Identification of Concealed and Manifest Long QT Syndrome Using a Novel T Wave Analysis Program

ST segment and T wave abnormalities: A narrative review

INTRODUCTION

The electrocardiogram (ECG) is a valuable tool for interpreting ventricular repolarization. This article aims to broaden the diagnostic scope beyond the conventional ischemia-centric approach, integrating an understanding of pathophisiological influences on ST-T wave changes.

METHODS

A review was conducted on the physiological underpinnings of ventricular repolarization and the pathophisiological processes that can change ECG patterns. The research encompassed primary repolarization abnormalities due to uniform variations in ventricular action potential, secondary changes from electrical or mechanical alterations, and non-ischemic conditions influencing ST-T segments.

RESULTS

Primary T waves are characterized by symmetrical waves with broad bases and variable QT intervals, indicative of direct myocardial action potential modifications due to ischemia, electrolyte imbalances, and channelopathies. Secondary T waves are asymmetric and often unassociated with significant QT interval changes, suggesting depolarization alterations or changes in cardiac geometry and contractility.

CONCLUSION

We advocate for a unified ECG analysis, recognizing primary and secondary ST-T changes, and their clinical implications. Our proposed analytical framework enhances the clinician's ability to discern a wide array of cardiac conditions, extending diagnostic accuracy beyond myocardial ischemia.

Deep learning-derived 12-lead electrocardiogram-based genotype prediction for hypertrophic cardiomyopathy: a pilot study

Objective: Given the psychosocial and ethical burden, patients with hypertrophic cardiomyopathy (HCMs) could benefit from the establishment of genetic probability prior to the test. This study aimed to develop a simple tool to provide genotype prediction for HCMs.Methods: A convolutional neural network (CNN) was built with the 12-lead electrocardiogram (ECG) of 124 HCMs who underwent genetic testing (GT), externally tested by predicting the genotype on another HCMs cohort (n = 54), and compared with the conventional methods (the Mayo and Toronto score). Using a third cohort of HCMs (n = 76), the role of the network in risk stratification was explored by calculating the sudden cardiac death (SCD) risk scorers (HCM risk-SCD) across the predicted genotypes. Score-CAM was employed to provide a visual explanation of the network.Results: Overall, 80 of 178 HCMs (45%) were genotype-positive. Using the 12-lead ECG as input, the network showed an area under the curve (AUC) of 0.89 (95% CI, 0.83-0.96) on the test set, outperforming the Mayo score (0.69 [95% CI, 0.65-0.78], p < 0.001) and the Toronto score (0.69 [95% CI, 0.64-0.75], p < 0.001). The network classified the third cohort into two groups (predicted genotype-negative vs. predicted genotype-positive). Compared with the former, patients predicted genotype-positive had a significantly higher HCM risk-SCD (0.04 ± 0.03 vs. 0.03 ± 0.02, p <0.01). Visualization indicated that the prediction was heavily influenced by the limb lead.Conclusions: The network demonstrated a promising ability in genotype prediction and risk assessment in HCM.

Decreased vector magnitudes may help identify events in patients with Long QT syndrome

INTRODUCTION

All Long QT syndrome (LQTS) patients are at elevated risk for channelopathy-induced delayed myocardial repolarization and consequently potentially life-threatening cardiac events with 90% of initial cardiac events occurring between preteen and 40 years old. Utilizing ECG and derived vectorcardiographic parameters, including T wave Vector Magnitude (TwVM) measurement data, this study attempts to determine whether TwVM from baseline ECGs is effectively predictive of future cardiac events for genotype-positive LQTS patients.

METHODS

Verified carriers of established LQTS disease-causing genotypes were selected from University of Minnesota patient encounters between 2010 and 2020 for inclusion in this retrospective study. Baseline and predictive ECG and derived vectorcardiographic parameter evaluation, clinical data, and statistical analysis were compared between patients with and patients without cardiac events. First recorded ECG was at presentation to our hospital and final ECG is defined as ECG just prior to cardiac event (event defined below in Methods) or the most final documented ECG before cut-off year of 2020 for the event-free group.

RESULTS

Of 41 participants, 15 experienced cardiac events and 26 did not. While many baseline electrocardiographic parameter measurements did not show significant differences between patient groups, vectorcardiographic parameters at baseline, specifically the QRS vector magnitude (QRSVM) and azimuth of the spatial ventricular gradient, showed significance. Additionally, final vectorcardiographic parameters, particularly the QRSVM, TwVM, and azimuth of the spatial ventricular gradient showed significant differences between patient groups. Final T-wave frontal axis was significantly larger in those without cardiac events. Significant Kaplan-Meier curve separation between patient groups was noted based on a QRSVM of 1.43 mV or lower, with additional consideration to patient age, genotype, and beta blocker use.

CONCLUSION

This study shows evidence of ECG and derived vectorcardiographic parameters, including TwVM, being effective in early prediction of cardiac events in genotype-positive LQTS patients.

Detection of QT interval prolongation using Apple Watch electrocardiogram in children and adolescents with congenital long QT syndrome

Background

Apple watch-derived electrocardiogram (awECG) may help identify prolongation of corrected QT (QTc) interval. This study aimed to determine its usefulness for assessment of prolongation of QTc interval in children and adolescents with long QT syndrome (LQTS).

Methods

Children and adolescents with and without LQTS were recruited for measurement of QTc intervals based on standard 12-lead (sECG) and awECG lead I, II and V5 tracings. Bland-Altman analysis of reproducibility, concordance assessment of T wave morphologies, and receiver operating characteristic (ROC) analysis of sensitivity and specificity of awECG-derived QTc interval for detecting QTc prolongation were performed.

Results

Forty-nine patients, 19 with and 30 without LQTS, aged 3-22 years were studied. The intraclass correlation coefficient was 1.00 for both intra- and inter-observer variability in the measurement of QTc interval. The awECG- and sECG-derived QTc intervals correlated strongly in all three leads (r = 0.90-0.93, all p < 0.001). Concordance between awECG and sECG in assessing T wave morphologies was 84% (16/19). For detection of QTc prolongation, awECG lead V5 had the best specificity (94.4% and 87.5%, respectively) and positive predictive value (87.5% and 80.0%, respectively), and for identification of patients with LQTS, awECG leads II and V5 had the greatest specificity (92.3%-94.1%) and positive predictive value (85.7% to 91.7%) in both males and females.

Conclusions

Apple Watch leads II and V5 tracings can be used for reproducible and accurate measurement of QTc interval, ascertainment of abnormal T wave morphologies, and detection of prolonged QTc interval in children and adolescents with LQTS.

Cardiac Repolarization in Health and Disease

Abnormal cardiac repolarization is at the basis of life-threatening arrhythmias in various congenital and acquired cardiac diseases. Dysfunction of ion channels involved in repolarization at the cellular level are often the underlying cause of the repolarization abnormality. The expression pattern of the gene encoding the affected ion channel dictates its impact on the shape of the T-wave and duration of the QT interval, thereby setting the stage for both the occurrence of the trigger and the substrate for maintenance of the arrhythmia. Here we discuss how research into the genetic and electrophysiological basis of repolarization has provided us with insights into cardiac repolarization in health and disease and how this in turn may provide the basis for future improved patient-specific management.

The diagnostic role of T wave morphology biomarkers in congenital and acquired long QT syndrome: A systematic review

INTRODUCTION

QTc prolongation is key in diagnosing long QT syndrome (LQTS), however 25%-50% with congenital LQTS (cLQTS) demonstrate a normal resting QTc. T wave morphology (TWM) can distinguish cLQTS subtypes but its role in acquired LQTS (aLQTS) is unclear.

METHODS

Electronic databases were searched using the terms "LQTS," "long QT syndrome," "QTc prolongation," "prolonged QT," and "T wave," "T wave morphology," "T wave pattern," "T wave biomarkers." Whole text articles assessing TWM, independent of QTc, were included.

RESULTS

Seventeen studies met criteria. TWM measurements included T-wave amplitude, duration, magnitude, Tpeak-Tend, QTpeak, left and right slope, center of gravity (COG), sigmoidal and polynomial classifiers, repolarizing integral, morphology combination score (MCS) and principal component analysis (PCA); and vectorcardiographic biomarkers. cLQTS were distinguished from controls by sigmoidal and polynomial classifiers, MCS, QTpeak, Tpeak-Tend, left slope; and COG x axis. MCS detected aLQTS more significantly than QTc. Flatness, asymmetry and notching, J-Tpeak; and Tpeak-Tend correlated with QTc in aLQTS. Multichannel block in aLQTS was identified by early repolarization (ERD_30% ) and late repolarization (LRD_30% ), with ERD reflecting hERG-specific blockade. Cardiac events were predicted in cLQTS by T wave flatness, notching, and inversion in leads II and V₅ , left slope in lead V₆ ; and COG last 25% in lead I. T wave right slope in lead I and T-roundness achieved this in aLQTS.

CONCLUSION

Numerous TWM biomarkers which supplement QTc assessment were identified. Their diagnostic capabilities include differentiation of genotypes, identification of concealed LQTS, differentiating aLQTS from cLQTS; and determining multichannel versus hERG channel blockade.

Detection of Patients with Congenital and Often Concealed Long-QT Syndrome by Novel Deep Learning Models

Introduction: The long-QT syndrome (LQTS) is the most common ion channelopathy, typically presenting with a prolonged QT interval and clinical symptoms such as syncope or sudden cardiac death. Patients may present with a concealed phenotype making the diagnosis challenging. Correctly diagnosing at-risk patients is pivotal to starting early preventive treatment. Objective: Identification of congenital and often concealed LQTS by utilizing novel deep learning network architectures, which are specifically designed for multichannel time series and therefore particularly suitable for ECG data. Design and Results: A retrospective artificial intelligence (AI)-based analysis was performed using a 12-lead ECG of genetically confirmed LQTS (n = 124), including 41 patients with a concealed LQTS (33%), and validated against a control cohort (n = 161 of patients) without known LQTS or without QT-prolonging drug treatment but any other cardiovascular disease. The performance of a fully convolutional network (FCN) used in prior studies was compared with a different, novel convolutional neural network model (XceptionTime). We found that the XceptionTime model was able to achieve a higher balanced accuracy score (91.8%) than the associated FCN metric (83.6%), indicating improved prediction possibilities of novel AI architectures. The predictive accuracy prevailed independently of age and QT_c parameters. Conclusions: In this study, the XceptionTime model outperformed the FCN model for LQTS patients with even better results than in prior studies. Even when a patient cohort with cardiovascular comorbidities is used. AI-based ECG analysis is a promising step for correct LQTS patient identification, especially if common diagnostic measures might be misleading.

Safety pharmacology during the COVID pandemic

This editorial summarizes the content of the current themed issue of J Pharmacol Toxicol Methods derived from the 2020 Annual Safety Pharmacology Society (SPS) meeting that was held virtually September 14–17, 2020 due to the ongoing COVID-19 global pandemic. A selection of articles arising from the virtual meeting is summarized. Like previous years they continue to reflect current areas of innovation in SP including new methodologies to predict human safety, best practices for IKr current measurement, and best practice considerations for the conduct of in vivo nonclinical QT studies. The meeting included scientific content from 94 abstracts (reproduced in the current volume of J Pharmacol Toxicol Methods). This continued innovation reflects a rubric in SP that identifies problems, seeks solutions and, importantly, validates the solutions.

Quantitative T-wave morphology assessment from surface ECG is linked with cardiac events risk in genotype-positive KCNH2 mutation carriers with normal QTc values

INTRODUCTION

Long QT syndrome (LQTS) mutation carriers have elevated the risk of cardiac events even in the absence of QTc prolongation; however, mutation penetrance in patients with normal QTc may be reflected in abnormal T-wave shape, particularly in KCNH2 mutation carriers. We aimed to assess whether the magnitude of a three-dimensional T-wave vector (TwVM) will identify KCNH2-mutation carriers with normal QTc at risk for cardiac events.

METHODS

Adult LQT2 patients with QTc < 460 ms in men and <470 ms in women (n = 113, age 42 ± 16 years, 43% male) were compared with genotype-negative family members (n = 1007). The TwVM was calculated using T-wave amplitudes in leads V6, II, and V2 as the square root of (TV6²  + TII²  + (0.5*TV2)² ). Cox regression analysis adjusted for gender and time-dependent beta-blocker use was performed to assess cardiac event (CE) risk, defined as syncope, aborted cardiac arrest, implantable cardioverter-defibrillator therapy, or sudden death.

RESULTS

Dichotomized by median of 0.30 mV, lower TwVM was associated with elevated CE risk compared to those with high TwVM (HR = 2.95, 95% CI, 1.25-6.98, P = .014) and also remained significant after including sex and time-dependent beta-blocker usage in the Cox regression analysis (HR = 2.64, 95% CI, 1.64-4.24, P < .001). However, these associations were found only in women but not in men who had low event rates.

CONCLUSION

T-wave morphology quantified as repolarization vector magnitude using T-wave amplitudes retrieved from standard 12-lead electrocardiogram predicts cardiac events risk in LQT2 women and appears useful for risk stratification of KCNH2-mutation carriers without QTc prolongation.

Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram

Asymptomatic left ventricular dysfunction (ALVD) is present in 3-6% of the general population, is associated with reduced quality of life and longevity, and is treatable when found^1-4. An inexpensive, noninvasive screening tool for ALVD in the doctor's office is not available. We tested the hypothesis that application of artificial intelligence (AI) to the electrocardiogram (ECG), a routine method of measuring the heart's electrical activity, could identify ALVD. Using paired 12-lead ECG and echocardiogram data, including the left ventricular ejection fraction (a measure of contractile function), from 44,959 patients at the Mayo Clinic, we trained a convolutional neural network to identify patients with ventricular dysfunction, defined as ejection fraction ≤35%, using the ECG data alone. When tested on an independent set of 52,870 patients, the network model yielded values for the area under the curve, sensitivity, specificity, and accuracy of 0.93, 86.3%, 85.7%, and 85.7%, respectively. In patients without ventricular dysfunction, those with a positive AI screen were at 4 times the risk (hazard ratio, 4.1; 95% confidence interval, 3.3 to 5.0) of developing future ventricular dysfunction compared with those with a negative screen. Application of AI to the ECG-a ubiquitous, low-cost test-permits the ECG to serve as a powerful screening tool in asymptomatic individuals to identify ALVD.

Noninvasive assessment of dofetilide plasma concentration using a deep learning (neural network) analysis of the surface electrocardiogram: A proof of concept study

Background

Dofetilide is an effective antiarrhythmic medication for rhythm control in atrial fibrillation, but carries a significant risk of pro-arrhythmia and requires meticulous dosing and monitoring. The cornerstone of this monitoring, measurement of the QT/QTc interval, is an imperfect surrogate for plasma concentration, efficacy, and risk of pro-arrhythmic potential.

Objective

The aim of our study was to test the application of a deep learning approach (using a convolutional neural network) to assess morphological changes on the surface ECG (beyond the QT interval) in relation to dofetilide plasma concentrations.

Methods

We obtained publically available serial ECGs and plasma drug concentrations from 42 healthy subjects who received dofetilide or placebo in a placebo‐controlled cross‐over randomized controlled clinical trial. Three replicate 10-s ECGs were extracted at predefined time-points with simultaneous measurement of dofetilide plasma concentration We developed a deep learning algorithm to predict dofetilide plasma concentration in 30 subjects and then tested the model in the remaining 12 subjects. We compared the deep leaning approach to a linear model based only on QTc.

Results

Fourty two healthy subjects (21 females, 21 males) were studied with a mean age of 26.9 ± 5.5 years. A linear model of the QTc correlated reasonably well with dofetilide drug levels (r = 0.64). The best correlation to dofetilide level was achieved with the deep learning model (r = 0.85).

Conclusion

This proof of concept study suggests that artificial intelligence (deep learning/neural network) applied to the surface ECG is superior to analysis of the QT interval alone in predicting plasma dofetilide concentration.

QT Prolongation and Malignant Arrhythmia: How Serious a Problem?

QT prolongation constitutes one of the most frequently encountered electrical disorders of the myocardium. This is due not only to the presence of several associated congenital syndrome but also, and mainly, due to the QT-prolonging effects of several acquired conditions, such as ischaemia and heart failure, as well as multiple medications from widely different categories. Propensity of repolarization disturbances to arrhythmia appears to be inherent in the function of and electrophysiology of the myocardium. In the present review the issue of QT prolongation will be addressed in terms of pathophysiology, arrhythmogenesis, treatment and risk stratification approaches. Although already discussed in literature, it is hoped that the mechanistic approach of the present review will assist in improved understanding of the underlying changes in electrophysiology, as well as the rationale for current diagnostic and therapeutic approaches.