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BackgroundCorrected QT (QTc) is an important electrocardiographic (ECG) interval. Physicians rely on automated QTc provided by ECG machines while the manual method is the recommended method. We sought to assess the prevalence and predictors of false positive QTc prolongation by the automated measurement.Methods and resultsConsecutive ECGs were retrieved from the ECG database at King Khaled University Hospital. Manual QT was measured by a trained physician using the tangent method and was corrected for heart rate and QRS duration. Automated QTc measurement was recorded by the ECG machine. “Long QT (LQT)” was defined as QTc≥470 ms for males and ≥480 for females. False positive LQT was defined as LQT by automated QTc but not manual QTc. Pre-determined factors were included in a multivariate logistic regression to assess predictors of false positive LQT. A total of 567 ECGs were included in this study. Automated QTc was longer than manual QTc (440 ms [±35] vs. 417 ms [±35], respectively) which resulted in a high negative predictive value (NPV) (99%) and a low positive predictive value (PPV) (32%). Male gender and abnormal rhythm were found to be independently associated with false positive LQT (OR = 1.9 [95% 1.1–3.5], p = 0.03 and OR = 3.1 [95% 1.2–8.3], p = 0.02; respectively).ConclusionAutomated QTc measurement is unreliable for detecting long QT, necessitating manual verification and further research to enhance its accuracy.

Introduction: Torsade de pointes (TdP) is a deadly complication from drug-induced QT prolongation. Each of the 12 lead of an electrocardiogram (ECG) has a different length of QT interval, and thus might have a different performance in TdP prediction. This study aimed to determine the best ECG lead or set of leads in this regard. Methods: This is a comparative prognostic accuracy study using a two-gate data gathering design. The population in this study was from two sources, a case group (Patients who had drug-induced TdP, which were identified through a systematic Medline search) and a control group (those who overdosed on QT-prolonging drugs, which included patients who were under the consultation of Medical Toxicology Services). The areas under the receiver operating characteristic curve (AUROC) of heart rate-corrected QT (QTc) in each single ECG lead and of a mean/median QTc from a set of ECG leads (17 index test) in predicting the risk of TdP were calculated and compared with each other, trying to find the best lead for this propose. QTc Interval measurements were done by four investigators (Interrater reliabilities 0.95). Results: Finally, we included 136 and 148 ECGs from TdP cases and controls, respectively. V3 lead had the highest frequency of longest QTc interval, among the leads. The lead having the longest QTc yielded the greatest AUROC in predicting TdP regardless of QT correction formulas (QTcFRA=0.9915, QTcRTH=0.9893, QTcBZT=0.9904). The mean QTc of 3 leads (lead II, plus any two of leads V2-V4), and a median QTc of 6 leads (I, II, aVF, V2, V4, V6) provided similar overall performance for TdP prediction (regardless of the type of QTc formula). Conclusion: The longest QTc provided the greatest AUROC in predicting drug-induced TdP, however, the longest QTc is not located in a fixed individual lead in any patient. A less time-consuming method with comparable performance to that of the longest QTc was to use a mean QTc from 3 leads (lead II, plus any two of leads V2-V4). The potential clinical impact of this finding needs to be verified in a prospective cohort study.

Competing Interests: All authors declare that they have no conflicts of interest.

Pharmaceuticals that prolong ventricular repolarization may be proarrhythmic in susceptible patients. While this fact is well recognized, schemes for sequential QTc interval monitoring in patients receiving QT-prolonging drugs are frequently overlooked or, if implemented, underutilized in clinical practice. There are several reasons for this gap in day-to-day clinical practice. One of these is the perception that serially measured QTc intervals are subject to substantial variability that hampers the distinction between potential proarrhythmic signs and other sources of QTc variability. This review shows that substantial part of the QTc variability can be avoided if more accurate methodology for electrocardiogram collection, measurement, and interpretation is used. Four aspects of such a methodology are discussed. First, advanced methods for QT interval measurement are proposed including suggestion of multilead measurements in problematic recordings such as those in atrial fibrillation patients. Second, serial comparisons of T-wave morphologies are advocated instead of simple acceptance of historical QTc measurements. Third, the necessity of understanding the pitfalls of heart rate correction is stressed including the necessity of avoiding the Bazett correction in cases of using QTc values for clinical decisions. Finally, the frequently overlooked problem of QT-heart rate hysteresis is discussed including the possibility of gross QTc errors when correcting the QT interval for simultaneously measured short-term heart rate. [ABSTRACT FROM AUTHOR]

Objective: The objective of the study was to determine the optimal formula to estimate QT interval adjusting for QRS prolongation during right ventricular (RV) pacing.Methods: This observational study included individuals (n = 43) with a newly implanted permanent ventricular pacemaker, who had a narrow QRS complex before pacemaker insertion. QT interval with RV pacing was related to QT interval before pacemaker implantation. The validation cohort (n = 442) had permanent RV pacing in DDD mode.Results: A new QTc formula was derived utilizing the constants from the relationship between the spline heart rate QT correction (QTcRBK) before and after pacing; specifically, QTcRBKPACED = QTcRBK × 0.86. The JT interval from paced complexes was highly heart rate (HR) dependent and was not accurate for QT assessment. Previous, QTc formula for paced complexes were not highly correlated with QT before pacing unless a robust HR correction is added. Formulae subtracting a fixed amount from QTcPACED markedly overestimated QTc before pacing.Conclusion: We proposed a new, simple formula for QT estimation in RV pacing. JT interval in paced complexes is highly HR dependent and is not accurate for QT assessment. The new spline approach for HR correction for the QT, once incorporated into some previously proposed formulae, blunts HR dependency and improves prediction of QT before pacing. QTcRBKPACED*0.86 and QTcRBKPACED - (QRS*0.5) demonstrated the best balance of relatively strong correlation to QTc before pacing and accurate QTc prolongation identification. Abnormal QT for QTcRBKPACED*0.86 as defined by the 97.5th and 99th percentile are 469 and 479 ms respectively. [ABSTRACT FROM AUTHOR]

The QT interval is measured manually or automatically. In comparison with manual methods, the automated ones offer advantages in terms of absolute repeatability of measurements, immunity from errors related to observer fatigue, lack of attention, as well as efficiency and cost effectiveness that permits either more extensive and rigorous testing for the same cost as manual methods, or more rapid testing at lower cost. But a question arises: 'Can the QT interval be measured by fully automated methods with accuracy acceptable for clinical evaluations?' We created a dataset of manually measured Q-onsets and T-ends for the PTB Diagnostic ECG Database. Further on we developed a fully automated method for QT measurements and forwarded it to PhysioNet/Computers in Cardiology Challenge, 2006. The manually measured dataset was then used as a 'gold standard' for assessment of the accuracy of the automated method. The current lecture notes summarize all our up to date publications on the QT measurements topic. Sources of variation in the QT readings are for the first time discussed by the authors.

Cardiovascular safety is a key area of concern for new drugs in development, and the collection and analysis of electrocardiograms (ECGs) is a standard and major component of nonclinical testing. Digital data capture technology allows for high-throughput and long-duration ECG collections, resulting in large volumes of data. Consistent analysis of these ECG data is critical for detecting meaningful changes during nonclinical drug development. We developed a method to assess the consistency of nonclinical ECG analysis for a group of analysts over time. Eight‑lead ECGs were collected from conscious dogs using Ponemah (v5.2, DSI). Analysts placed P start , Q start , R peak , S end , and T end marks on six waveforms for each animal. The ECG files were randomized and re-marked under blinded conditions 4 to 14 days following initial mark placement. Averages of each parameter measured (RR interval, QRS duration, PR interval, and QT interval) were compiled for each marking session and analyst. A Gage R&R evaluation was completed. Graphical output from the Gage R&R evaluation showed distinct variability on group and individual analyst levels. Differences in inter- and intra-analyst variability (reproducibility and repeatability, respectively) were observed between trained analysts and analysts in training. The Gage R&R method is an effective tool for assessing consistency of digital ECG mark placement at a group level. Furthermore, it is able to identify areas of improvement for individual ECG analysts and to assess ECG analyst consistency during their training period. The assessment results are useful for facilitating discussions on best practices and maintaining consistency of mark placement. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

–Open–loop linear parametric models were exploited to describe ventricular repolarization duration (VRD) variability during graded head–up tilt. Surface ECG and thoracic movements were recorded in 15 healthy humans (age: 24–54 yr, median: 28 yr; 6 women and 9 men). Tilt table inclinations ranged from 15 to 90° and were varied in steps of 15°. All subjects underwent recordings at every step in random order. Heart period was assessed as the time difference between two consecutive R–wave peaks (RR) and the respiratory signal (R) as the sampling of the thoracic movement signal at the R–wave peaks. VRD was measured automatically as the temporal difference between the R–wave peak and T–wave apex (RTa) or T–wave end (RTe). The best model decomposed RT variability as due to RR changes (RR–related RT variability) to direct respiratory–related inputs (R–related RT variability) and to unknown rhythmical sources unrelated to RR changes and R (RR–R–unrelated RT variability). Using this model, RTe variability was found to be less predictable than RTa variability and composed of a smaller fraction of RR–related RT variability and a larger fraction of RR–R–unrelated RT variability. Predictability progressively decreased with tilt table angles, suggesting increased complexity of RT regulation. RT variance progressively increased with tilt table inclination. This increase was characterized by a gradual rise of the amount of RR–R–unrelated RT variability, whereas the amount of RR–related RT variability remained unchanged. These results suggest that the amount of RT variability, complexity of RT dynamics, and amount of RR–Runrelated RT variability increase with the magnitude of the sympathetic drive directly related to tilt table inclination. We propose the utilization of the amount of RR–R–unrelated RT variability instead of overall RT variability as an indirect measure of autonomic regulation directed to ventricles. [ABSTRACT FROM AUTHOR]

The article presents a study which compares the corrected Q-T interval (QTc) prolongation by a quinolone antibiotic detected by the 2 semiautomated QT measurement methods in phase I study in healthy participants. The prolongation of drug-induced QTc is a critical issue for the outcome of the regulatory review of new drug applications. It notes that the assessment of QTc in phase I can facilitate early decisions on the viability of drug development and optimal timing of thorough QTc study.

Objective: To establish a general method to estimate the measuring error in QT dispersion (QTD) determination, and to assess this error using a computer program for automated measurement of QTD.Subjects: Measurements were done on 1220 standard simultaneous 12 lead electrocardiograms.Design: The computer program was validated against two observers on a random subset of 100 electrocardiograms. Simple laws of physics require that at least five of the six extremity leads have the same QT duration. This allows the direct assessment of the error in measuring QTD derived from five extremity leads (QTD5). It also enables ST-T amplitude dependent distributions of measurement error in determining QT duration to be established. These QT error distributions were then used to estimate the error in measuring QTD from all 12 leads (QTD12).Main Outcome Measures: Mean and standard deviation of error in measuring QTD duration, QTD5, and QTD12.Results: Performance of the program was comparable to that of observers. Errors in measuring QT duration (measured QT minus reference QT) fell from a mean (SD) of 6.9 (17.1) ms for ST-T amplitudes < 50 microV to -1.4 (6.3) ms for amplitudes > 350 microV. Measurement errors of QTD5 and QTD12 were 20.4 (11.5) ms and 29.4 (14.9) ms.Conclusions: The fact that no QTD can exist between five of the six extremity leads provides a means of estimating QTD measurement error. Measuring error of QT duration is dependent on ST-T amplitude. QTD measurement error is large compared with typical QTD values reported. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

One of the most feared potential adverse effects of many drugs is life threatening or fatal arrhythmia — particularly torsade de pointes (TdP) ventricular tachycardia in conjunction with QT prolongation. To fully understand the implications of QT prolongation, it is essential to have an understanding of the ion currents that comprise repolarization and their relation to electrophysiological abnormalities associated with TdP. Also, the QT interval is subject to patient-specific and sometimes idiosyncratic variability. The following questions are addressed: How close is the relationship between QT prolongation and proarrhythmia? How accurately do QT-interval measurements reflect cardiac repolarization? How representative is a single QT measurement with respect to the QT response to a drug? The presumed relationship between the QT interval and myocardial repolarization will be deconstructed, demonstrating that most of the important aspects of repolarization, and subsequent arrhythmogenesis, cannot be understood only through the simple numerical measurement of the QT interval. Repolarization reserve is also discussed. Suggestions for refining the understanding of drug-induced QT prolongation, TdP, and shortcomings of some current definitions are outlined. We speculate on possible future developments in understanding this relationship. [ABSTRACT FROM PUBLISHER]