.001), and horizontal percent amplitude change (%) (p < 0.001) yielded effective WCT differentiation (AUC of 0.96). The validation cohort consisted of 284 paired WCT (116 VT, 168 SWCT) and baseline ECGs. The WCT Formula achieved favorable accuracy (91.5%) with strong sensitivity (89.7%) and specificity (92.9%) for VT. CONCLUSION: The WCT Formula is an example of how contemporary CEI software could be used to successfully differentiate WCTs. The incorporation of similar automated methods into CEI software may improve clinicians' ability to accurately distinguish VT and SWCT.

Novel criterion for the differential diagnosis of wide QRS complexes and wide complex tachycardia using the initial activation of QRS on leads V1 and V2: Differential diagnosis of wide QRS based on V1-V2.

BACKGROUND: Many diagnostic criteria for the differential diagnosis of wide complex tachycardia (WCT) are complex and not completely accurate. Incorrect diagnosis is also related to error in applying criteria. OBJECTIVES: To propose a novel reliable criterion for wide QRS complexes' differential diagnosis. MATERIAL AND METHODS: One hundred Electrocardiograms (ECGs) with wide QRS complexes were analyzed using the ECG software. Five variables were measured during the first 20 ms of QRS in leads V1 and V2 and compared between premature ventricular contraction (PVC) and conducted supraventricular impulse with bundle branch block (BBB) groups. The best discriminant variable was identified. The validity of this variable was tested on a group of 20 patients who had WCT during an electrophysiology study. RESULTS: Almost all variables were statistically different between PVC and BBB groups. The sum of voltages in absolute value of vectors during the initial 20 ms of the QRS in leads V1 and V2 (ΣV1 + V2) was the most discriminant between the two groups (131 ±â€¯85 microvolt [µV] vs. 498 ±â€¯392 µV, p < 0.01). A ΣV1 + V2 < 258 µV (rounded to <0.25 millivolt [mV]) diagnosed PVCs with good sensitivity and specificity (90% and 85% respectively). The ΣV1 + V2 in WCT group had lower values in VT versus supra-ventricular tachycardia (SVT) group (0.53 ±â€¯0.35 mV vs. 1.79 ±â€¯1.04 mV, p = 0.004). CONCLUSIONS: The ΣV1 + V2 < 258 µV is a reliable criterion for PVC diagnosis. It could be measured accurately using ECG Software, which could be programmed to calculate it automatically, limiting the risk of human error. The ΣV1 + V2 also seems capable of discriminating between VT and SVT.

Electrocardiographic manifestations of severe hyperkalemia.

Severe hyperkalemia is a hazardous condition that warrants urgent intervention. In critically ill patients, the electrocardiogram (ECG) can be the most immediately available diagnostic tool in identifying patients with potentially lethal hyperkalemia. Peaking of the T waves, the most widely appreciated ECG sign, is actually rarely a manifestation of life-threatening hyperkalemia. In this review, we provide several clinical-electrocardiographic manifestations that can help identify those patients with hyperkalemia who require prompt intervention.