T-wave morphology abnormalities in benign, potent, and arrhythmogenic I(kr) inhibition

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 (ERD30% ) and late repolarization (LRD30% ), 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.

Effect of hyperglycaemia in combination with moxifloxacin on cardiac repolarization in male and female patients with type I diabetes

Background

Patients with Type 1 diabetes mellitus have been shown to be at a two to ten-fold higher risk of sudden cardiac death (SCD) (Svane et al., Curr Cardiol 2020; 22:112) than the general population, but the underlying mechanism is unclear. Hyperglycaemia is a recognised cause of QTc prolongation; a state patients with type 1 diabetes are more prone to, potentially increasing their risk of ventricular arrhythmia. Understanding the QTc prolongation effect of both hyperglycaemia and the concomitant additive risk of commonly prescribed QTc-prolonging drugs such as Moxifloxacin may help to elucidate the mechanism of sudden cardiac death in this cohort. This single-blinded, placebo-controlled study investigated the extent to which hyperglycaemia prolongs the QTc in controlled conditions, and the potential additive risk of QTc-prolonging medications.

Methods

21 patients with type 1 diabetes mellitus were enrolled to a placebo-controlled crossover study at a single clinical trials unit. Patients underwent thorough QTc assessment throughout the study. A ‘hyperglycaemic clamp’ of oral and intravenous glucose was administered with a target blood glucose of > 25 mM and maintained for 2 h on day 1 and day 3, alongside placebo on day 1 and moxifloxacin on day 3. Day 2 served as a control day between the two active treatment days.

Thorough QTc assessment was conducted at matched time points over 3 days, and regular blood sampling was undertaken at matched time intervals for glucose levels and moxifloxacin exposure.

Results

Concentration-effect modelling showed that acute hyperglycaemia prolonged the QTc interval in female and male volunteers with type 1 diabetes by a peak mean increase of 13 ms at 2 h. Peak mean QTc intervals after the administration of intravenous Moxifloxacin during the hyperglycaemic state were increased by a further 9 ms at 2 h, to 22 ms across the entire study population. Regression analysis suggested this additional increase was additive, not exponential.

Hyperglycaemia was associated with a significantly greater mean QTc-prolonging effect in females, but the mean peak increase with the addition of moxifloxacin was the same for males and females. This apparent sex difference was likely due to the exclusive use of basal insulin in the male patients, which provided a low level of exogenous insulin during the study assessments thereby mitigating the effects of hyperglycaemia on QTc. This effect was partially overcome by Moxifloxacin administration, suggesting both hyperglycaemia and moxifloxacin prolong QTc by different mechanisms, based on subinterval analysis.

Conclusions

Hyperglycaemia was found to be a significant cause of QTc prolongation and the additional effect of a QTc-prolonging positive control (moxifloxacin) was found to be additive. Given the high risk of sudden cardiac death in type 1 diabetes mellitus, extra caution should be exercised when prescribing any medication in this cohort for QTc effects, and further research needs to be undertaken to elucidate the exact mechanism underlying this finding and explore the potential prescribing risk in diabetes.

Trial Registration

NCT number: NCT01984827.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s00392-022-02037-8.

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.

Meta-analysis of Tpeak-Tend and Tpeak-Tend/QT ratio for risk stratification in congenital long QT syndrome

BACKGROUND AND OBJECTIVES

Congenital long QT syndrome (LQTS) predisposes affected individuals to ventricular tachycardia/fibrillation (VF/VF), potentially resulting in sudden cardiac death. The T_peak-T_end interval and the T_peak-T_end/QT ratio, electrocardiographic markers of dispersion of ventricular repolarization, were proposed for risk stratification but their predictive values in LQTS have been controversial. A systematic review and meta-analysis was conducted to examine the value of T_peak-T_end intervals and T_peak-T_end/QT ratios in predicting arrhythmic and mortality outcomes in congenital LQTS.

METHOD

PubMed and Embase databases were searched until 9th May 2017, identifying 199 studies.

RESULTS

Five studies on long QT syndrome were included in the final meta-analysis. T_peak-T_end intervals were longer (mean difference [MD]: 13ms, standard error [SE]: 4ms, P=0.002; I²=34%) in congenital LQTS patients with adverse events [syncope, ventricular arrhythmias or sudden cardiac death] compared to LQTS patients without such events. By contrast, T_peak-T_end/QT ratios were not significantly different between the two groups (MD: 0.02, SE: 0.02, P=0.26; I²=0%).

CONCLUSION

This meta-analysis showed that T_peak-T_end interval is significant higher in individuals who are at elevated risk of adverse events in congenital LQTS, offering incremental value for risk stratification.

Analysis of 24-h Rhythm in Ventricular Repolarization Identifies QT Diurnality As a Novel Clinical Parameter Associated with Previous Ventricular Arrhythmias in Heart Failure Patients

Introduction: Cardiac repolarization abnormalities are among the major causes of ventricular arrhythmias and sudden cardiac death. In humans, cardiac repolarization duration has a 24-h rhythm. Animal studies show that this rhythm is regulated by 24-h rhythms in ion channel function and that disruption of this rhythm leads to ventricular arrhythmias. We hypothesized that 24-h rhythms in QT duration can be used as a predictor for sudden cardiac death and are associated with ventricular arrhythmias. Secondly, we assessed a possible mechanistic explanation by studying the putative role of hERG channel dysfunction.

Materials and Methods: In 2 retrospective studies, measures of the 24-h variation in the QT and QTc intervals (QT and QTc diurnality, QTd and QTcd, respectively) have been derived from Holter analyses and compared between groups: 1) 39 post-infarct patients with systolic heart failure (CHF: EF < 35%), of which 14 with, and 25 without a history of ventricular arrhythmias and 2) five patients with proven (LQTS2) and 16 with potential (Sotalol-induced) hERG channel dysfunction vs. 22 controls.

Results: QTd was two-fold higher in CHF patients with a history of ventricular arrhythmias (38 ± 15 ms) compared to CHF patients without VT (16 ± 9 ms, p = 0.001). QTd was significantly increased in LQT2 patients (43 ± 24 ms) or those treated with Sotalol (30 ± 10 ms) compared to controls (21 ± 8 ms, p < 0.05 for both).

Discussion: QT diurnality presents a novel clinical parameter of repolarization that can be derived from Holter registrations and may be useful for identification of patients at risk for ventricular arrhythmias.

GENomE wide analysis of sotalol-induced IKr inhibition during ventricular REPOLarization, "GENEREPOL study": Lack of common variants with large effect sizes

Many drugs used for non-cardiovascular and cardiovascular purposes, such as sotalol, have the side effect of prolonging cardiac repolarization, which can trigger life-threatening cardiac arrhythmias by inhibiting the potassium-channel IKr (KCNH2). On the electrocardiogram (ECG), IKr inhibition induces an increase in QTc and Tpeak-Tend (TpTe) interval and a decrease of T wave maximal amplitude (TAmp). These changes vary markedly between subjects, suggesting the existence of predisposing genetic factors. 990 healthy individuals, prospectively challenged with an oral 80mg sotalol dose, were monitored for changes in ventricular repolarization on ECG between baseline and 3 hours post dosing. QTc and TpTe increased by 5.5±3.5% and 15±19.6%, respectively, and TAmp decreased by 13.2±15.5%. A principal-component analysis derived from the latter ECG changes was performed. A random subsample of 489 individuals were subjected to a genome-wide-association analysis where 8,306,856 imputed single nucleotide polymorphisms (SNPs) were tested for association with QTc, TpTe and TAmp modulations, as well their derived principal-components, to search for common genetic variants associated with sotalol-induced IKr inhibition. None of the studied SNPs reached the statistical threshold for genome-wide significance. This study supports the lack of common variants with larger effect sizes than one would expect based on previous ECG genome-wide-association studies.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov NCT00773201.

Automated T-wave analysis can differentiate acquired QT prolongation from congenital long QT syndrome

BACKGROUND

Prolongation of the QT on the surface electrocardiogram can be due to either genetic or acquired causes. Distinguishing congenital long QT syndrome (LQTS) from acquired QT prolongation has important prognostic and management implications. We aimed to investigate if quantitative T-wave analysis could provide a tool for the physician to differentiate between congenital and acquired QT prolongation.

METHODS

Patients were identified through an institution-wide computer-based QT screening system which alerts the physician if the QTc ≥ 500 ms. ECGs were retrospectively analyzed with an automated T-wave analysis program. Congenital LQTS was compared in a 1:3 ratio to those with an identified acquired etiology for QT prolongation (electrolyte abnormality and/or prescription of known QT prolongation medications). Linear discriminant analysis was performed using 10-fold cross-validation to statistically test the selected features.

RESULTS

The 12-lead ECG of 38 patients with congenital LQTS and 114 patients with drug-induced and/or electrolyte-mediated QT prolongation were analyzed. In lead V₅ , patients with acquired QT prolongation had a shallower T wave right slope (-2,322 vs. -3,593 mV/s), greater T-peak-Tend interval (109 vs. 92 ms), and smaller T wave center of gravity on the x axis (290 ms vs. 310 ms; p < .001). These features could distinguish congenital from acquired causes in 77% of cases (sensitivity 90%, specificity 58%).

CONCLUSION

T-wave morphological analysis on lead V₅ of the surface ECG could successfully differentiate congenital from acquired causes of QT prolongation.

Electrocardiographic Predictors of Torsadogenic Risk During Dofetilide or Sotalol Initiation: Utility of a Novel T Wave Analysis Program

INTRODUCTION

Initiation of class III anti-arrhythmic medications requires telemetric monitoring for ventricular arrhythmias and QT prolongation to reduce the risk of torsades de pointes (TdP). Heart rate-corrected QT interval (QTc) is an indicator of risk, however it is imperfect, and subtle abnormalities of repolarization have been linked with arrhythmogenesis.

PURPOSE

Identification of electrocardiographic predictors of torsadogenic risk through the application of a novel T wave analysis tool.

METHODS

Among all patients admitted to Mayo Clinic for initiation of dofetilide or sotalol, we identified 13 cases who developed drug-induced TdP and 26 age and sex matched controls that did not develop TdP. The immediate pre-TdP ECG of those with TdP was compared to the last ECG performed prior to hospital discharge in controls using a novel T wave program that quantified subtle changes in T wave morphology.

RESULTS

The QTc and 12 T wave parameters successfully distinguished TdP cases from controls. The top performing parameters were the QTc in lead V3 (mean case vs control 480 vs 420 msec, p < 0.001, r = 0.72) and T wave right slope in lead I (mean case vs control -840.29 vs -1668.71 mV/s, p = 0.002, r = 0.45). The addition of T wave right slope to QTc improved prediction accuracy from 79 to 88 %.

CONCLUSION

Our data demonstrate that, in addition to QTc, the T wave right slope is correlated strongly with TdP risk. This suggests that a computer-based repolarization measurement tool that integrates additional data beyond the QTc may identify patients with the greatest torsadogenic potential.

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

Background—

Congenital long QT syndrome (LQTS) is characterized by QT prolongation. However, the QT interval itself is insufficient for diagnosis, unless the corrected QT interval is repeatedly ≥500 ms without an acquired explanation. Further, the majority of LQTS patients have a corrected QT interval below this threshold, and a significant minority has normal resting corrected QT interval values. Here, we aimed to develop and validate a novel, quantitative T wave morphological analysis program to differentiate LQTS patients from healthy controls.

Methods and Results—

We analyzed a genotyped cohort of 420 patients (22±16 years, 43% male) with either LQT1 (61%) or LQT2 (39%). ECG analysis was conducted using a novel, proprietary T wave analysis program that quantitates subtle changes in T wave morphology. The top 3 discriminating features in each ECG lead were determined and the lead with the best discrimination selected. Classification was performed using a linear discriminant classifier and validated on an untouched cohort. The top 3 features were Tpeak–Tend interval, T wave left slope, and T wave center of gravity x axis (last 25% of the T wave). Lead V6 had the best discrimination. It could distinguish 86.8% of LQTS patients from healthy controls. Moreover, it distinguished 83.33% of patients with concealed LQTS from controls, despite having essentially identical resting corrected QT interval values.

Conclusions—

T wave quantitative analysis on the 12-lead surface ECG provides an effective, novel tool to distinguish patients with either LQT1/LQT2 from healthy matched controls. It can provide guidance while mutation-specific genetic testing is in motion for family members.

The detection of T-wave variation linked to arrhythmic risk: an industry perspective

Although the scientific literature contains ample descriptions of peculiar patterns of repolarization linked to arrhythmic risk, the objective quantification and classification of these patterns continues to be a challenge that impacts their widespread adoption in clinical practice. To advance the science, computerized algorithms spawned in the academic environment have been essential in order to find, extract and measure these patterns. However, outside the strict control of a core lab, these algorithms are exposed to poor quality signals and need to be effective in the presence of different forms of noise that can either obscure or mimic the T-wave variation (TWV) of interest. To provide a practical solution that can be verified and validated for the market, important tradeoffs need to be made that are based on an intimate understanding of the end-user as well as the key characteristics of either the signal or the noise that can be used by the signal processing engineer to best differentiate them. To illustrate this, two contemporary medical devices used for quantifying T-wave variation are presented, including the modified moving average (MMA) for the detection of T-wave Alternans (TWA) and the quantification of T-wave shape as inputs to the Morphology Combination Score (MCS) for the trending of drug-induced repolarization abnormalities.

Quantifying the origins of population variability in cardiac electrical activity through sensitivity analysis of the electrocardiogram

Altered function of ion channels in the heart can increase the risk of sudden arrhythmic death. Hundreds of genetic variants exist in these cardiac ion channel genes. The challenge is how to interpret the effects of multiple conductance perturbations on the complex multi-variable cardiac electrical system? In theory, sensitivity analysis can address this question. However, to date this approach has been restricted by computational overheads to analysis of isolated cells, which has limited extrapolation to physiologically relevant scales. The goal of this study was to extend existing sensitivity analyses to electrocardiogram (ECG) signals derived from multicellular systems and quantify the contribution of ionic conductances to emergent properties of the ECG. To achieve this, we have developed a highly parallelised simulation environment using unconventional high performance computing architectures to analyse the emergent electrical properties of a multicellular system. This has permitted the first systematic analysis of the molecular basis of the T wave amplitude, revealing important but distinct roles for delayed rectifier and inward rectifier K(+) currents. In addition to quantifying how interactions between multiple ion channels influence ECG parameters we show that these sensitivities are dynamic functions of heart rate. This study provides a significant advance in our understanding both of how individual ion conductances define ECG signals and of epistatic modification of cardiac electrical phenotypes. The parallelised simulation environment we have developed removes the computational roadblock that has limited this approach and so provides the framework for future analysis of more complex tissue and whole organ systems.

Measure of the QT-RR dynamic coupling in patients with the long QT syndrome

BACKGROUND

The patients with the long QT syndrome type-1 (LQT-1) have an impaired adaptation of the QT interval to heart rate changes. Yet, the description of the dynamic QT-RR coupling in genotyped LQT-1 has never been thoroughly investigated.

METHOD

We propose a method to model the dynamic QT-RR coupling by defining a transfer function characterizing the relationship between a QT interval and its previous RR intervals measured from ambulatory Holter recordings. Three parameters are used to characterize the QT-RR coupling: a fast gain (Gain(F) ), a slow gain (Gain(L) ), and a time constant (τ). We investigated the values of these parameters across genders, and in genotyped LQT-1 patients with normal QTc interval duration (QTc < 470 ms).

RESULTS

The QT-RR dynamic profiles are significantly different between LQT-1 patients (97) and controls (154): LQT-1 have longer QTc interval (453 ± 35 vs. 384 ± 26 ms, P < 0.0001), and an increased dependency of the QT interval to previous RR changes revealed by a larger Gain(L) (0.22 ± 0.06 vs. 0.18 ± 0.07, P < 0.0001) and Gain(F) (0.05 ± 0.02 vs. 0.03 ± 0.01, P < 0.0001). Importantly, LQT-1 patients have a faster QT dynamic response to previous RR changes described by τ: 122 ± 44 vs. 172 ± 92 beats (P < 0.0001). This faster QT dynamic response of the QT-RR dynamic coupling remained in LQT-1 patients with QTc in a normal range (<430 ms).

CONCLUSIONS

The measurement of QT-RR dynamic coupling could be used in patients suspected to carry a concealed form of the LQT-1 syndrome, or to provide insights into the types of arrhythmogenic triggers a patient may be prone to.

Genotype- and Sex-Specific QT-RR Relationship in the Type-1 Long-QT Syndrome

BACKGROUND

Genotype-phenotype investigations have revealed significantly larger risk for cardiac events in patients with type 1 long-QT syndrome (LQT-1), particularly in adult females, with missense mutation in the cytoplasmic loop (C-loop) regions of the α subunit of the KCNQ1 gene associated with an impaired ion channel activation by adrenergic stimulus. We hypothesize that the impaired response to increases in heart rate leads to abnormal QT-RR dynamic profiles and is responsible for the increased cardiac risk for these patients.

METHODS AND RESULTS

We measured the QT-RR slope in 24-hour Holter ECGs from LQT-1 patients with the mutations associated with impaired adrenergic stimulus (C-loop, n=18) and compared to LQT-1 patients with other mutations (non-C-loop, n=48), and to a healthy control group (n=195). The diurnal QT-RR slope was less steep in C-loop mutation patients (0.10±0.05) than in the ECGs from non-C-loop mutation patients (0.17±0.09, P=0.002). For female patients, slower heart rates were associated with prolonged QT and increased QT-RR slope. Male patients with C-loop mutations showed an impaired repolarization for shorter range of heart rates than in females, which is consistent with gender differences in triggers for events in this syndrome.

CONCLUSIONS

Our observations suggest that the C-loop LQT-1 patients have specific impaired adrenergic regulation of the ventricular repolarization. This response to heart rate increases may be useful in identification of high-risk patients with inherited prolonged QT and may help select an optimal antiarrhythmic therapeutic strategy. (J Am Heart Assoc. 2012;1:e000570 doi: 10.1161/JAHA.112.000570.).

Diastolic electromechanical coupling: association of the ECG T-peak to T-end interval with echocardiographic markers of diastolic dysfunction

BACKGROUND

Electromechanical coupling, a well-described phenomenon in systolic dysfunction, has not been well studied in diastole. We hypothesized that the ECG T-peak to T-end (TpTe) interval, representing transmural dispersion of repolarization, is associated with echocardiographic markers of diastolic dysfunction (DD).

METHODS AND RESULTS

We performed a prospective, cross-sectional study of the association between TpTe and markers of DD in 84 consecutive, unselected patients referred for exercise echocardiography. We systematically measured TpTe on the resting ECG, and we performed comprehensive assessment of DD at rest and at peak stress. ECGs and echocardiograms were analyzed independently, blinded to each other and to all clinical data. By univariable analysis, increased TpTe was associated with older age, increased E/e' ratio, and DD (P<0.05 for all associations after correcting for multiple comparisons). Increased TpTe was inversely associated with reduced tissue Doppler e' velocity, a marker of DD (R=-0.66, P<0.0001). This association persisted after adjusting for age, QTc, exercise-induced wall motion abnormalities, and left ventricular mass index (β=-0.41 [95% confidence interval, -0.70 to -0.12] cm/s per 10-ms increase in TpTe; P=0.006). Baseline TpTe was also independently associated with resting DD (adjusted odds ratio, 3.9 [95% confidence interval, 1.4-10.7]; P=0.009) and peak exercise E/e' ratio (P<0.0001).

CONCLUSIONS

Increased TpTe is associated with both resting and exercise-induced DD. Electromechanical coupling may represent a pathophysiologic link between electrical transmural dispersion of repolarization and abnormal myocardial relaxation, and may be a novel therapeutic target.