Comparison of two methods of estimating reader variability in QT interval measurements in thorough QT/QTc studies.

BACKGROUND: Two methods of estimating reader variability (RV) in QT measurements between 12 readers were compared. METHODS: Using data from 500 electrocardiograms (ECGs) analyzed twice by 12 readers, we bootstrapped 1000 datasets each for both methods. In grouped analysis design (GAD), the same 40 ECGs were read twice by all readers. In pairwise analysis design (PAD), 40 ECGs analyzed by each reader in a clinical trial were reanalyzed by the same reader (intra-RV) and also by another reader (inter-RV); thus, variability between each pair of readers was estimated using different ECGs. RESULTS: Inter-RV (mean [95% CI]) between pairs of readers by GAD and PAD was 3.9 ms (2.1-5.5 ms) and 4.1 ms (2.6-5.4 ms), respectively, using ANOVA, 0 ms (-0.0 to 0.4 ms), and 0 ms (-0.7 to 0.6 ms), respectively, by actual difference between readers and 7.7 ms (6.2-9.8 ms) and 7.7 ms (6.6-9.1 ms), respectively, by absolute difference between readers. Intra-RV too was comparable. CONCLUSIONS: RV estimates by the grouped- and pairwise analysis designs are comparable.

Reader variability in QT measurement due to measurement error and variability in leads selection: a simulation study comparing 2-way vs. 3-way interaction ANOVA model.

Reader variability (RV) results from measurement differences or variability in lead used for QT measurements; the latter is not reflected in conventional methods for estimating RV. Mean and SD of QT intervals in 12 leads of 100 ECGs measured twice were used to simulate data sets with inter-RV of 5, 10, 15, 20, and 25 ms and intra-RV of 3, 6, 9, 12, and 15 ms. Six hundred twenty-five data sets were simulated such that different leads were used in Read1 and Read2 in 0, 10%, 20%, 30%, 40% of ECGs by 25 readers. RV was estimated using ANOVA interaction models: three-way model using Reader, ECG and lead as factors, and 2-way model using reader and ECG as factors. Estimates from three-way model accurately matched inter- and intra-RV that were introduced during simulation regardless of percent of ECGs with lead selection variability. The two-way model provides identical estimates when both reads are in same leads, but higher, more realistically estimates when measurements are made in different leads.

Choice of an alternative lead for QT interval measurement in serial ECGs when Lead II is not suitable for analysis.

INTRODUCTION: Conventionally, QT interval is measured in lead II. There are no data to select an alternative lead for QT measurement when it cannot be measured in Lead II for any reason. METHODS AND RESULTS: We retrospectively analyzed ECGs from 1906 healthy volunteers from 41 phase I studies. QT interval was measured on the median beat in all 12 leads. The mean difference in QT interval between lead aVR and in Lead II was the least, followed by aVF, V5, V6 and V4; lead aVL had maximum difference. The T wave was flat (<0.1 mV) in Lead II in 6.9% of ECGs; it was also flat in 20% of these ECGs (1.4% of all ECGs) in Leads aVR, aVF and V5. CONCLUSIONS: When QT interval cannot be measured in Lead II, the best alternative leads are aVR, aVF, V5, V6 and V4 in that sequence. It differs maximally from that in Lead II in Lead aVL.

Comparison of 5 methods of QT interval measurements on electrocardiograms from a thorough QT/QTc study: effect on assay sensitivity and categorical outliers.

INTRODUCTION: We studied moxifloxacin-induced QT prolongation and proportion of categorical QTc outliers when 5 methods of QT measurement were used to analyze electrocardiograms (ECGs) from a thorough QT study. METHODS: QT interval was measured by the threshold, tangent, superimposed median beat, automated global median beat, and longest QT methods in a central ECG laboratory in 2730 digital ECGs from 39 subjects during placebo and moxifloxacin treatment. RESULTS: All 5 methods were able to demonstrate statistically significant moxifloxacin-induced QTcF prolongation. However, lower bound of 95% 1-sided confidence interval of QTcF prolongation did not exceed 5 milliseconds with the longest QT method. More QTcF outliers were observed with the longest QT and tangent methods, whereas the other 3 methods were comparable. QTcF values greater than 500 milliseconds were observed only with moxifloxacin by the tangent method, and with moxifloxacin and placebo by the longest QT method. CONCLUSION: The method of QT measurement must be considered when interpreting individual thorough QT/QTc studies.

Arrhythmias Seen in Baseline 24-Hour Holter ECG Recordings in Healthy Normal Volunteers During Phase 1 Clinical Trials.

Regulatory agencies encourage sponsors to submit 24-hour ambulatory ECG data for assessing cardiac safety of new drugs, and some arrhythmias, hitherto considered rare, have been observed in some early-phase studies. Interpretation of these observations is difficult given the dearth of published data on the prevalence of cardiac arrhythmias seen during 24-hour continuous ECG monitoring in healthy volunteers (HV) from clinical trials. We analyzed drug-free ambulatory ECG recordings from 1273 HV (1000 males, 273 females; age 18-65 years) from 22 phase 1 studies that were analyzed in a core ECG laboratory; all subjects had normal screening ECGs. Supraventricular arrhythmias such as supraventricular premature complexes were observed in 60.8% of healthy volunteers, supraventricular tachycardia in 2.2%, and atrial fibrillation in 0.1%. Ventricular arrhythmias included premature ventricular complexes (PVCs) in 43.4%, >200 PVCs per 24 hours in 3.3%, multifocal PVCs in 5.3%, nonsustained ventricular tachycardia in 0.7%, and accelerated idioventricular rhythm in 0.3%. Bradyarrhythmias included sinus pause >3 seconds in 0.3%, and second-degree AV block in 2.4%. Complete heart block and torsades de pointes were not seen in any subject. Based on the observed incidence, we estimated the maximum number of healthy subjects in whom these arrhythmias may be seen as a matter of chance in studies with smaller sample sizes if the study drug has no arrhythmogenic effect. Our results and these estimates could help interpret whether cardiac arrhythmias observed in early-phase studies are due to chance or possibly are a drug effect.