A modified electrocardiographic algorithm for differentiating typical atrioventricular node re-entrant tachycardia from atrioventricular reciprocating tachycardia mediated by concealed accessory pathway

Analysis of ST segment depression in supraventricular tachycardia and its relationship with underlying mechanism

BACKGROUND

Electrocardiographic diagnosis of causes of supraventricular tachycardia (SVT) is sometimes difficult and application of routine algorithms can lead to misdiagnosis in as many as 37 % of patients. ST segment depression may be useful in diagnosing the nature of SVT.

METHODS

We reviewed surface electrocardiogram (ECG) characteristics of 300 patients having SVT with 1:1 AV relationship and correlated findings with electrophysiology study (EPS) findings. Final diagnosis of AVNRT (Atrioventricular nodal reentrant tachycardia), Orthodromic AVRT (atrioventricular reentrant tachycardia) and atrial tachycardia (AT) was correlated with ECG parameters like heart rate, ST segment depressions and QRS morphology.

RESULTS

Out of 300 patients, majority patients included in study, were having AVNRT or AVRT. ST depression predicted AVRT if the ST depression was ≥ 2 mm (overall sensitivity of 38.3 % and specificity of 93.8 % to predict AVRT) and was downsloping in morphology (sensitivity of 36.9 % and specificity of 94.7 % to predict AVRT). At heart rates ≥214 beats per minute (bpm) as measured by 7 small squares of ECG at 25 mm/s, downsloping ST depression ≥2 mm had a sensitivity 37.9 % of and specificity of 89.2 % to predict AVRT. At heart rate <214 bpm, downsloping ST depression ≥2 mm had sensitivity of 37.2 % and specificity of 96.5 % to predict AVRT. Downsloping ST depression of ≥2 mm helps to differentiate AVNRT from AVRT.

CONCLUSION

A downsloping ST segment depression ≥2 mm predicted SVT being an AVRT and can be used as a useful criteria in diagnosing the tachycardia.

Artificial intelligence-enabled electrocardiogram to distinguish atrioventricular re-entrant tachycardia from atrioventricular nodal re-entrant tachycardia

Background

Accurately determining arrhythmia mechanism from a 12-lead electrocardiogram (ECG) of supraventricular tachycardia can be challenging. We hypothesized a convolutional neural network (CNN) can be trained to classify atrioventricular re-entrant tachycardia (AVRT) vs atrioventricular nodal re-entrant tachycardia (AVNRT) from the 12-lead ECG, when using findings from the invasive electrophysiology (EP) study as the gold standard.

Methods

We trained a CNN on data from 124 patients undergoing EP studies with a final diagnosis of AVRT or AVNRT. A total of 4962 5-second 12-lead ECG segments were used for training. Each case was labeled AVRT or AVNRT based on the findings of the EP study. The model performance was evaluated against a hold-out test set of 31 patients and compared to an existing manual algorithm.

Results

The model had an accuracy of 77.4% in distinguishing between AVRT and AVNRT. The area under the receiver operating characteristic curve was 0.80. In comparison, the existing manual algorithm achieved an accuracy of 67.7% on the same test set. Saliency mapping demonstrated the network used the expected sections of the ECGs for diagnoses; these were the QRS complexes that may contain retrograde P waves.

Conclusion

We describe the first neural network trained to differentiate AVRT from AVNRT. Accurate diagnosis of arrhythmia mechanism from a 12-lead ECG could aid preprocedural counseling, consent, and procedure planning. The current accuracy from our neural network is modest but may be improved with a larger training dataset.

Is There a Difference in Tachycardia Cycle Length during SVT in Children with AVRT and AVNRT?

BACKGROUND

There are limited adult data suggesting the tachycardia cycle length (TCL) of atrioventricular reentry tachycardia (AVRT) is shorter than atrioventricular nodal reentry tachycardia (AVNRT), though little data exist in children. We sought to determine if there is a difference in TCL between AVRT and AVNRT in children.

METHODS

A single-center retrospective review of children with supraventricular tachycardia (SVT) from 2000 to 2015 was performed.

INCLUSION CRITERIA

Age ≤ 18 years, invasive electrophysiology study (EPS) confirming AVRT or AVNRT.

EXCLUSION CRITERIA

Atypical AVNRT, congenital heart disease, antiarrhythmic medication use at time of EPS. Data were compared between patients with AVRT and AVNRT via t-test, χ² test, and linear regression.

RESULTS

A total of 835 patients were included (12 ± 4 years, 52 ± 31 kg, TCL 321 ± 55 ms), 539 (65%) with AVRT (270 Wolff-Parkinson-White, 269 concealed pathways) and 296 (35%) with AVNRT. Patients with AVRT were younger (11.7 ± 4.1 years vs 13.0 ± 3.6 years, P < 0.001) and smaller (49 ± 22 kg vs 57 ± 43 kg, P < 0.001). In the baseline state, the TCL was shorter in AVRT than AVRNT (329 ± 51 ms vs 340 ± 60 ms, P = 0.04). In patients requiring isoproterenol to induce SVT, there was no difference in TCL (290 ± 49 ms vs 297 ± 49 ms, P = 0.26). When controlling for age, there was no difference in TCL between AVRT and AVNRT at baseline or on isoproterenol. The regression equation for TCL in the baseline state was TCL = 290 + 4 (age), indicating the TCL will increase by 4 ms above a baseline of 290 ms for each year of life.

CONCLUSIONS

When controlling for age, there is no difference in the TCL between AVRT and AVNRT in children. Age, not tachycardia mechanism, is the most significant factor in predicting TCL.

Clinical value of lead aVR

Lead aVR is the only lead in the surface ECG that does not face the "typically" relevant walls of the left ventricle. Historically, its value has been neglected most likely due to its unusual configuration and direction, which appeared to have little correlation with other more congruous and easily diagnostic frontal leads. The isolation of the unipolar leads in the Standard surface ECG presentation may also have played an important role. Even with this "unfair" neglect, we know nowadays that it is very sensitive to locate obstructed epicardial coronary arteries. Besides helping distinguishing the culprit lesion of an infarct, lead aVR also helps recognizing other conditions that could be of clinical significance such as pericarditis, Brugada syndrome, fascicular blocks of the right branch, ectopic left atrial rhythms, etc. The purpose of this review is to revise the clinical value of lead aVR in the recognition of frequent and not so frequent clinical conditions.

Clinical utility of aVR-The neglected electrocardiographic lead

BACKGROUND

Several studies suggest that electrocardiographers tend to neglect lead aVR during the reading of electrocardiograms (ECGs). Our objective was to provide a systematic review of the most important diagnostic and prognostic uses of lead aVR.

METHODS

We performed a thorough review of the literature about the lead aVR using PubMed, MEDLINE and the archives of the University at Buffalo libraries.

RESULTS

We found that lead aVR may provide important additional information in the diagnosis of coronary artery disease. It may provide a clue to the location of a lesion as well as the possibility of three vessel disease during an acute coronary syndrome. Lead aVR was found useful in the locus of arrhythmias and in differentiation of narrow and wide QRS complex tachycardias. It provides useful prognostic information for patients with the Brugada syndrome and tricyclic antidepressant toxicity. Lead aVR provides alternative criteria for the electrocardiographic diagnosis of left ventricular hypertrophy and left anterior fascicular block.

CONCLUSION

Lead aVR provides very important additional diagnostic and prognostic information in multiple cardiac conditions and can be used either alone or in conjunction with other electrocardiographic leads.