The Bayesian approach improves the electrocardiographic diagnosis of broad complex tachycardia

Interpreting Wide-Complex Tachycardia With the Use of Artificial Intelligence

BACKGROUND

Adopting artificial intelligence (AI) in medicine may improve speed and accuracy in patient diagnosis. We sought to develop an AI algorithm to interpret wide-complex tachycardia (WCT) electrocardiograms (ECGs) and compare its diagnostic accuracy with that of cardiologists.

METHODS

Using 3330 WCT ECGs (2906 supraventricular tachycardia [SVT] and 424 ventricular tachycardia [VT]), we created a training/validation (3131) and a test set (199 ECGs). A convolutional neural network structure using a modification of differentiable architecture search was developed to differentiate between SVT and VT.

RESULTS

The mean accuracy of electrophysiology (EP) cardiologists was 92.5% with sensitivity 91.7%, specificity 93.4%, positive predictive value 93.7%, and negative predictive value 91.7%. Non-EP cardiologists had an accuracy of 73.2 ± 14.4% with sensitivity, specificity, and positive and negative predictive values of 59.8 ± 18.2%, 93.8 ± 3.7%, 93.6 ± 2.3%, and 73.2 ± 14.4%, respectively. AI had superior sensitivity and accuracy (91.9% and 93.0%, respectively) than non-EP cardiologists and similar performance compared with EP cardiologists. Mean time to interpret each ECG varied from 10.1 to 13.8 seconds for EP cardiologists and from 3.1 to 16.6 seconds for non-EP cardiologists. AI required a mean of 0.0092 ± 0.0035 seconds for each ECG interpretation.

CONCLUSIONS

AI appears to diagnose WCT with accuracy superior to non-EP cardiologists and similar to EP cardiologists. Using AI to assist with ECG interpretations may improve patient care.

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.

Validation of a Simple Electrocardiographic Algorithm for Detection of Ventricular Tachycardia

Fundamento

O diagnóstico diferencial de taquicardia de QRS largo, entre taquicardia ventricular (TV) ou taquicardia supraventricular com condução aberrante (TSV-A) é algumas vezes difícil de ser feito na sala de emergência.

Objetivo

Avaliar a acurácia de um algoritmo novo e simples para a detecção de TV no eletrocardiograma (ECG) em pacientes com taquicardia de QRS largo.

Métodos

ECGs de 12 derivações para detecção de taquicardia de QRS largo foram obtidos prospectivamente de 120 pacientes durante estudo eletrofisiológico. Seis médicos com diferentes experiências analisaram os ECGs, e fizeram o diagnóstico com base no algoritmo D12V16, que envolve a análise da polaridade predominante do complexo QRS nas derivações I, II, V1 e V6. O diagnóstico foi comparado com os obtidos pelo algoritmo tradicional de Brugada e pelo estudo eletrofisiológico, o qual é considerado padrão ouro. Adotou-se um nível de significância de 5% (p<0,05) nas análises estatísticas.

Resultados

De acordo com o estudo eletrofisiológico, 82 ECGs eram de TV e 38 de TSV-A. Doenças cardíacas estruturais estavam presentes em 71 (86,6%) dos pacientes com TV e em oito (21,1%) com TSV-A. O algoritmo de Brugada teve uma maior sensibilidade global (87,2%), enquanto o algoritmo D12V16 apresentou maior especificidade global (85,1%) para TV. Tanto o algoritmo D12V16 como o de Brugada apresentou um alto valor preditivo positivo (90,9% vs. 85,8%, respectivamente) e acurácia similar (73,8% vs. 81,4%, respectivamente) para o diagnóstico de TV. Nos avaliadores experientes, a acurácia foi maior utilizando o algoritmo de Brugada que o algoritmo D12V16, mas a acurácia dos dois algoritmos foi similar segundo os avaliadores menos experientes.

Conclusão

O algoritmo simplificado pode ser um método útil para reconhecer TV no ECG, principalmente para médicos menos experientes. (Arq Bras Cardiol. 2021; [online].ahead print, PP.0-0)

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.

Differentiating wide complex tachycardias: A historical perspective

One of the most critical and challenging skills is the distinction of wide complex tachycardias into ventricular tachycardia or supraventricular wide complex tachycardia. Prompt and accurate differentiation of wide complex tachycardias naturally influences short- and long-term management decisions and may directly affect patient outcomes. Currently, there are many useful electrocardiographic criteria and algorithms designed to distinguish ventricular tachycardia and supraventricular wide complex tachycardia accurately; however, no single approach guarantees diagnostic certainty. In this review, we offer an in-depth analysis of available methods to differentiate wide complex tachycardias by retrospectively examining its rich literature base - one that spans several decades.

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.

[Automatic detection of ventricular and supraventricular wide QRS arrhythmias using complex of morphological criteria and algorithms]

AIM

The aim of study is a detection of ventricular and supraventricular wide QRS arrhythmias using complex of morphological criteria and algorithms by method of automatic analysis.

MATERIALS AND METHODS

For 100 patients (m/f - 61/39, Me (min; max) - 44.5 (10; 85) years) of researched group the analysis of 14306 single wide ectopic complexes (QRS 120-230 ms) has been done. Wide complexes include 11028 (77%) ventricular complexes and 3278 (23%) supraventricular complexes represented by 145 different forms of QRS. For verification of arrhythmias origin transesophageal ECG recording and endocardial electrophysiological study were done. The control group included 59 patients (m/f - 25/34, Me (min; max) - 49.5 (14,85) years) with 720 wide QRS, including 467 (65%) ventricular and 253 (35%) supraventricular complexes represented by 86 forms of QRS. The criteria Drew B.J., Scheinman M.M. (1995); Wellens H.J. (1978), RWPT II (Pava LF, 2010) and the algorithms of Brugada P. (1991); Bayesian (2000); Vereckei A. (2008) were used to evaluate sensitivity, specificity and diagnostic accuracy of wide QRS complexes recognition one by one and together, using the method of Wald sequential automatic analysis (KT Result3, CJSC INCART, Russia) and method of artificial neural networks.

RESULTS

The best results for the detection of ventricular arrhythmias algorithms were demonstrated by the  Brugada  P., Drew  B.J., Scheinman  M.M. algorithm (sensitivity 86.43%, specificity 66.73%, diagnostic accuracy 82.14% in the study group, sensitivity 81.80%, specificity 73.12%, diagnostic accuracy 78.75% in the control group), and the Bayesian algorithm (sensitivity 87.81%, specificity 73.62%, diagnostic accuracy 84.72% in the study group, sensitivity 83.30%, specificity 77.08%, diagnostic accuracy 81.11% in the control group). A complex analysis of the Wald method recognized ventricular arrhythmias in the research group with sensitivity 83.11%, specificity 83.65%, diagnostic accuracy 83.23% and in the control group with a sensitivity 83.51%, specificity of 84.58% and diagnostic accuracy 83.89%. Artificial neural networks recognized ventricular arrhythmias with sensitivity 91.43%, specificity 91.30% and diagnostic accuracy 91.39% in the control group and with sensitivity 97.06%, specificity 99.39% and diagnostic accuracy 97.6% in the research group.

CONCLUSION

Automatic analysis allows obtaining simultaneously the results of each algorithms/criteria and in combination. It significantly reduces the doctor's work in assessing of amplitude-time characteristics of the complexes. Using artificial neural networks increases the accuracy of of ventricular and supraventricular arrhythmias recognition.

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.

Wide complex tachycardia discrimination using dynamic time warping of ECG beats

BACKGROUND AND OBJECTIVE

Automatic processing and accurate diagnosis of wide complex tachycardia (WCT) arrhythmia groups using electrocardiogram signals (ECG) remains a challenge. WCT arrhythmia consists of two main groups: ventricular tachycardia (VT) and supraventricular tachycardia with aberrancy (SVT-A). These two groups have similar morphologies in the realm of ECG signals. VT and SVT-A arrhythmias originate from the ventricle and atrium, respectively. Hence, inaccurate diagnosis of SVT-A instead of VT can be fatal.

METHODS

In this paper, we present a novel algorithm using dynamic time warping (DTW) to discriminate between VT and SVT-A arrhythmias. This method includes pre-processing, best template search (BTS), and classifier modules. The first module, pre-processing, is responsible for filtering, R-wave detection, and beat detection of ECG signals. The second module, BTS, automatically extracts the minimum possible number of signals as a template from the entire training dataset using an intelligent algorithm. These template signals have the greatest morphological difference, which leads to accurate WCT discrimination. Finally, a 1NN classifier categorizes the test data using DTW distance.

RESULTS

Our proposed method was evaluated on an ECG signal database consisting of 171 subjects. The results showed that the proposed algorithm can accurately discriminate between VT, SVT-A, and normal subjects, and appears to be suitable for future use in clinical application. The obtained accuracy, sensitivity, specificity, and positive predictive values were 93.22%, 88.68%, 96.98%, and 90.27%, respectively.

CONCLUSION

The presented diagnostic method for discriminating VT and SVT-A, using only one ECG lead, is suitable for future clinical use. It can reduce needless therapeutic interventions and minimize risk for patients.

[Ventricular arrhythmias : What has been confirmed in therapy?]

Ventricular arrhythmias include a wide range of potentially benign single ventricular premature contractions to ventricular tachycardia and ventricular fibrillation with a risk for sudden cardiac death. The diagnosis of ventricular arrhythmia is made by 12-lead electrocardiogram, 24 h Holter monitoring, an external or implantable loop recorder, or during in-hospital monitoring. Especially the diagnosis of wide complex tachycardias is challenging in terms of differentiating between ventricular tachycardia and supraventricular tachycardia with aberrant atrioventricular conduction. After documentation of ventricular arrhythmias, diagnostic work-up with respect to structural or electrical cardiomyopathy is mandatory followed by risk stratification for sudden cardiac death. Therapeutic options for treatment of ventricular arrhythmias range from pharmacological therapy and interventional procedures such as catheter ablation and implantable devices. The current article provides an overview of the diagnosis of ventricular tachycardia and underlying cardiomyopathies. Furthermore, medical and interventional therapies are described. In addition, the indications for implantable and wearable defibrillators are presented.

Machine Learning Approaches in Cardiovascular Imaging

Cardiovascular imaging technologies continue to increase in their capacity to capture and store large quantities of data. Modern computational methods, developed in the field of machine learning, offer new approaches to leveraging the growing volume of imaging data available for analyses. Machine learning methods can now address data-related problems ranging from simple analytic queries of existing measurement data to the more complex challenges involved in analyzing raw images. To date, machine learning has been used in 2 broad and highly interconnected areas: automation of tasks that might otherwise be performed by a human and generation of clinically important new knowledge. Most cardiovascular imaging studies have focused on task-oriented problems, but more studies involving algorithms aimed at generating new clinical insights are emerging. Continued expansion in the size and dimensionality of cardiovascular imaging databases is driving strong interest in applying powerful deep learning methods, in particular, to analyze these data. Overall, the most effective approaches will require an investment in the resources needed to appropriately prepare such large data sets for analyses. Notwithstanding current technical and logistical challenges, machine learning and especially deep learning methods have much to offer and will substantially impact the future practice and science of cardiovascular imaging.

Specificity of wide QRS complex tachycardia criteria and algorithms in patients with ventricular preexcitation

BACKGROUND

Despite substantial progress in the field of differentiation between ventricular tachycardia (VT) and supraventricular tachycardia (SVT) with wide QRS complexes, differentiation between VT and preexcited SVT remains largely unresolved due to significant overlap in QRS morphology. Our aim was to assess the specificities of various single ECG criteria and sets of criteria (Brugada algorithm, aVR algorithm, Steurer algorithm, and the VT score) for diagnosis of VT in a sizable cohort of patients with preexcitation.

METHODS

We performed a retrospective study of consecutive accessory pathway ablation procedures to identify preexcited tachycardias. Among 670 accessory pathway ablation procedures, 329 cases with good quality ECG with either bona fide preexcited SVT (n = 30) or a surrogate preexcited SVT (fast paced atrial rhythm with full preexcitation, n = 299) were identified. ECGs were analyzed with the use of wide QRS complex algorithms/criteria to determine specificities of these methods.

RESULTS

The Steurer algorithm and VT score (≥3 points), with specificities of 97.6% and 96.1%, respectively, were significantly (p < .01) more specific for the diagnosis of VT than Brugada algorithm, aVR algorithm, and Pava criterion with specificities of 31%, 11.6%, and 57.1%, respectively. The first step of the Brugada algorithm and the first step of the aVR algorithm had also high specificities of 93.3% and 96.0%, respectively.

CONCLUSION

There are sufficient electrocardiographical differences between VT and preexcited SVT to allow electrocardiographic differentiation. VT score, Steurer algorithm, and some single criteria do not overdiagnose VT in patients with preexcitation.

Ventricular tachycardia score - A novel method for wide QRS complex tachycardia differentiation - Explained

Philosophy, merits and limitations of a novel method for wide QRS complex tachycardia differentiation, based on a scoring system and called the ventricular tachycardia (VT) score, were explained. The following criteria were assigned one point: initial dominant R wave in V1; initial r>40 ms in V1 or V2; notched S in V1; initial R wave in aVR; lead II RWPT≥50 ms; and absence of an RS in leads V1-V6. Atrioventricular dissociation (including fusion/capture beats and partial dissociation) was assigned two points. We recommend ≥3 VT score points for a firm diagnosis of VT. A cut-off ≥1 point can be used for diagnosis of VT when highest overall accuracy rather than error-free diagnosis is desired. However, in case of VT score of 0-2 (i.e., not fully diagnostic ECG), we recommend using other options (electrophysiological study, clinical data, previous and following ECGs, etc.) for confirming the diagnosis.

Wide Complex Tachycardia - Ventricular Tachycardia or Not Ventricular Tachycardia, That Remains the Question

Arriving at the correct diagnosis in cases of wide complex tachycardia remains problematic for many clinicians. In this paper, we review the historical development of criteria used to differentiate among the major diagnostic possibilities and compare the strengths and weaknesses of various differentiating algorithms.

Current algorithms for the diagnosis of wide QRS complex tachycardias

The differential diagnosis of a regular, monomorphic wide QRS complex tachycardia (WCT) mechanism represents a great diagnostic dilemma commonly encountered by the practicing physician, which has important implications for acute arrhythmia management, further work-up, prognosis and chronic management as well. This comprehensive review discusses the causes and differential diagnosis of WCT, and since the ECG remains the cornerstone of WCT differential diagnosis, focuses on the application and diagnostic value of different ECG criteria and algorithms in this setting and also provides a practical clinical approach to patients with WCTs.

Comparison of the "real-life" diagnostic value of two recently published electrocardiogram methods for the differential diagnosis of wide QRS complex tachycardias

OBJECTIVES

The diagnostic values of the aVR lead or "Vereckei algorithm," and the lead II R-wave peak time (RWPT) criterion, recently devised for the differential diagnosis of wide QRS complex tachycardias (WCTs), were compared.

METHODS

A total of 212 WCTs (142 ventricular tachycardias [VTs], 62 supraventricular tachycardias [SVT], and eight preexcitation SVTs) from 145 patients with proven electrophysiologic diagnoses were retrospectively analyzed by seven examiners blinded to the electrophysiologic diagnoses.

RESULTS

The overall test accuracy of the Vereckei algorithm was superior to that of the RWPT criterion (84.3% vs. 79.6%; p = 0.0003). The sensitivity of the Vereckei algorithm for VT diagnosis was greater than that of RWPT criterion (92.4% vs. 79.1%; p < 0.0001). The negative predictive value (NPV) for the Vereckei algorithm was also greater (77.8%; 95% confidence interval [CI] = 73.6% to 82.1%) than that of the RWPT criterion (61.6%; 95% CI = 57.6% to 65.6%). The specificity of the Vereckei algorithm was lower than that of the RWPT criterion (64.7% vs. 80.9%; p < 0.0001). The positive predictive value (PPV) was also lower for the Vereckei algorithm (86.4%; 95% CI = 84.4% to 88.4%) than for the RWPT criterion (90.9%; 95% CI = 89.1% to 92.8%). Incorrect diagnoses made by the Vereckei algorithm were mainly due to misdiagnosis of SVT as VT (65.7% of cases), and those made by the RWPT criterion were due to the more dangerous misdiagnosis of VT as SVT (72.5% of cases).

CONCLUSIONS

The Vereckei algorithm was superior in overall test accuracy, sensitivity, and NPV for VT diagnosis and inferior in specificity and PPV to the RWPT criterion. All of these parameters were lower in "real life" than those reported by the original authors for each of the particular electrocardiographic methods.

Critical cardiovascular skills and procedures in the emergency department

The management of cardiovascular emergencies is a fundamental component of the practice of an emergency practitioner. Delays in the evaluations and management can lead to significant morbidity or mortality. It is of vital importance to be familiar with procedures such as pericardiocentesis, cardioversion, defibrillation, temporary pacing, and options for the management of tachyarrhythmias. This article discusses the most common cardiovascular procedures encountered in an emergency setting, including the indications, contraindications, equipment, technique, and complications for each procedure.

Specificity of the wide QRS complex tachycardia algorithms in recipients of cardiac resynchronization therapy

BACKGROUND

We assessed the specificity of wide QRS complex tachycardia (WCT) differentiating algorithms in patients with preexistent left bundle branch block (LBBB) and heart failure.

METHODS

Three hundred fourteen patients with resynchronization devices were retrospectively screened. electrocardiograms with supraventricular LBBB rhythm were used as a surrogate for supraventricular tachycardia QRS morphology. The Pava lead II criterion, ventricular activation velocity ratio (Vi/Vt) ratio in V(2), Vereckei aVR, Brugada, Griffith, and Bayesian algorithms were investigated.

RESULTS

The WCT algorithms had a lower specificity (33%-69%) in patients with LBBB than in general WCT populations. The Pava lead II criterion and Brugada algorithm had higher specificity than other algorithms (P < .05). Several of the single criteria (absence of an RS complex in V(1) through V(6), initial R wave in aVR, Vi/Vt < 1 in V(2)) had specificities of 92% to 99%.

CONCLUSIONS

In patients with heart failure and LBBB, an electrocardiographic diagnosis of ventricular tachycardia should be based on selected, specific criteria rather than on WCT algorithms.

Bayesian quantitative electrophysiology and its multiple applications in bioengineering

Bayesian interpretation of observations began in the early 1700s, and scientific electrophysiology began in the late 1700s. For two centuries these two fields developed mostly separately. In part that was because quantitative Bayesian interpretation, in principle a powerful method of relating measurements to their underlying sources, often required too many steps to be feasible with hand calculation in real applications. As computer power became widespread in the later 1900s, Bayesian models and interpretation moved rapidly but unevenly from the domain of mathematical statistics into applications. Use of Bayesian models now is growing rapidly in electrophysiology. Bayesian models are well suited to the electrophysiological environment, allowing a direct and natural way to express what is known (and unknown) and to evaluate which one of many alternatives is most likely the source of the observations, and the closely related receiver operating characteristic (ROC) curve is a powerful tool in making decisions. Yet, in general, many people would ask what such models are for, in electrophysiology, and what particular advantages such models provide. So to examine this question in particular, this review identifies a number of electrophysiological papers in bioengineering arising from questions in several organ systems to see where Bayesian electrophysiological models or ROC curves were important to the results that were achieved.

Atrioventricular delay optimization by doppler-derived left ventricular dP/dt improves 6-month outcome of resynchronized patients

BACKGROUND

Atrioventricular (AV) interval optimization, ensuring the best filling and the abolishment of presystolic mitral regurgitation, is crucial for the efficacy of cardiac resynchronization therapy (CRT). The methods proposed to optimize AV delay have many limitations. The maximum left ventricular pressure derivative (LV dP/dt)--an index of cardiac performance--could provide a clue for AV optimization. DP/dt can be calculated by the Doppler curve of mitral regurgitation jet and it is related to micromanometer-derived dP/dt.

AIM

The aim of this study was to assess whether optimal AV delay, defined as the highest noninvasive dP/dt, may provide clinical and functional benefits in CRT patients.

METHODS

Of 41 consecutive patients, 23 echo Doppler recordings were obtained at AV delays of 60, 80, 100, 120, 140, 160, 180 ms (Group I). Three patients were discarded because of suboptimal Doppler signal. In 15 patients an empiric AV delay of 120 ms was chosen (Group II). Both groups were programmed to atriosynchronous pacing mode and synchronous VV stimulation.

RESULTS

In Group I optimal AV delay was 60 ms in one patient, 80 ms in 6, 100 in 6, 120 in 8, 140 in 2. At 6 months follow-up, Group I showed a significantly lower NYHA class (2.1 +/- 0.1 vs 3 +/- 0.2 P < 0.01) and higher LV ejection fraction (LVEF): 32.1 + 1 versus 27.5 +/- 1.6% (P < 0.05) as compared to Group II.

CONCLUSIONS

Doppler-derived dP/dt for AV delay optimization determines better functional class and LVEF at 6 months follow-up relative to an empiric AV delay program.

The deleterious consequences of right ventricular apical pacing: time to seek alternate site pacing

BACKGROUND

The purpose of this article is to critically review the data accumulated to date from studies evaluating the hemodynamic and clinical effects of right ventricular apical pacing during conventional permanent cardiac pacing. The data from studies comparing the effects of right ventricular apical pacing and alternate site ventricular pacing are also reviewed.

METHODS

We conducted a MEDLINE and journal search of English-language reports published in the last decade and searched relevant papers.

RESULTS

Although intraventricular conduction delay in the form of left bundle branch block (LBBB) has traditionally been viewed as an electrophysiologic abnormality, it has now become abundantly clear that it has profound hemodynamic effects due to ventricular dyssynchrony, especially in patients with heart failure. These deleterious effects can be significantly ameliorated by cardiac resynchronization therapy effected by biventricular or left ventricular pacing. However, not only is spontaneous LBBB harmful, but the iatrogenic variety produced by right ventricular apical pacing in patients with permanent pacemakers may be equally deleterious. In this review new evidence from recent studies is presented, which strongly suggests a harmful effect of our long-standing practice of producing an iatrogenic LBBB by conventional right ventricular apical pacing in patients receiving permanent pacemakers. This emerging strong new evidence about the adverse hemodynamic and clinical effects of right ventricular apical pacing would dictate a reassessment of our traditional approach to permanent cardiac pacing and direct our attention to alternate sites of pacing, such as the left ventricle and/or the right ventricular outflow tract or septum, if not for all patients, at least for those with left ventricular dysfunction. Indeed, current convincing data on alternate site ventricular pacing are encouraging and this approach should be actively pursued and further investigated in future studies.

CONCLUSIONS

Not only is spontaneous permanent LBBB harmful to our patients, but the iatrogenic variety produced by right ventricular apical pacing during conventional permanent pacing may also be deleterious to some patients. The compelling evidence presented herein cannot be ignored; it may dictate a change of attitude toward right ventricular apical pacing directing our attention to alternate sites of ventricular pacing and avoidance of the right ventricular apex.

Blood oxygen saturation during atrio-ventricular dissociation with wide-QRS complex tachycardias

Amplitude ratios of two adjacent mixed blood oxygen saturation curve peaks in randomly selected strips of ECG were analysed in 32 patients with regular wide-QRS complex tachycardias and 60 control subjects where pacing was performed before ablation of narrow-QRS complex tachycardias. Patients with VT showed different patterns of A-to-V relationships, leading to varying ratios of SpO2 adjacent peak amplitudes, in contrast to patients with aberrant SVTs and 1:1 AV association. Application of a criterion of adjacent SpO2 peaks differing by twofold or more had a sensitivity of 90.0% and specificity of 83.3% to detect AV dissociation during VT. Fluctuations on the mixed oxygen saturation curve may be a useful non-invasive clinical parameter to detect different ventricular filling caused by atrio-ventricular dissociation during VT.

Comparison of Efficacy of Reverse Remodeling and Clinical Improvement for Relatively Narrow and Wide QRS Complexes After Cardiac Resynchronization Therapy for Heart Failure

INTRODUCTION

Cardiac resynchronization therapy (CRT) has been shown to reverse left ventricular (LV) remodeling and improve symptoms in heart failure patients with wide QRS complexes; however, its role in patients with mildly prolonged QRS complexes is unclear. This study investigated if CRT benefited patients with mildly prolonged QRS complexes >120 to 150 ms and explored if the severity of systolic asynchrony determined such a response.

METHODS AND RESULTS

Fifty-eight patients (age 66 +/- 11 years, 66% male) who had undergone CRT were studied prospectively. Of these patients, 27 had QRS duration between 120 and 150 ms (group A), and 31 had QRS duration >150 ms (group B). Tissue Doppler echocardiography and clinical assessment were performed at baseline and 3 months after CRT. Both groups had significant reduction of LV volume and increased ejection fraction, +dP/dt, and sphericity index (all P < 0.05). These improvements were greater in group B and were explained by the higher prevalence of systolic intraventricular asynchrony. Significant reverse remodeling (reduction of LV end-systolic volume >15%) was evident in 46% of group A patients and 68% of group B patients. Improvement in clinical endpoints was observed in both groups (all P < 0.01), although the changes in metabolic equivalent and New York Heart Association functional class were greater in group B. In both groups, systolic asynchrony index (TS-SD) was the most important predictor of reverse remodeling (r =-0.78, P < 0.001) and was the only independent predictor in the multivariate model (beta=-1.80, confidence interval =-2.18 to -1.42, P < 0.001); QRS duration was not. A predefined TS-SD value >32.6 ms had a sensitivity of 94% and specificity of 83% to predict reverse remodeling. Improvement of intraventricular asynchrony after CRT was evident only in responders (P = 0.01).

CONCLUSION

Improvement of LV remodeling and clinical status is evident after CRT in heart failure patients with QRS duration >120 to 150 ms. These responders are closely predicted by the severity of prepacing intraventricular asynchrony but not QRS duration.