Robust QT interval estimation--from algorithm to validation

Intra-subject stability of different expressions of spatial QRS-T angle and their relationship to heart rate

Three-dimensional angle between the QRS complex and T wave vectors is a known powerful cardiovascular risk predictor. Nevertheless, several physiological properties of the angle are unknown or poorly understood. These include, among others, intra-subject profiles and stability of the angle relationship to heart rate, characteristics of angle/heart-rate hysteresis, and the changes of these characteristics with different modes of QRS-T angle calculation. These characteristics were investigated in long-term 12-lead Holter recordings of 523 healthy volunteers (259 females). Three different algorithmic methods for the angle computation were based on maximal vector magnitude of QRS and T wave loops, areas under the QRS complex and T wave curvatures in orthogonal leads, and weighted integration of all QRS and T wave vectors moving around the respective 3-dimensional loops. These methods were applied to orthogonal leads derived either by a uniform conversion matrix or by singular value decomposition (SVD) of the original 12-lead ECG, giving 6 possible ways of expressing the angle. Heart rate hysteresis was assessed using the exponential decay models. All these methods were used to measure the angle in 659,313 representative waveforms of individual 10-s ECG samples and in 7,350,733 individual beats contained in the same 10-s samples. With all measurement methods, the measured angles fitted second-degree polynomial regressions to the underlying heart rate. Independent of the measurement method, the angles were found significantly narrower in females (p < 0.00001) with the differences to males between 10o and 20o, suggesting that in future risk-assessment studies, different angle dichotomies are needed for both sexes. The integrative method combined with SVD leads showed the highest intra-subject reproducibility (p < 0.00001). No reproducible delay between heart rate changes and QRS-T angle changes was found. This was interpreted as a suggestion that the measurement of QRS-T angle might offer direct assessment of cardiac autonomic responsiveness at the ventricular level.

Short-Term Beat-to-Beat QT Variability Appears Influenced More Strongly by Recording Quality Than by Beat-to-Beat RR Variability

Increases in beat-to-beat variability of electrocardiographic QT interval duration have repeatedly been associated with increased risk of cardiovascular events and complications. The measurements of QT variability are frequently normalized for the underlying RR interval variability. Such normalization supports the concept of the so-called immediate RR effect which relates each QT interval to the preceding RR interval. The validity of this concept was investigated in the present study together with the analysis of the influence of electrocardiographic morphological stability on QT variability measurements. The analyses involved QT and RR measurements in 6,114,562 individual beats of 642,708 separate 10-s ECG samples recorded in 523 healthy volunteers (259 females). Only beats with high morphology correlation (r > 0.99) with representative waveforms of the 10-s ECG samples were analyzed, assuring that only good quality recordings were included. In addition to these high correlations, SDs of the ECG signal difference between representative waveforms and individual beats expressed morphological instability and ECG noise. In the intra-subject analyses of both individual beats and of 10-s averages, QT interval variability was substantially more strongly related to the ECG noise than to the underlying RR variability. In approximately one-third of the analyzed ECG beats, the prolongation or shortening of the preceding RR interval was followed by the opposite change of the QT interval. In linear regression analyses, underlying RR variability within each 10-s ECG sample explained only 5.7 and 11.1% of QT interval variability in females and males, respectively. On the contrary, the underlying ECG noise contents of the 10-s samples explained 56.5 and 60.1% of the QT interval variability in females and males, respectively. The study concludes that the concept of stable and uniform immediate RR interval effect on the duration of subsequent QT interval duration is highly questionable. Even if only stable beat-to-beat measurements of QT interval are used, the QT interval variability is still substantially influenced by morphological variability and noise pollution of the source ECG recordings. Even when good quality recordings are used, noise contents of the electrocardiograms should be objectively examined in future studies of QT interval variability.

Applications of Machine Learning in Ambulatory ECG

The ambulatory ECG (AECG) is an important diagnostic tool for many heart electrophysiology-related cases. AECG covers a wide spectrum of devices and applications. At the core of these devices and applications are the algorithms responsible for signal conditioning, ECG beat detection and classification, and event detections. Over the years, there has been huge progress for algorithm development and implementation thanks to great efforts by researchers, engineers, and physicians, alongside the rapid development of electronics and signal processing, especially machine learning (ML). The current efforts and progress in machine learning fields are unprecedented, and many of these ML algorithms have also been successfully applied to AECG applications. This review covers some key AECG applications of ML algorithms. However, instead of doing a general review of ML algorithms, we are focusing on the central tasks of AECG and discussing what ML can bring to solve the key challenges AECG is facing. The center tasks of AECG signal processing listed in the review include signal preprocessing, beat detection and classification, event detection, and event prediction. Each AECG device/system might have different portions and forms of those signal components depending on its application and the target, but these are the topics most relevant and of greatest concern to the people working in this area.

Influence of heart rate correction formulas on QTc interval stability

Monitoring of QTc interval is mandated in different clinical conditions. Nevertheless, intra-subject variability of QTc intervals reduces the clinical utility of QTc monitoring strategies. Since this variability is partly related to QT heart rate correction, 10 different heart rate corrections (Bazett, Fridericia, Dmitrienko, Framingham, Schlamowitz, Hodges, Ashman, Rautaharju, Sarma, and Rabkin) were applied to 452,440 ECG measurements made in 539 healthy volunteers (259 females, mean age 33.3 ± 8.4 years). For each correction formula, the short term (5-min time-points) and long-term (day-time hours) variability of rate corrected QT values (QTc) was investigated together with the comparisons of the QTc values with individually corrected QTcI values obtained by subject-specific modelling of the QT/RR relationship and hysteresis. The results showed that (a) both in terms of short-term and long-term QTc variability, Bazett correction led to QTc values that were more variable than the results of other corrections (p < 0.00001 for all), (b) the QTc variability by Fridericia and Framingham corrections were not systematically different from each other but were lower than the results of other corrections (p-value between 0.033 and < 0.00001), and (c) on average, Bazett QTc values departed from QTcI intervals more than the QTc values of other corrections. The study concludes that (a) previous suggestions that Bazett correction should no longer be used in clinical practice are fully justified, (b) replacing Bazett correction with Fridericia and/or Framingham corrections would improve clinical QTc monitoring, (c) heart rate stability is needed for valid QTc assessment, and (d) development of further QTc corrections for day-to-day use is not warranted.

Heart Rate Dependency and Inter-Lead Variability of the T Peak - T End Intervals

The electrocardiographic (ECG) assessment of the T peak–T end (Tpe) intervals has been used in many clinical studies, but several related physiological aspects have not been reported. Specifically, the sources of the Tpe differences between different ECG leads have not been systematically researched, the relationship of Tpe duration to underlying heart rate has not been firmly established, and little is known about the mutual correspondence of Tpe intervals measured in different ECG leads. This study evaluated 796,620 10-s 12-lead ECGs obtained from long-term Holters recorded in 639 healthy subjects (311 female) aged 33.8 ± 9.4 years. For each ECG, transformation to orthogonal XYZ lead was used to measure Tpe in the orthogonal vector magnitude (used as a reference for lead-to-lead comparisons) and to construct a three-dimensional T wave loop. The loop roundness was expressed by a ratio between its circumference and length. These ratios were significantly related to the standard deviation of Tpe durations in different ECG leads. At the underlying heart rate of 60 beats per minute, Tpe intervals were shorter in female than in male individuals (82.5 ± 5.6 vs 90.0 ± 6.5 ms, p < 0.0001). When studying linear slopes between Tpe intervals measured in different leads and the underlying heart rate, we found only minimal heart rate dependency, which was not systematic across the ECG leads and/or across the population. For any ECG lead, positive Tpe/RR slope was found in some subjects (e.g., 79 and 25% of subjects for V2 and V4 measurements, respectively) and a negative Tpe/RR slope in other subjects (e.g., 40 and 65% for V6 and V5, respectively). The steepest positive and negative Tpe/RR slopes were found for measurements in lead V2 and V4, respectively. In all leads, the Tpe/RR slope values were close to zero, indicating, on average, Tpe changes well below 2 ms for RR interval changes of 100 ms. On average, longest Tpe intervals were measured in lead V2, the shortest in lead III. The study concludes that the Tpe intervals measured in different leads cannot be combined. Irrespective of the measured ECG lead, the Tpe interval is not systematically heart rate dependent, and no heart rate correction should be used in clinical Tpe investigations.

Heart Rate Influence on the QT Variability Risk Factors

QT interval variability, mostly expressed by QT variability index (QTVi), has repeatedly been used in risk diagnostics. Physiologic correlates of QT variability expressions have been little researched especially when measured in short 10-second electrocardiograms (ECGs). This study investigated different QT variability indices, including QTVi and the standard deviation of QT interval durations (SDQT) in 657,287 10-second ECGs recorded in 523 healthy subjects (259 females). The indices were related to the underlying heart rate and to the 10-second standard deviation of RR intervals (SDRR). The analyses showed that both QTVi and SDQT (as well as other QT variability indices) were highly statistically significantly (p < 0.00001) influenced by heart rate and that QTVi showed poor intra-subject reproducibility (coefficient of variance approaching 200%). Furthermore, sequential analysis of regression variance showed that SDQT was more strongly related to the underlying heart rate than to SDRR, and that QTVi was influenced by the underlying heart rate and SDRR more strongly than by SDQT (p < 0.00001 for these comparisons of regression dependency). The study concludes that instead of QTVi, simpler expressions of QT interval variability, such as SDQT, appear preferable for future applications especially if multivariable combination with the underlying heart rate is used.

A computational investigation into rate-dependant vectorcardiogram changes due to specific fibrosis patterns in non-ischæmic dilated cardiomyopathy

Patients with scar-associated fibrotic tissue remodelling are at greater risk of ventricular arrhythmic events, but current methods to detect the presence of such remodelling require invasive procedures. We present here a potential method to detect the presence, location and dimensions of scar using pacing-dependent changes in the vectorcardiogram (VCG). Using a clinically-derived whole-torso computational model, simulations were conducted at both slow and rapid pacing for a variety of scar patterns within the myocardium, with various VCG-derived metrics being calculated, with changes in these metrics being assessed for their ability to discern the presence and size of scar. Our results indicate that differences in the dipole angle at the end of the QRS complex and differences in the QRS area and duration may be used to predict scar properties. Using machine learning techniques, we were also able to predict the location of the scar to high accuracy, using only these VCG-derived rate-dependent changes as input. Such a non-invasive predictive tool for the presence of scar represents a potentially useful clinical tool for identifying patients at arrhythmic risk.

Effects of the Fluoroquinolones Moxifloxacin and Levofloxacin on the QT Subintervals: Sex Differences in Ventricular Repolarization

Women are associated with longer electrocardiographic QT intervals and increased proarrhythmic risks of QT‐prolonging drugs. The purpose of this study was to characterize the differences in cardiac electrophysiology between moxifloxacin and levofloxacin in men and women and to assess the balance of inward and outward currents through the analysis of QT subintervals. Data from 2 TQT studies were used to investigate the impact of moxifloxacin (400 mg) and levofloxacin (1000 and 1500 mg) on QT subintervals using algorithms for measurement of J‐T_peak and T_peak‐T_end intervals. Concentration‐effect analyses were performed to establish potential relationships between the ECG effects and the concentrations of the 2 fluoroquinolones. Moxifloxacin was shown to be a more potent prolonger of QT interval corrected by Fredericia (QTcF) and had a pronounced effect on J‐T_peakc. Levofloxacin had little effect on J‐T_peakc. For moxifloxacin, the concentration‐effect modeling showed a greater effect for women on QTcF and J‐T_peakc, whereas for levofloxacin the inverse was true: women had smaller QTcF and J‐T_peakc effects. The different patterns in repolarization after administration of both drugs suggested a sex difference, which may be related to the combined I_Ks and I_Kr inhibitory properties of moxifloxacin versus I_Kr suppression only of levofloxacin. The equipotent inhibition of I_Ks and I_Kr appears to affect women more than men. Sex hormones are known to influence cardiac ion channel expression and differences in QT duration. Differences in I_Kr and I_Ks balances, influenced by sex hormones, may explain the results. These results support the impact of sex differences on the cardiac safety assessment of drugs.

Optimal method of measuring the T-peak to T-end interval for risk stratification in primary prevention

Aims

Several published investigations demonstrated that a longer T-peak to T-end interval (Tpe) implies increased risk for ventricular tachyarrhythmia (VT/VF) and mortality. Tpe has been measured using diverse methods. We aimed to determine the optimal Tpe measurement method for screening purposes.

Methods and results

We evaluated 305 patients with LVEF ≤ 35% and an implantable cardioverter-defibrillator implanted for primary prevention. Tpe was measured using seven different methods described in the literature, including six manual methods and the automated algorithm '12SL', and was corrected for heart rate. Endpoints were VT/VF and death. To account for differences in the magnitude of Tpe measurements, results are expressed in standard deviation (SD) increments. We evaluated the clinical utility of each measurement method based on predictive ability, fraction of immeasurable tracings, and intra- and interobserver correlation. >Over 31 ± 23 months, 82 (27%) patients had VT/VF, and over 49 ± 21 months, 91 (30%) died. Several rate-corrected Tpe measurement methods predicted VT/VF (HR per SD 1.20-1.34; all P < 0.05), and nearly all methods (both corrected and uncorrected) predicted death (HR per SD 1.19-1.35; all P < 0.05). Optimal predictive ability, readability, and correlation were found in the automated 12SL method and the manual tangent method in lead V2.

Conclusion

For the prediction of VT/VF, the utility of Tpe depends upon the measurement method, but for the prediction of mortality, most published Tpe measurement methods are similarly predictive. Heart rate correction improves predictive ability. The automated 12SL method performs as well as any manual measurement, and among manual methods, lead V2 is most useful.

The Cardiovascular Effects of a Meal: J-Tpeak and Tpeak -Tend Assessment and Further Insights Into the Physiological Effects

Meal intake leads to a significant and prolonged increase in cardiac output to supply the splanchnic vasculature. A meal is associated with sympathetic activation of the cardiovascular system, and food ingestion is correlated with an increase in heart rate, an increase in cardiac stroke volume, and QTc interval shortening for up to 7 hours. Given the complexity of the system, one or several of many mechanisms could explain this observation. The shortening of the QTc interval was correlated with a rise of C‐peptide following food ingestion, but the mechanisms by which C‐peptide may be involved in the modulation of cardiac repolarization are still unknown. This shortening of the myocardial action potential caused by the ingestion of food was further investigated in the present study by measuring the QRS, J‐T_peak, and T_peak‐T_end intervals in search of further clues to better understand the underlying mechanisms. A retrospective analysis was conducted based on data collected in a formal thorough QT/QTc study in which 32 subjects received a carbohydrate‐rich “continental” breakfast, moxifloxacin without food, and moxifloxacin with food. We assessed the effect of food on T‐wave morphology using validated algorithms for measurement of J‐T_peak and T_peak‐T_end intervals. Our findings demonstrate that a standardized meal significantly shortened J‐T_peak for 4 hours after a meal and to a much lesser extent and shorter duration (up to 1 hour) prolonged the T_peak‐T_end and QRS intervals. This suggests that the QTc shortening occurs mainly during phase 2 of the cardiac action potential. As there was no corresponding effect on T_peak‐T_end beyond the first hour, we conclude that a meal does not interfere with the outward correcting potassium channels but possibly with Ca²⁺ currents. An effect on mainly Ca²⁺ aligns well with our understanding of physiology whereby an increase in stroke volume, as observed after a meal, is associated with changes in Ca²⁺ cycling in and out of the sarcoplasmic reticulum during cardiac myocyte contraction.

T-wave loop area from a pre-implant 12-lead ECG is associated with appropriate ICD shocks

Aims

In implantable cardioverter-defibrillator (ICD) patients, predictors of ICD shocks and mortality are needed to improve patient selection. Electrocardiographic (ECG) markers are simple to obtain and have been demonstrated to predict mortality. We aimed to assess the association of T-wave loop area and circularity with ICD shocks.

Methods

The study investigated patients with ICDs implanted between 1998 and 2010 for whom digital 12-lead ECGs (Schiller CS200 ECG-Network) of sufficient quality were obtained within 1 month prior to the implantation. T-wave loop area and circularity were calculated. Follow-up data of appropriate shocks were obtained during ICD clinic visits that included reviews of device stored electrograms.

Results

A total of 605 patients (82% males) were included; 68% had ischemic cardiomyopathy and 72% were treated for primary prevention. Over 3.8±1.4 years of follow-up, 114 patients (19%) experienced appropriate shock(s). Those with smaller T-wave loop area received fewer shocks (TLA, hazard ratio, HR, per increase of 1 technical unit, 0.71; [95% confidence interval, 0.53–0.94]; P = 0.02) and those with larger T-wave loop circularity (TLC) representing rounder T wave loop received more shocks (HR per 1% TLC increase 2.96; [0.85–10.36]; P = 0.09). When the quartile containing the largest TLA and TLC values, respectively, were compared to the remaining cases, TLA remained significantly associated with fewer and TLC with more frequent shocks also after multivariate adjustment for clinical variables (HR, 0.59 [0.35–0.99], P = 0.044; and 1.64 [1.08–2.49], P = 0.021, respectively).

Conclusions

The size and shape of the T-wave loop calculated from pre-implantation 12-lead ECGs are associated with appropriate ICD shocks.

T-Wave Morphology Analysis in Congenital Long QT Syndrome Discriminates Patients From Healthy Individuals

OBJECTIVES

This study aims to assess the capability of T-wave analysis to: 1) identify genotype-positive long QT syndrome (LQTS) patients; 2) identify LQTS patients with borderline or normal QTc interval (≤460 ms); and 3) classify LQTS subtype.

BACKGROUND

LQTS often presents with a nondiagnostic electrocardiogram (ECG). T-wave abnormalities may be the only marker of this potentially lethal arrhythmia syndrome.

METHODS

ECGs taken at rest in 108 patients (43 with LQTS1, 20 with LQTS2, and 45 control subjects) were evaluated for T-wave flatness, asymmetry, and notching, which produces a morphology combination score (MCS) of the 3 features (MCS = 1.6 × flatness + asymmetry + notch) using QT Guard Plus Software (GE Healthcare, Milwaukee, Wisconsin). To assess for heterogeneity of repolarization, the principal component analysis ratio 2 (PCA-2) was calculated.

RESULTS

Mean QTc intervals were 486 ± 50 ms (LQTS1), 479 ± 36 ms (LQTS2), and 418 ± 24 ms (control subjects) (p < 0.05). MCS and PCA-2 differed between LQTS patients and control subjects (MCS: 117.8 ± 57.4 vs. 71.9 ± 16.2; p < 0.001; PCA-2: 20.2 ± 10.4% vs. 14.6 ± 5.5%; p < 0.001), LQTS1 and LQTS2 patients (MCS: 96.3 ± 28.7 vs. 164 ± 75.2; p < 0.001; PCA-2: 17.8 ± 8.3% vs. 25 ± 12.6%; p < 0.001), and between LQTS patients with borderline or normal QTc intervals (n = 17) and control subjects (MCS: 105.7 ± 49.9 vs. 71.9 ± 16.2; p < 0.001; PCA-2: 18.1 ± 7.2% vs. 14.6 ± 5.5%; p < 0.001). T-wave metrics were consistent across multiple ECGs from individual patients based on the average intraclass correlation coefficient (MCS: 0.96; PCA-2: 0.86).

CONCLUSIONS

Automated T-wave morphology analysis accurately discriminates patients with pathogenic LQTS mutations from control subjects and between the 2 most common LQTS subtypes. Mutation carriers without baseline QTc prolongation were also identified. This may be a useful tool for screening families of LQTS patients, particularly when the QTc interval is subthreshold and genetic testing is unavailable.

T-peak to T-end interval for prediction of ventricular tachyarrhythmia and mortality in a primary prevention population with systolic cardiomyopathy

BACKGROUND

The electrocardiographic T-wave peak to T-wave end interval (Tpe) correlates with dispersion of ventricular repolarization (DVR). Increased DVR increases propensity toward electrical reentry that can cause ventricular tachyarrhythmia. The baseline rate-corrected Tpe (Tpec) has been shown to predict ventricular tachyarrhythmia and death in multiple patient populations but not among cardiomyopathic patients undergoing insertion of an implantable cardioverter-defibrillator (ICD) for primary prevention.

OBJECTIVE

The purpose of this study was to assess the risk stratification ability of the Tpec in patients with systolic cardiomyopathy without prior ventricular tachyarrhythmia (ie, the primary prevention population).

METHODS

We performed prospective follow-up of 305 patients (73% men; left ventricular ejection fraction [LVEF] 23 ± 7%) with LVEF ≤35% and an ICD implanted for primary prevention. Baseline ECGs were analyzed with automated algorithms. Endpoints were ventricular tachycardia (VT)/ventricular fibrillation (VF), death, and a combined endpoint of VT/VF or death, assessed by device follow-up and Social Security Death Index query.

RESULTS

The average Tpec was 107 ± 22 ms. During device clinic follow-up of 31 ± 23 months, 82 patients (27%) had appropriate ICD therapy for VT/VF, and during mortality follow-up of 49 ± 21 months, 91 patients (30%) died. On univariable analysis, Tpec predicted VT/VF, death, and the combined endpoint of VT/VF or death (P < .05 for each endpoint). Multivariable analysis included univariable predictors among demographics, clinical data, laboratory data, medications used, and electrocardiography parameters. After correction, Tpec remained predictive of VT/VF (hazard ratio [HR] per 10-ms increase 1.16, P = .009), all-cause mortality (HR per 10 ms 1.13, P = .05), and the combined endpoint (HR per 10 ms 1.17, P = .001).

CONCLUSION

Tpec independently predicts both VT/VF and overall mortality in patients with systolic dysfunction and ICDs implanted for primary prevention.

Electrocardiographic markers of repolarization heterogeneity during dofetilide or sotalol initiation for paroxysmal atrial fibrillation

Serial electrocardiographic monitoring of ΔQTc as an assumed harbinger of proarrhythmia is currently recommended for dofetilide and sotalol initiation. Markers of repolarization heterogeneity such as increased peak to end of T-wave (TpTe) duration and abnormal T-wave morphology may also predict proarrhythmia. We investigated whether such T-wave measurements on baseline electrocardiogram will correlate with ΔQTc after drug initiation. An analysis of 140 consecutive patients with paroxysmal atrial fibrillation hospitalized in sinus rhythm for sotalol or dofetilide initiation was performed. Baseline and serial electrocardiograms were analyzed using QT Guard Plus software (GE Healthcare), which measured QTc and TpTe and scored T-wave morphology for asymmetry, notching, and flatness using T-wave vector magnitude and principal component analysis algorithms. Sotalol and dofetilide were administered in 71% and 29% of patients, respectively. Mean age was 61 ± 14 years, and 34% were women. After a single dose of either drug, there was a statistically significant increase in QTc and TpTe (p <0.01), as well as composite and individual T-wave markers of repolarization heterogeneity (p <0.01). QTc increased by a mean of 19 ± 30 ms after initial antiarrhythmic dose. ΔQTc was inversely related to baseline QTc and TpTe (p <0.01). After controlling for baseline QTc, there was no independent association between T-wave markers of repolarization heterogeneity and ΔQTc. In conclusion, for patients with paroxysmal atrial fibrillation admitted for dofetilide or sotalol loading, T-wave markers of increased repolarization heterogeneity are measurable within hours after initiation. A shorter baseline QTc is associated with an increased ΔQTc; however, there is no independent relation between baseline T-wave markers of repolarization heterogeneity and ΔQTc.

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.

Potassium dynamics are attenuated in hyperkalemia and a determinant of QT adaptation in exercising hemodialysis patients

Disturbances in plasma potassium concentration (pK) are well known risk factors for the development of cardiac arrhythmia. The aims of the present study were to evaluate the effect of hemodialysis on exercise pK dynamics and QT hysteresis, and whether QT hysteresis is associated with the pK decrease following exercise. Twenty-two end-stage renal disease patients exercised on a cycle ergometer with incremental work load before and after hemodialysis. ECG was recorded and pK was measured during exercise and recovery. During exercise, pK increased from 5.1 ± 0.2 to 6.1 ± 0.2 mM (mean ± SE; P < 0.0001) before hemodialysis and from 3.8 ± 0.1 to 5.1 ± 0.1 mM (P < 0.0001) after hemodialysis. After 2 min of recovery, pK had decreased to 5.0 ± 0.2 mM and 4.1 ± 0.1 mM (P < 0.0001) before and after hemodialysis, respectively. pK increase during exercise was accentuated after hemodialysis. The pK increase was negatively linearly correlated with pK before exercise (β = -0.21, R(2) = 0.23, P = 0.001). QT hysteresis was negatively linearly correlated with the decrease in pK during recovery (β = -28 ms/mM, R(2) = 0.36, P = 0.006). Thus, during recovery, low pK was associated with relatively longer QT interval. In conclusion, new major findings are an accentuated increase in pK during exercise after hemodialysis, an attenuated increase in pK in hyperkalemia, and an association between pK and QT interval adaptation during recovery. The acute pK shift after exercise may modulate QT interval adaptation and trigger cardiac arrhythmias.

Effects of bilastine on T-wave morphology and the QTc interval: a randomized, double-blind, placebo-controlled, thorough QTc study

BACKGROUND AND OBJECTIVES

The International Conference of Harmonisation (ICH) E14 guideline for thorough QT studies requires assessing the propensity of new non-antiarrhythmic drugs to affect cardiac repolarization. The present study investigates whether a composite ECG measure of T-wave morphology (Morphology Combination Score [MCS]) can be used together with the heart rate corrected QT interval (QTc) in a fully ICH E14-compliant thorough QT study to exclude clinically relevant repolarization effects of bilastine, a novel antihistamine.

METHODS

Thirty participants in this crossover study were randomly assigned to receive placebo, moxifloxacin 400 mg, bilastine at therapeutic and supratherapeutic doses (20 and 100 mg) and bilastine 20 mg co-administered with ketoconazole 400 mg. Resting ECGs recorded at 12 nominal time points before and after treatments were used to determine Fridericia corrected QTc (QTcF) and MCS from the T-wave characteristics: asymmetry, flatness and notching.

RESULTS

There were no effects of bilastine monotherapy (20 and 100 mg) on MCS or QTcF at those study times where the bilastine plasma concentrations were highest. MCS changes for bilastine monotherapy did not exceed the normal intrasubject variance of T-wave shapes for triplicate ECG recordings. Maximum QTcF prolongation for bilastine monotherapy was 5 ms or less: 3.8 ms (90% CI 0.3, 7.3 ms) for bilastine 20 mg and 5.0 ms (90% CI 2.0, 8.0 ms) for bilastine 100 mg. There were no indications of bilastine inducing larger repolarization effects on T-wave morphology as compared with the QTcF interval, as evidenced by the similarity of z-score equivalents for placebo-corrected changes in MCS and QTcF values.

CONCLUSION

This study shows that bilastine, at therapeutic and supratherapeutic dosages, does not induce any effects on T-wave morphology or QTcF. These results confirm the absence of an effect for bilastine on cardiac repolarization.

Relationships between the T-peak to T-end interval, ventricular tachyarrhythmia, and death in left ventricular systolic dysfunction

AIMS

The interval between the T-wave's peak and end (Tpe), an electrocardiographic (ECG) index of ventricular repolarization, has been proposed as an indicator of arrhythmic risk. We aimed to clarify the clinical usefulness of Tpe for risk stratification.

METHODS AND RESULTS

We evaluated 327 patients with left ventricular ejection fraction (LVEF) ≤ 35% (75% male, LVEF 23 ± 7%). All patients had an implanted implantable cardioverter-defibrillator (ICD). Clinical data and ECGs were analysed at baseline. Prospective follow-up for the endpoints of appropriate ICD therapy and mortality was conducted via periodic device interrogation, chart review, and the Social Security Death Index. During device clinic follow-up of 17 ± 12 months, 59 (18%) patients had appropriate ICD therapy, and during mortality follow-up of 30 ± 13 months, 67 (21%) patients died. A longer Tpe(c) predicted appropriate ICD therapy, death, and the combination of appropriate ICD therapy or death (P< 0.01 for each endpoint). On multivariable analysis correcting for other univariable predictors, Tpe(c) remained predictive of ICD therapy [hazard ratio (HR) per 10 ms increase: 1.16, P= 0.02], all-cause mortality (HR per 10 ms: 1.14, P= 0.03), and the composite endpoint of ICD therapy or death (HR per 10 ms: 1.16, P< 0.01).

CONCLUSIONS

In patients with left ventricular systolic dysfunction and an implanted ICD, Tpe(c) independently predicts both ventricular tachyarrhythmia and overall mortality.

Highly automated QT measurement techniques in 7 thorough QT studies implemented under ICH E14 guidelines

Thorough QT (TQT) studies are designed to evaluate potential effect of a novel drug on the ventricular repolarization process of the heart using QTc prolongation as a surrogate marker for torsades de pointes. The current process to measure the QT intervals from the thousands of electrocardiograms is lengthy and expensive. In this study, we propose a validation of a highly automatic-QT interval measurement (HA-QT) method. We applied a HA-QT method to the data from 7 TQT studies. We investigated both the placebo and baseline-adjusted QTc interval prolongation induced by moxifloxacin (positive control drug) at the time of expected peak concentration. The comparative analysis evaluated the time course of moxifloxacin-induced QTc prolongation in one study as well. The absolute HA-QT data were longer than the FDA-approved QTc data. This trend was not different between ECGs from the moxifloxacin and placebo arms: 9.6 ± 24 ms on drug and 9.8 ± 25 ms on placebo. The difference between methods vanished when comparing the placebo-baseline-adjusted QTc prolongation (1.4 ± 2.8 ms, P = 0.4). The differences in precision between the HA-QT and the FDA-approved measurements were not statistically different from zero: 0.1 ± 0.1 ms (P = 0.7). Also, the time course of the moxifloxacin-induced QTc prolongation adjusted for placebo was not statistically different between measurements methods.

Improvement and limitation of the reliability of automated QT measurement by recent algorithms

BACKGROUND

Newer algorithms for automated QT interval measurements may be more reliable than previous algorithms.

OBJECTIVE

This study compares Bazett-corrected QTc obtained by an older algorithm (Old12SL) and by 2 newer ones (New12SL and v3.19) to semiautomated measurement performed by experienced cardiologists.

METHODS

A total of 6105 randomly selected electrocardiograms were classified by the cardiologists as normal (4227), borderline (1254), abnormal (575), or not analyzable (49). Errors of automated measurement were defined by more than 30 milliseconds of absolute difference between Bazett-corrected QTc obtained by automated algorithms and semiautomated measurement.

RESULTS

The Old12SL had approximately twice as many errors (5.25%) as the New12SL (2.33%) and v3.19 (2.30%), P < .0001. Abnormal tracings resulted in more errors than did normal ones (Old12SL: 16.52% vs 3.45%, New12SL: 7.30% vs 1.51%, and v3.19: 10.61% vs 1.21%).

CONCLUSION

Newer automated algorithms for QT measurements are highly reliable in normal tracings. However, electrocardiogram abnormalities increase the risk of QT measurement errors.