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

Markers of ventricular repolarization and overall mortality in sleep disordered breathing

INTRODUCTION

Variability and prolongation of ventricular repolarization - measured by changes in QT interval and QT variability are independently associated with ventricular arrhythmias, sudden death, and mortality but such studies did not examine the role of sleep-disordered breathing. We aimed to determine whether sleep-disordered breathing moderated the association between measures of ventricular repolarization and overall mortality.

METHODS

Eight hundred participants were randomly selected from each of the following four groups in the Sleep Heart Health Study: mild, moderate, severe or no sleep disordered breathing (n = 200 each). Overnight electrocardiograms were analyzed for QTc duration and QT variability (standard deviation of QT intervals, normalized QT interval variance and the short-term interval beat-to-beat QT variability). Cox proportional hazards penalized regression modeling was used to identify predictors of mortality.

RESULTS

Eight hundred of 5600 participants were randomly selected. The participants (68 ± 10 years; 56.8% male) were followed for an average of 8.2 years during which time 222 (28.4%) died. QTc, SDQT, and QTVN were associated with the presence of SDB (p = 0.002, p = 0.014, and p = 0.024, respectively). After adjusting for covariates, the presence of sleep-disordered breathing did not moderate the association between QTc length, QT variability and mortality (p > 0.05).

CONCLUSION

Sleep-disordered breathing was associated with some measures of ventricular repolarization. However, sleep-disordered breathing was not an effect modifier for the relationship between QTc and QT variability and mortality.

Research implications of the FDA ECG warehouse and related resources

The electrocardiogram is often used as an efficacy endpoint for comparing new drugs or as an indicator for cardiovascular safety in both studies of arrhythmic and non-arrhythmic novel drugs. The FDA ECG Warehouse data are owned by the submitting entities, generally pharmaceutical company manufacturers. However, a subset of these ECG data was released with permission from the data owners to the CSRC for access by investigators, equipment manufacturers and algorithm developers for CSRC-approved research and development studies. This article provides an overview of the Cardiac Safety Research Consortium (CSRC) ECG Warehouse, including data availability, completed and ongoing projects, as well as future growth potential amidst an ever expanding FDA ECG Warehouse. Given that current ICH E14 guidelines request that sponsors submitting new drug applications assess the effects on the QT interval using a thorough QT (TQT) or dose-ranging study with concentration-QT analysis during early clinical development to assess cardiac risk, developing novel methods to determine cardiovascular safety, as well as understanding current ECG collection and analysis methods are prudent. The ability to utilize previously collected ECG data for secondary analyses improves cardiovascular safety by multiplying the scientific contribution of the original research.

Bayesian fusion of physiological measurements using a signal quality extension

OBJECTIVE

The fusion of multiple noisy labels for biomedical data (such as ECG annotations, which may be obtained from human experts or from automated systems) into a single robust annotation has many applications in physiologic monitoring. Directly modelling the difficulty of the task has the potential to improve the fusion of such labels. This paper proposes a means for the incorporation of task difficulty, as quantified by 'signal quality', into the fusion process.

APPROACH

We propose a Bayesian fusion model to infer a consensus through aggregating labels, where the labels are provided by multiple imperfect automated algorithms (or 'annotators'). Our model incorporates the signal quality of the underlying recording when fusing labels. We compare our proposed model with previously published approaches. Two publicly available datasets were used to demonstrate the feasibility of our proposed model: one focused on QT interval estimation in the ECG and the other focused on respiratory rate (RR) estimation from the photoplethysmogram (PPG). We inferred the hyperparameters of our model using maximum- a posteriori inference and Gibbs sampling.

MAIN RESULTS

For the QT dataset, our model significantly outperformed the previously published models (root-mean-square error of [Formula: see text] ms for our model versus [Formula: see text] ms from the best existing model) when fusing labels from only three annotators. For the RR dataset, no improvement was observed compared to the same model without signal quality modelling, where our model outperformed existing models (mean-absolute error of [Formula: see text] bpm for our model versus [Formula: see text] bpm from the best existing model). We conclude that our approach demonstrates the feasibility of using a signal quality metric as a confidence measure to improve label fusion.

SIGNIFICANCE

Our Bayesian learning model provides an extension over existing work to incorporate signal quality as a confidence measure to improve the reliability of fusing labels from biomedical datasets.

Unsupervised Bayesian Inference to Fuse Biosignal Sensory Estimates for Personalizing Care

The role of sensing technologies, such as wearables, in delivering precision care is becoming widely acceptable. Given the very large quantities of sensor data that rapidly accumulate, there is a need to employ automated algorithms to label biosignal sensor data. In many real-life clinical applications, no such expert labels are available, and algorithms for processing sensor data must be relied upon, without access to the "ground truth." It is therefore extremely difficult to choose which algorithms to trust or discard at any point in time, where different algorithms may be optimal for different patients, or even for different points in time for the same patient. We propose two fully Bayesian approaches for fusing labels from independent and potentially correlated annotators (i.e., algorithms or, where available, experts). These are generative models to aggregate labels (i.e., the outputs of the algorithms, such as identified ECG morphology) in an unsupervised manner, to estimate jointly the assumed bias and precision of each algorithm without access to the ground truth. The latter fused estimate may then be used to infer the underlying ground truth. For the first time in the biomedical context, we show that modeling correlations between annotators, and fusing information concerning task difficulty (such as the estimated quality of the sensor data), improve these estimates with respect to commonly employed strategies in the literature. Also, we adopt a strongly Bayesian approach to inference using Gibbs sampling to improve estimates over the existing state of the art. We present results from applying the proposed pair of models to simulated and two publicly available biomedical datasets, to demonstrate proof-of-principle. We show that our proposed models outperform all existing approaches recreated from the literature. We also show that the proposed methods are robust when dealing with missing values (as often occurs in real-life biomedical applications), and that they are suitably efficient for use in real-time applications, thereby providing the basis for the reliable use of sensors for personalizing the care of the individual.

Comparison of automated interval measurements by widely used algorithms in digital electrocardiographs

BACKGROUND

Automated measurements of electrocardiographic (ECG) intervals by current-generation digital electrocardiographs are critical to computer-based ECG diagnostic statements, to serial comparison of ECGs, and to epidemiological studies of ECG findings in populations. A previous study demonstrated generally small but often significant systematic differences among 4 algorithms widely used for automated ECG in the United States and that measurement differences could be related to the degree of abnormality of the underlying tracing. Since that publication, some algorithms have been adjusted, whereas other large manufacturers of automated ECGs have asked to participate in an extension of this comparison.

METHODS

Seven widely used automated algorithms for computer-based interpretation participated in this blinded study of 800 digitized ECGs provided by the Cardiac Safety Research Consortium. All tracings were different from the study of 4 algorithms reported in 2014, and the selected population was heavily weighted toward groups with known effects on the QT interval: included were 200 normal subjects, 200 normal subjects receiving moxifloxacin as part of an active control arm of thorough QT studies, 200 subjects with genetically proved long QT syndrome type 1 (LQT1), and 200 subjects with genetically proved long QT syndrome Type 2 (LQT2).

RESULTS

For the entire population of 800 subjects, pairwise differences between algorithms for each mean interval value were clinically small, even where statistically significant, ranging from 0.2 to 3.6milliseconds for the PR interval, 0.1 to 8.1milliseconds for QRS duration, and 0.1 to 9.3milliseconds for QT interval. The mean value of all paired differences among algorithms was higher in the long QT groups than in normals for both QRS duration and QT intervals. Differences in mean QRS duration ranged from 0.2 to 13.3milliseconds in the LQT1 subjects and from 0.2 to 11.0milliseconds in the LQT2 subjects. Differences in measured QT duration (not corrected for heart rate) ranged from 0.2 to 10.5milliseconds in the LQT1 subjects and from 0.9 to 12.8milliseconds in the LQT2 subjects.

CONCLUSIONS

Among current-generation computer-based electrocardiographs, clinically small but statistically significant differences exist between ECG interval measurements by individual algorithms. Measurement differences between algorithms for QRS duration and for QT interval are larger in long QT interval subjects than in normal subjects. Comparisons of population study norms should be aware of small systematic differences in interval measurements due to different algorithm methodologies, within-individual interval measurement comparisons should use comparable methods, and further attempts to harmonize interval measurement methodologies are warranted.

Clinical applications of QT/RR hysteresis assessment: A systematic review

BACKGROUND

QT/RR hysteresis (QT-hys) is an index of the time accommodation of ventricular repolarization to heart rate changes. This report comprehensively reviews studies addressing QT-hys as a biomarker of medical conditions.

METHODS

This is a secondary analysis of data from a recent systematic review pertaining to methods of assessment of QT-hys. Articles included in the former review were filtered in order to select original articles investigating the association of QT-hys with medical conditions in humans.

RESULTS

Nineteen articles fulfilled our inclusion criteria. Given the heterogeneity of the methods and investigated conditions, no pooled analysis of data could be implemented. QT-hys was mostly studied as a risk marker of severe arrhythmias, as a predictor of the long QT syndrome (LQTS) phenotypes and genotypes and as a marker of exercise-induced ischemia. An increased QT-hys appears to be implicated in arrhythmogenesis, although the evidence in this regard relies on few human studies. An augmented QT-hys was reported in the LQTS, predominantly in the LQT2 genotype, but conflicting results were obtained between studies using different methods of assessment. In addition, QT-hys appears to be a useful marker of stress-induced myocardial ischemia in patients suspected of coronary artery disease.

CONCLUSIONS

QT-hys evaluation has potential clinical utility in at least some clinical conditions. Further studies of the clinical validity of QT-hys assessment are warranted, particularly condition specific studies based on QT-hys evaluation methods that provide separate estimates of QT-hys and QT/RR dependency.

Population-based beat-to-beat QT analysis from Holter recordings in the long QT syndrome

The increasing dissemination of wearable ECG recorders (e.g. Holter, patches, and strap sensors) enables the acquisition of large amounts of data during long periods of time. However, the clinical value of these long-term continuous recordings is hindered by the lack of automatic tools to extract clinically relevant information (other than non-sinus and life-threatening rhythms) from such long-term data, particularly when targeting population-based research. In this work, we propose and test a new tool for analyzing beat-to-beat interval measurements and extracting features from Holter ECGs. Specifically, we assess the adaptation of the QT interval following sudden changes in heart rate in the primary long QT types (1 & 2). We find that in long QT syndrome type 2, certain QT adaptation patterns can indicate a higher risk for cardiac events.

The QT Scale: A Weight Scale Measuring the QTc Interval

INTRODUCTION

Despite the strong evidence of the clinical utility of QTc prolongation as a surrogate marker of cardiac risk, QTc measurement is not part of clinical routine either in hospital or in physician offices. We evaluated a novel device ("the QT scale") to measure heart rate (HR) and QTc interval.

METHOD

The QT scale is a weight scale embedding an ECG acquisition system with four limb sensors (feet and hands: lead I, II, and III). We evaluated the reliability of QT scale in healthy subjects (cohort 1) and cardiac patients (cohorts 2 and 3) considering a learning (cohort 2) and two validation cohorts. The QT scale and the standard 12-lead recorder were compared using intraclass correlation coefficient (ICC) in cohorts 2 and 3. Absolute value of heart rate and QTc intervals between manual and automatic measurements using ECGs from the QT scale and a clinical device were compared in cohort 1.

RESULTS

We enrolled 16 subjects in cohort 1 (8 w, 8 m; 32 ± 8 vs 34 ± 10 years, P = 0.7), 51 patients in cohort 2 (13 w, 38 m; 61 ± 16 vs 58 ± 18 years, P = 0.6), and 13 AF patients in cohort 3 (4 w, 9 m; 63 ± 10 vs 64 ± 10 years, P = 0.9). Similar automatic heart rate and QTc were delivered by the scale and the clinical device in cohort 1: paired difference in RR and QTc were -7 ± 34 milliseconds (P = 0.37) and 3.4 ± 28.6 milliseconds (P = 0.64), respectively. The measurement of stability was slightly lower in ECG from the QT scale than from the clinical device (ICC: 91% vs 80%) in cohort 3.

CONCLUSION

The "QT scale device" delivers valid heart rate and QTc interval measurements.

Comparing QT interval variability of semiautomated and high-precision ECG methodologies in seven thorough QT studies-implications for the power of studies intended for definitive evaluation of a drug's QT effect

BACKGROUND

In studies of drug effects on electrocardiographic parameters, the level of precision in measuring QTc interval changes will influence a study's ability to detect small effects.

METHODS

Variability data from investigational, placebo and moxifloxacin treatments from seven thorough QT studies performed by the same sponsor were analyzed with the objective to compare the performance of two commonly used approaches for ECG interval measurements: semiautomated (SA) and the high-precision QT (HPQT) analysis. Five studies were crossover and two parallel. Harmonized procedures were implemented to ensure similar experimental conditions across studies. ECG replicates were extracted serially from continuous 12-lead recordings at predefined time points from subjects supinely resting. The variability estimates were based on the time-point analysis of change-from-baseline QTcF as the dependent variable for the standard primary analysis of previous thorough QT studies. The residual variances were extracted for each study and ECG technique.

RESULTS

High-precision QT resulted in a substantial reduction in ∆QTc variability as compared to SA. A reduction in residual variability or approximately 50% was achieved in both crossover and parallel studies, both for the active comparison (drug vs. placebo) and for assay sensitivity (moxifloxacin vs. placebo) data.

CONCLUSIONS

High-precision QT technique significantly reduces QT interval variability and thereby the number of subjects needed to exclude small effects in QT studies. Based on this assessment, the sample size required to exclude a QTc effect >10 ms with 90% power is reduced from 35 with SA to 18 with HPQT, if a 3 ms underlying drug effect is assumed.

Fusing Continuous-Valued Medical Labels Using a Bayesian Model

With the rapid increase in volume of time series medical data available through wearable devices, there is a need to employ automated algorithms to label data. Examples of labels include interventions, changes in activity (e.g. sleep) and changes in physiology (e.g. arrhythmias). However, automated algorithms tend to be unreliable resulting in lower quality care. Expert annotations are scarce, expensive, and prone to significant inter- and intra-observer variance. To address these problems, a Bayesian Continuous-valued Label Aggregator (BCLA) is proposed to provide a reliable estimation of label aggregation while accurately infer the precision and bias of each algorithm. The BCLA was applied to QT interval (pro-arrhythmic indicator) estimation from the electrocardiogram using labels from the 2006 PhysioNet/Computing in Cardiology Challenge database. It was compared to the mean, median, and a previously proposed Expectation Maximization (EM) label aggregation approaches. While accurately predicting each labelling algorithm's bias and precision, the root-mean-square error of the BCLA was 11.78 ± 0.63 ms, significantly outperforming the best Challenge entry (15.37 ± 2.13 ms) as well as the EM, mean, and median voting strategies (14.76 ± 0.52, 17.61 ± 0.55, and 14.43 ± 0.57 ms respectively with p < 0.0001). The BCLA could therefore provide accurate estimation for medical continuous-valued label tasks in an unsupervised manner even when the ground truth is not available.

Man versus Machine: Comparison of Automated and Manual Methodologies for Measuring the QTc Interval: A Prospective Study

BACKGROUND

Electrocardiographic (ECG) safety evaluation is a required element of drug development. Performance characteristics of ECG measurement methodologies have rarely been studied prospectively.

METHODS

We conducted a randomized, placebo-controlled, crossover study in 24 subjects to evaluate effects of moxifloxacin on the Fridericia rate-corrected QT (QTcF) interval. Five ECG replicates were obtained at 30 time points. Change from baseline QTcF (ΔQTcF) was fit by mixed-model analysis of variance to evaluate residual standard deviation. Precision was defined as intrasubject QTcF variance. Two core lab approaches were compared: QTinno, fully automated, 5 replicates and HeartSignals, computer-assisted manual, 3 replicates. Core lab values were then compared to an automated commercial algorithm (VERITAS).

RESULTS

Twenty-three subjects provided 3450 ECGs potentially available for analysis. QTinno QTcF values were based upon 3419 ECGs, HeartSignals data on 2028 ECGs. Variance was similar between the QTinno and HeartSignals approaches (41.5 and 44 ms(2)). After excluding VERITAS QTcF measurements that deviated by >40 ms on visual review, variance in a set of 1907 common ECGs was lowest for HeartSignal, followed by QTinno and VERITAS (43.8, 52.6, 89.4 ms(2)) P = 0.02 HeartSignals versus QTinno, P < 0.0001 for both HeartSignals and QTinno versus VERITAS.

CONCLUSIONS

A fully automated core lab approach using 5 replicates and a computer-assisted manual approach using 3 replicates were equally precise. When an identical number of ECGs were compared, the computer-assisted manual method was most precise, while the commercial algorithm was relatively imprecise. Although suitable for clinical assessment the standard commercial algorithm cannot be recommended for regulated safety research.

Evaluation of differences in automated QT/QTc measurements between Fukuda Denshi and Nihon Koden systems

BACKGROUND

Automatic measurement becomes a preference, and indeed a necessity, when analyzing 1000 s of ECGs in the setting of either drug-inducing QT prolongation screening or genome-wide association studies of QT interval. The problem is that individual manufacturers apply different computerized algorithms to measure QT interval. We conducted a comparative study to assess the outcomes with different automated measurements of QT interval between ECG machine manufacturers and validated the related heart rate correction methods.

METHODS AND RESULTS

Herein, we directly compared these different commercial systems using 10,529 Fukuda Denshi ECGs and 72,754 Nihon Kohden ECGs taken in healthy Japanese volunteers. Log-transformed data revealed an equal optimal heart rate correction formula of QT interval for Fukuda Denshi and Nihon Kohden, in the form of QTc = QT/RR(-0.347). However, with the raw data, the optimal heart rate correction formula of QT interval was in the form of QTc = QT+0.156×(1-RR) for Fukuda Denshi and QTc = QT+0.152×(1-RR) for Nihon Kohden. After optimization of heart rate correction of QT interval by the linear regression model using either log-transformed data or raw data, QTc interval was ∼10 ms longer in Nihon Kohden ECGs than in those recorded on Fukuda Denshi machines. Indeed, regression analysis revealed that differences in the ECG machine used had up to a two-fold larger impact on QT variation than gender difference. Such an impact is likely to be of considerable importance when ECGs for a given individual are recorded on different machines in the setting of multi-institutional joint research.

CONCLUSIONS

We recommend that ECG machines of the same manufacturer should be used to measure QT and RR intervals in the setting of multi-institutional joint research. It is desirable to unify the computer algorithm for automatic QT and RR measurements from an ECG.

Use of ECG restitution (beat-to-beat QT-TQ interval analysis) to assess arrhythmogenic risk of QTc prolongation with guanfacine

BACKGROUND

Guanfacine (Intuniv) is a centrally active alpha-2A adrenergic agonist for the new indication of attention-deficit/hyperactivity disorder. QTc (QTcF and QTcNi) was prolonged at both therapeutic (4 mg) and supratherapeutic (8 mg) doses of a thorough QT study even though guanfacine has had a safe clinical history of over 3 million prescriptions for the treatment of hypertension. In an attempt to understand this disparity, retrospective evaluation of the continuous ECG data utilized dynamic beat-to-beat and ECG restitution analyses was performed.

METHODS

Sixty healthy subjects using 24-hour Holters were examined in a 3-arm, placebo- and positive-controlled, double-blind crossover study for effects on beat-to-beat QT, TQ, and RR intervals.

RESULTS

ECG restitution analyses indicated that, at all time points, a disproportionate effect to increase the TQ interval (rest) occurred more in relationship to each QT interval lengthening resulting in a placebo-adjusted reduced QT/TQ ratio of 21% after 4 mg and 31% after 8 mg (both antiarrhythmic responses). Additionally, the percentage of time and magnitude of stress on the heart, as measured by the upper limits of the QT/TQ ratio, were reduced with guanfacine by 22% to 24%. In contrast to guanfacine, moxifloxacin did not show a significant improvement in any restitution parameters but reflected a trend toward proarrhythmia with an increase in the QT/TQ ratio of up to 11%.

CONCLUSION

These results indicate that guanfacine causes a stabilizing effect on cardiac restitution that helps reconcile the clinical evidence for a lack of arrhythmia liability despite apparent increases in typical QT/QTc prolongation measures.

Comparison of automated measurements of electrocardiographic intervals and durations by computer-based algorithms of digital electrocardiographs

BACKGROUND AND PURPOSE

Automated measurements of electrocardiographic (ECG) intervals are widely used by clinicians for individual patient diagnosis and by investigators in population studies. We examined whether clinically significant systematic differences exist in ECG intervals measured by current generation digital electrocardiographs from different manufacturers and whether differences, if present, are dependent on the degree of abnormality of the selected ECGs.

METHODS

Measurements of RR interval, PR interval, QRS duration, and QT interval were made blindly by 4 major manufacturers of digital electrocardiographs used in the United States from 600 XML files of ECG tracings stored in the US FDA ECG warehouse and released for the purpose of this study by the Cardiac Safety Research Consortium. Included were 3 groups based on expected QT interval and degree of repolarization abnormality, comprising 200 ECGs each from (1) placebo or baseline study period in normal subjects during thorough QT studies, (2) peak moxifloxacin effect in otherwise normal subjects during thorough QT studies, and (3) patients with genotyped variants of congenital long QT syndrome (LQTS).

RESULTS

Differences of means between manufacturers were generally small in the normal and moxifloxacin subjects, but in the LQTS patients, differences of means ranged from 2.0 to 14.0 ms for QRS duration and from 0.8 to 18.1 ms for the QT interval. Mean absolute differences between algorithms were similar for QRS duration and QT intervals in the normal and in the moxifloxacin subjects (mean ≤6 ms) but were significantly larger in patients with LQTS.

CONCLUSIONS

Small but statistically significant group differences in mean interval and duration measurements and means of individual absolute differences exist among automated algorithms of widely used, current generation digital electrocardiographs. Measurement differences, including QRS duration and the QT interval, are greatest for the most abnormal ECGs.

The application of root mean square electrocardiography (RMS ECG) for the detection of acquired and congenital long QT syndrome

BACKGROUND

Precise measurement of the QT interval is often hampered by difficulty determining the end of the low amplitude T wave. Root mean square electrocardiography (RMS ECG) provides a novel alternative measure of ventricular repolarization. Experimental data have shown that the interval between the RMS ECG QRS and T wave peaks (RTPK) closely reflects the mean ventricular action potential duration while the RMS T wave width (TW) tracks the dispersion of repolarization timing. Here, we tested the precision of RMS ECG to assess ventricular repolarization in humans in the setting of drug-induced and congenital Long QT Syndrome (LQTS).

METHODS

RMS ECG signals were derived from high-resolution 24 hour Holter monitor recordings from 68 subjects after receiving placebo and moxifloxacin and from standard 12 lead ECGs obtained in 97 subjects with LQTS and 97 age- and sex-matched controls. RTPK, QTRMS and RMS TW intervals were automatically measured using custom software and compared to traditional QT measures using lead II.

RESULTS

All measures of repolarization were prolonged during moxifloxacin administration and in LQTS subjects, but the variance of RMS intervals was significantly smaller than traditional lead II measurements. TW was prolonged during moxifloxacin and in subjects with LQT-2, but not LQT-1 or LQT-3.

CONCLUSION

These data validate the application of RMS ECG for the detection of drug-induced and congenital LQTS. RMS ECG measurements are more precise than the current standard of care lead II measurements.

Lomitapide at supratherapeutic plasma levels does not prolong the Qtc interval--results from a TQT study with moxifloxacin and ketoconazole

BACKGROUND

The aim of this study was to assess the effect of high plasma levels of lomitapide and its main metabolite on ECG parameters.

METHODS

In this randomized five-way cross-over thorough QT study, 56 healthy subjects were enrolled. Study treatments were administered orally for 3 days in five separate periods in which subjects were dosed with (1) a single dose of 75 mg lomitapide on Day 1 followed by a single dose of 200 mg on Day 3; (2) ketoconazole 200 mg BID; (3) ketoconazole with a single dose of 75 mg lomitapide on Day 3; (4) a single dose of 400 mg moxifloxacin on Day 3 and (5) placebo.

RESULTS

Single doses of 75 and 200 mg lomitapide alone or in combination with ketoconazole caused minor changes in the change-from-baseline QTcI (ΔQTcI), whereas moxifloxacin and ketoconazole caused an increase of ΔQTcI with a peak effect at 1 and 3 hours postdosing, respectively. The largest mean placebo-corrected ΔQTcI (ΔΔQTcI) for lomitapide did not exceed 3 ms (upper bound of 90% CI: 4.7 ms) at any time points postdosing. Ketoconazole caused mild QT prolongation with mean ΔΔQTcI of 5.9 and 6.5 ms at 2 and 3 hours postdosing, and exposure-response analysis demonstrated a significantly positive slope of 1.3 ms per μg/mL (90% CI: 1.0-1.7). Moxifloxacin met the criteria for assay sensitivity.

CONCLUSIONS

Lomitapide does not have an effect on cardiac repolarization. The study's ability to detect small QTc changes was demonstrated with both moxifloxacin and ketoconazole.

Comparative TQT analysis with three fully-automated platforms: comparison to core laboratory semi-automated results

BACKGROUND AND PURPOSE

Technological advances in machine-read QT measurement now enable detailed and precise cardiac repolarization assessments. This study assessed the applicability of three state-of-art ECG measurement applications to provide reliable continuous analyses from data obtained in a positive thorough QT study previously characterized with sparse semi-automated measurements performed by an ECG core laboratory.

METHODS

Continuous RR, QT, QTc measurements, and individual QT/RR relationships and their associated intra- and inter-subject variability were derived in parallel with BioQT, Ponemah PRO, and WinAtrec analysis software.

RESULTS

Despite slight vendor-specific differences in measurement variability and QTc, all machine-read methods demonstrated requisite assay sensitivity and yielded similar conclusions in accordance with SA analysis.

CONCLUSIONS

Three commercially available ECG analytical software applications reliably detected the drug-induced QT prolonging effects and replicated the SA core-laboratory conclusions, with greatly improved temporal resolution and reduced analytical costs. With broader experience, these data suggest that current SA methodologies could be effectively replaced by fully automated ECG analysis.

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.).

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

BACKGROUND

There is a consensus on the limited value of the QTc interval prolongation as a surrogate marker of drug cardiotoxicity and as a risk stratifier in inherited long QT syndrome (LQTS) patients.

OBJECTIVE

We investigated the interest of repolarization morphology in the acquired and the inherited LQTS.

METHODS

We analyzed 2 retrospective electrocardiographic (ECG) datasets from healthy on/off moxifloxacin and from genotyped KCNH2 patients. We measured QT, RR, and T-peak to T-end intervals, early repolarization duration (ERD) and late repolarization duration, T-roundness, T-amplitude, left (αL) and right slopes of T-waves. We designed multivariate logistic models to predict the presence of the KCNH2 mutation or moxifloxacin while adjusting for the level of QTc prolongation and the level of heart rate in LQT2 patients. Independent learning and validation sets were used. A list of 4,874 ECGs from 411 healthy individuals, 293 from 143 LQT2 carriers and 150 noncarrier family members were analyzed.

RESULTS

In the moxifloxacin model, ERD was associated with the presence of the drug (odds ratio = 1.15 per ms increase, confidence interval 1.04 to 1.26, P = .0001) after adjustment for QTc. The model for the LQT2 revealed that left slope was associated with the presence of the KCNH2 mutation (odds ratio = 0.38 per 1.5 μV/ms decrease, confidence interval 0.23 to 0.64, P = .0002). Only T-roundness complemented QTc in the model investigating cardiac events in LQT2.

CONCLUSIONS

These observations demonstrate that the phenotypic expression of KCNH2 mutations and the effect of IKr-inhibitory drug on the surface electrocardiogram are specific. Future research should investigate whether this phenomenon is linked to different level/form of loss functions of Ikr channels, and whether they could result in different arrhythmogenic mechanisms.