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Background Improved automated methods for electrocardiographic (ECG) analysis are needed, particularly for drug development purposes. Objectives This study compared a novel fully automated method for ECG analysis (QTinno; NewCardio, Santa Clara, CA) to 2 semiautomated digital methods: global measurement from the earliest QRS onset to the latest T-wave offset on representative superimposed beats (global) and tangent measurement on 3 consecutive beats in one lead (tangent). Methods All 3 methods were used to determine uncorrected and rate-corrected QT interval duration (QT and QTcF) and related metrics in 1422 digital 12-lead ECGs from a phase 1 drug study. Global and tangent annotations were manually adjusted by the same 3 cardiologists wherever necessary. No adjustments were made in QTinno determinations. Results QTinno returned QTcF change from time-matched baseline (ΔQTcF) that differed minimally from both global and tangent methods (mean pairwise difference: 0.1 millisecond between QTinno and global, 1.1 milliseconds between QTinno and tangent). The average absolute QT and QTcF intervals by QTinno were approximately 5 milliseconds longer than global and 25 milliseconds longer than by tangent. QTinno had lower intrinsic variability for ΔQTcF than either global or tangent (between-subject SD: QTinno 4.0 milliseconds, global 5.6 milliseconds, tangent 6.4 milliseconds; within-subject SD: QTinno 4.8 milliseconds, global 7.4 milliseconds, tangent 10.6 milliseconds). All methods were robust in detecting the largest placebo-adjusted mean time-matched ΔQTcF (15-25 milliseconds) induced by study drug. Conclusions The methods show good agreement for drug-induced QTc prolongation. Lower intrinsic variability of ΔQTcF by QTinno could facilitate smaller sample sizes or increase study power in thorough QTc studies.

A novel fully automated method for wave identification and extraction from electrocardiogram (ECG) waveforms is presented. This approach implements the combined use of a new machinelearning algorithm and of specified parameterized functions called Gaussian mesa functions (GMFs). Individual cardiac cycle waveforms are broken up into GMFs using a generalized orthogonal forward regression algorithm; each individual GMF is subsequently identified (wave labeling) and analyzed for feature and morphologic extraction. The GMF associated with the repolarization waveform of the main vector lead, based on principal components analysis, was analyzed, and a set of morphologic parameters were derived under 2 experimental settings: first, in 100 digital 12-lead ECG Holter recordings acquired during three 24-hour periods (baseline and after 160 and 320 mg of sotalol) from 38 healthy subjects; second, in drug-free 12-lead resting ECGs from 100 genotyped long QT syndrome (LQTS) patients (50 each with LQT1 and LQT2). QTinterval duration was measured using an on-screen method applied to the global representative beats and reviewed by a senior cardiologist. QTci (individual correction) was used for analysis. All parameters in the sotalol test showed highly significant differences between the time of peak plasma concentration (Tmax) and baseline ECGs; however, the dynamic pattern of individual parameters followed different patterns. The LQTS test confirmed the results of the sotalol test, showing that GMF-based repolarization parameters were strongly modified as compared with healthy controls. In particular, T-wave width and descending phase of repolarization were more prolonged in LQT2 compared to LQT1. © 2008 Elsevier Inc. All rights reserved.

Background: The dissociation between a drug-induced increase of the QT interval prolongation and an increased risk for ventricular arrhythmias has been suggested by academic investigators and regulatory agencies. Yet, there are no alternative or complimentary electrocardiographic (ECG) techniques available for assessing the cardiotoxicity of novel compounds. In this study, we investigated a set of novel ECG parameters quantifying the morphology of the T-loop. In a group of healthy individuals exposed to sotalol, we compared their drug-induced changes to the drug-induced prolongations of the QTc, QTc apex and T-peak to T-end intervals. Methods: We implemented a set of parameters describing the morphology of the T loop in its preferential plane. These parameters measure the time interval needed for the heart vector amplitude to change from its maximum value to a time when its amplitude has been reduced by 30%, 50%, and 70%. These measurements are called early repolarization duration (ERD) when they are located before the T-wave apex and late repolarization duration (LRD) when measured after the apex. They depend on both the speed of the repolarization process and the morphology of the T loop. Thirty-nine healthy individuals were exposed to sotalol in a crossover-design study. Sixteen ECGs were recorded per day during 3 days. The first day (day 0) was baseline; a single dose of sotalol (160 mg) was given during day 1, and a double dose was given during day 2 (320 mg). The plasma concentration of the drug was measured just before the ECG recordings. Results: The values of all investigated parameters revealed a dose-dependent effect of sotalol (in average between parameters, ρ = 0.9, P b .001). Our investigations described profound and statistically significant changes in the morphology of the vectorial T loop for day 1 (peak effect of sotalol: ΔERD50% = 23 ± 6 msec, P b.05; ΔLRD50% = 8 ± 3 msec, P = .05) and day 2 (peak effect of sotalol: ΔERD50% = 51 ± 14 msec, P b .05; ΔLRD50% = 20 ± 12 msec, P = .05). When investigating the timing of peak drug concentration and peak effect of the drug on the various repolarization parameters, we found asynchrony between ERDs/LRDs (≥3.5 hours after dosing) and QTc/QTc apex profiles (b3.5 hours after dosing), suggesting that the time of maximum prolongation on the repolarization process was not synchronized with the time of maximum drug-induced heterogeneity of repolarization. Conclusion: This study describes the sotalol-induced changes of the T-loop morphology in healthy individuals based on novel vectocardiographic parameters. These observations might help in improving the next generation of ECG markers for the evaluation of drug cardiotoxicity.

In this study, we investigated the validity of T wave area-based parameters for the identification of drug-induced changes of repolarization. Based on electrocardiograms from 39 healthy patients, we computed the stability of repolarization measurements and compared the sotalol-induced repolarization changes when measured with area-based parameters and traditional QT interval techniques (manual and automatic). Also, we evaluated the effect of different types of heart rate correction on these repolarization measurements. The results show that the stability of the automatic repolarization measurements is higher when measured using computer algorithm than using manual QT measurement. By using a population-based heart rate correction, the area-based parameters reveal significant modification of the overall shape of the T wave in its early, middle, and final part. In conclusion, morphological parameters of T wave are able to identify the changes in repolarization interval induced by sotalol. These parameters are more stable and thus more reliable than the traditional QT interval measurements.

Well-specified recommendations have yet to be established on how electrocardiogram (ECG) interval measurement should be performed by digital on-screen caliper systems to assess drug-induced effect on cardiac repolarization in pharmaceutical clinical trials with adequate precision and reproducibility. Since 1997, the industry has followed the European Committee for Proprietary Medicinal Products Points to Consider by using fully manual measurement of 3 consecutive sinus rhythm PQRST complexes in 1 lead only (typically limb lead II). More recently, semiautomatic measurement performed on representative (median) beats and based on the global leads has been considered. The International Conference on Harmonization E14 guidance (June 2005) advocates development of quality standards for centralized ECG interval measurement and allows all methods "whether or not assisted by computer" but includes no recommendations on how to perform the measurement. We provide an overview of the currently available methods for digital ECG interval measurement and the implications of between-method differences on quality of ECG interval measurements. We applied 4 methods most commonly used to assess QT prolongation (applied on 3 raw beats in limb lead II or by global measurement on 1 or 12 superimposed representative beats). QT, QTc Fridericia, and RR interval durations were measured on resting 12-lead digital ECGs obtained in 26 healthy volunteers predose and at 1, 2, and 3 hours after dosing with a single 160 mg oral dose of sotalol. Absolute interval durations and changes from baseline were compared between the 4 measurement methods. A better understanding of the implications from different measurement methodologies will facilitate more informed choice of the appropriate method for ECG interval measurement on clinical trials.