Reproducibility of Minimum,Maximum and Median QT Intervals in the 12‐Lead Resting ECG

Background: Heterogeneity in the recovery of ventricular refractory periods is an important factor in the development of ventricular arrhythmia. The QT dispersion (QTD) is increasingly used to measure this heterogeneity but its clinical value is limited due to methodological problems. QTD is defined as the maximum minus the minimum QT intervals that are suspected to be the least reproducible of the QT measurements. Objective: To analyze the reproducibility of the minimum, maximum and median QT intervals. Material: One database consisted of 356 subjects: 169 with diabetes and 187 nondiabetic control persons. The other database consisted of 110 subjects with remote myocardial infarction: 55 with no history of arrhythmia, and 55 with a recent history of ventricular tachycardia or fibrillation. Methods: 12‐lead surface ECGs were recorded with an amplification of 10 millimeters per millivolt at a paper speed of 50 mm/s. QT was measured manually by the tangent‐method. The reproducibility was calculated from measurements of QT in successive beats. Results: The standard deviation (SD) of QTs reproducibility was 9 ms for the arrhythmia data and 8 ms for the diabetes data. The reproducibility of QT_max and QT_min were on average 30% and 15% worse than for QT_median. The SD of QT_max was significantly higher than for QT_median in both database (P < 0.001), whereas SD of QT_min was only significantly higher than for QT_median for the diabetes data (P < 0.001). Conclusions: The reproducibility of QT_min and in particular QTmax was significantly lower than for QT_median. This indicates that the QT dispersion is based on the least reproducible of the QT measurements. A.N.E. 2000;5(4):354–357     

Is QT Dispersion Associated with Sudden Cardiac Death in Patients with Hypertrophic Cardiomyopathy?

QT dispersion is significantly greater in patients with hypertrophic cardiomyopathy (HCM) than that in healthy subjects. Few data exist regarding the prognostic value of QT dispersion in HCM. In this study, we retrospectively investigated the association between QT dispersion and sudden cardiac death in 46 patients with HCM (mean 33.1 ±; 15.5 years, 32 men). The case group consisted of 23 HCM patients who died suddenly, and the control group consisted of 23 HCM patients who survived uneventfully during follow‐up. Study patients were pair‐matched for age, gender, and maximum left ventricular wall thickness. QT dispersion (maximum minus minimum QT interval) was manually measured on early 12‐lead ECGs using a digitizing; board. An in‐house program was used for calculating QT interval, QT dispersion, JT interval, and JT dispersion (maximum minus minimum J point to T end interval). Patients in the case group tended to have shorter RR intervals than those in the control group (777 ±; 171 vs 856 ±; 192 ms, P = 0.08). Maximum corrected QT and JT intervals did not discriminate the case group from controls (489 ±; 29 vs 479 ±; 27 ms, P = NS; 375 ±; 36 vs 366 ±; 22 ms, P = NS, respectively). Greater QT dispersion and JT dispersion were found in the case group compared with controls (74 ±; 28 vs 59 ±; 21 ms, P = 0.02 and 76 ±; 32 vs 59 ±; 26 ms, P = 0.03, respectively). The measurements of maximum QT, JT, and T peak to T end intervals, precordial QT and JT dispersion, and T peak and T end dispersion were all comparable between the two groups (P = NS for all). No systematic changes in ECG measurements were found from late ECGs of the case group compared to those from early ECGs (P = NS). No correlation between maximum left ventricular wall thickness and QT dispersion, JT dispersion, maximum QTc or JTc intervals was observed (r < 0.29, P > 0.05 for all). Our results; show that increased QT dispersion and JT dispersion is weakly associated with sudden cardiac death in the selected patients with HCM. A.N.E. 2001; 6(3):209–215     

Seasonal Variability of QT Dispersion in Healthy Young Males

Background: There are few data related to the seasonal influences on the QT dispersion. Methods: We analyzed the effects of seasons on QT dispersion in a large group of healthy young males. We studied the seasonal variability of QT dispersion in 523 healthy male subjects aged 22 ± 4 years (ranging from 20 to 26). Four seasonal 12‐lead resting electrocardiograms (ECGs) recorded at double amplitude were performed at 25 mm/s at intervals of 3 months. Subsequent ECGs were recorded within 1 hour of the reference winter recording. QT dispersion was defined as the difference between the longest and the shortest mean QT intervals. Results: There was a significant seasonal variation in QT dispersion (P = 0.001) , with the largest QT dispersion in winter (71 ± 18 ms) and the smallest one in spring (43 ± 19) . Conclusion: There exists a significant seasonal variation in QT dispersion of healthy subjects and such variability should be taken into consideration in the evaluation process of QT dispersion.     

Improving the Reproducibility of QT Dispersion Measures

Background: The low reproducibility of the QT dispersion (QTD) method is a major reason why it is not used in clinics. The purpose of this study was to develop QT dispersion parameters with better reproducibility and identification of patients with a high risk of ventricular arrhythmia or death. Methods and Results: Three institutions using different methods for measuring QT intervals provided QT databases, which included more than 3500 twelve‐lead surface ECGs. The data represented low and high risk subjects from the following groups: the normal population EpiSet (survivors vs dead from cardiovascular causes), acute myocardial infarction patients AmiSet (survivors vs dead) and remote myocardial infarction patients ArrSet (with vs without a history of ventricular arrhythmia). The EpiSet, AmiSet, and the ArrSet contributed with N = 122, 0, and 110 ECGs for reproducibility analysis, and 3244, 446, and 100 ECGs for the analysis of prognostic accuracy. The prognostic accuracy was measured as the area under the Receiver Operator Curve. The QT intervals were divided into six QT pairs; the longest pair consisted of the longest and the shortest QT intervals etc. The QT dispersion trend (QTDT) was defined as the slope of the linear regression of the N longest QT pairs after estimation of missing QT intervals by interpolation of measured QT intervals. The QTMAD and the QTSTD methods were defined as twice the mean absolute deviation and the standard deviation of the N longest QT pairs. The reproducibility was improved by 27% and 19% in the EpiSet and the ArrSet relative to the reproducibility of QTD. The accuracy improved for the EpiSet and the ArrSet and was maintained for the AmiSet. Conclusions: By using the three longest and the three shortest QT intervals in QTDT, QTMAD, or QTSTD, the reproducibility improved significantly while maintaining or improving the prognostic accuracy compared to QTD. A.N.E. 2001;6(2):143–152     

Interobserver Reproducibility of QT Interval Measurement and QT Dispersion in Patients After Acute Myocardial Infarction

Background: The study evaluated interobserver differences in the classification of the T-U wave repolarization pattern, and their influence on the numerical values of manual measurements of QT interval duration and dispersion in standard predischarge 12-lead ECGs recorded in survivors after acute myocardial infarction. Methods: Thirty ECGs recorded at 25 mm/s were measured by six independent observers. The observers used an adopted scheme to classify the repolarization pattern into 1 of 7 categories, based on the appearance of the T wave, and/or the presence of the U wave, and the various extent of fusion between these. In each lead with measurable QRST(U) pattern, the RR, QJ, QT-end, QT-nadir (i.e., interval between Q onset and the nadir or transition between T and U wave) and QU interval were measured, when applicable. Based on these measurements, the mean RR interval, the maximum, minimum, and mean QJ interval, QT-end and/or QT-nadir interval, and QU interval, the difference between the maximum and minimum QT interval (QT dispersion [QTD]), and the coefficient of variation of QT intervals was derived for each recording. The agreement of an individual observer with other observers in the selection of a given repolarization pattern were investigated by an agreement index, and the general reproducibility of repolarization pattern classification was evaluated by the reproducibility index. The interobserver agreement of numerical measurements was assessed by relative errors. To assess the general interobserver reproducibility of a given numerical measurement, the coefficient of variance of the values provided by all observers was computed for each ECG. Statistical comparison of these coefficients was performed using a standard sign test. Results: The results demonstrated the existence of remarkable differences in the selection of classification patterns of repolarization among the observers. More importantly, these differences were mainly related to the presence of more complex patterns of repolarization and contributed to poor interobserver reproducibility of QTD parameters in all 12 leads and in the precordial leads (relative error of 31%–35% and 34%–43%, respectively) as compared with the interobserver reproducibility of both QT and QU interval duration measurements (relative error of 3%–6%, P < 0.01). This observation was not explained by differences in the numerical order between QT interval duration and QTD, as the reproducibility of the QJ interval (i.e., interval of the same numerical order as QTD was significantly better (relative error of 7.5%–13%, P < 0.01) than that of QTD. Conclusions: Poor interobserver reproducibility of QT dispersion related to the presence of complex repolarization patterns may explain, to some extent, a spectrum of QT dispersion values reported in different clinical studies and may limit the clinical utility in this parameter.     

Bias of QT Dispersion

Background: Prolonged QT dispersion (QID) is associated with an increased risk of arrhythmic death but its accuracy varies substantially between otherwise similar studies. This study describes a new type of bias that can explain some of these differences. Material: One dataset (DiaSet) consisted of 356 subjects: 169 with diabetes, 187 nondiabetic control persons. Another dataset (ArrSet) consisted of 110 subjects with remote myocardial infarction: 55 with no history of arrhythmia and 55 with a recent history of ventricular tachycardia or fibrillation. Methods: 12‐lead surface ECGs were recorded with an amplification of 10 mm/mV at a paper speed of 50 mm/s. The QT interval was measured manually by the tangent‐method. The bias depends on the magnitude of the measurement errors and the measurable part of the bias increases with the number of the repeated measurements of QT. Results: The measurable bias was significant for both datasets and decreased for increasing QTD in the DiaSet (P < 0.001) and in the ArrSet (P = 0.11). The bias was 2.5 ms and 1.9 ms at QTD = 38 ms and 68 ms, respectively, in the ArrSet, and 7.5 ms and 2.8 ms at QTD = 19 ms and 55 ms, respectively, in the DiaSet. Conclusions: This study shows that random measurement errors of QT introduces a type of bias in QTD that decreases as the dispersion increases, thus reducing the separation between patients with low versus high dispersion. The bias can also explain some of the differences in the mean QTD between studies of healthy populations. Averaging QT over three successive beats reduces the bias efficiently. A.N.E. 2001;6(1):38–42     

Relation between Beat‐to‐Beat QT Interval Variability and T‐Wave Amplitude in Healthy Subjects

Objectives: Elevated beat‐to‐beat QT interval variability (QTV) has been associated with increased cardiovascular morbidity and mortality.The aim of this study was to investigate interlead differences in beat‐to‐beat QTV of 12‐lead ECG and its relationship with the T wave amplitude. Methods: Short‐term 12‐lead ECGs of 72 healthy subjects (17 f, 38 ± 14 years; 55 m, 39 ± 13 years) were studied. Beat‐to‐beat QT intervals were extracted separately for each lead using a template matching algorithm. We calculated the standard deviation of beat‐to‐beat QT intervals as a marker of QTV as well as interlead correlation coefficients. In addition, we measured the median T‐wave amplitude in each lead. Results: There was a significant difference in the standard deviation of beat‐to‐beat QT intervals between leads (minimum: lead V₃ (2.58 ± 1.36 ms), maximum: lead III (7.2 ± 6.4 ms), ANOVA: P < 0.0001). Single measure intraclass correlation coefficients of beat‐to‐beat QT intervals were 0.27 ± 0.18. Interlead correlation coefficients varied between 0.08 ± 0.33 for lead III and lead V₁ and 0.88 ± 0.09 for lead II and lead aVR. QTV was negatively correlated with the T‐wave amplitude (r =–0.62, P < 0.0001). There was no significant affect of mean heart rate, age or gender on QT variability (ANOVA: P > 0.05). Conclusions: QTV varies considerably between leads in magnitude as well as temporal patterns. QTV is increased when the T wave is small.     

Effect of premature ventricular beats on manual and automatic repolarization measurements

OBJECTIVE: To compare QT interval and QT dispersion in ventricular ectopic beats with measurements from the preceding and the immediately following sinus beats, and investigate differences between manual and automatic measurements. Patients: Eleven chronic uremic patients. Main outcome measures: ECGs were recorded during hemodialysis treatment and 12-lead sections containing five consecutive beats were extracted, each containing four sinus beats and one centrally-positioned premature ventricular beat. QT measurements were performed both manually and with a computer-automated technique. Results: T wave amplitude was greater in the ectopic beats compared to the sinus beats (0.61 +/- 0.18 vs. 0.23 +/- 0.06 mV, P <.001). The ectopic beats had a greater QT than the sinus beats when measured manually (415 +/- 35 ms vs. 386 +/- 28 ms, P <.001), or automatically (375 +/- 30 vs. 366 +/- 27 ms, P<.01). The sinus beats following the ectopics had a greater QT than the preceding sinus beats (400 +/- 27 vs. 386 +/- 28 ms, P<.001, manual; 382 +/- 24 vs. 366 +/- 27 ms, P<.001, automatic). Differences in QT dispersion were seen only between the ectopic and sinus beats (91 +/- 31 vs. 58 +/- 27 ms, P <.001, manual; 68 +/- 33 vs. 49 +/- 35 ms, P <.001, automatic). Conclusions: Manual measurement resulted in greater QT values than automatic measurement. Both techniques identified differences between sinus and ectopic beats. The ventricular ectopic beats resulted in an increase in the QT of the immediately following sinus beats. These results confirm the need to interpret QT measurements with care in the presence of ectopic beats.     

QT离散度测定的重复性和正常值的研究

为评价ＱＴ离散度（ＱＴｄ）测定的重复性和制定正常值范围,采用同步十二导联心电图机测量１００例正常人和５０例心肌梗死患者的ＱＴｄ,分析ＱＴｄ在不同纸速时测量的重复性情况,以及测量ＱＴｄ正常值范围。结果：正常人心电图以同步５０ｍｍ／ｓ纸速记录时ＱＴｄ的重复性较好,此时一人两次（Ｉｎｔｒａｏｂｓｅｒｖｅｒ）和两人一次（Ｉｎｔｅｒｏｂｓｅｒｖ－ｅｒ）测量的相关系数分别为０．８５,０．７５（Ｐ均＜０．０５）。心肌梗死患者的ＱＴｄ重复性以同步２５ｍｍ／ｓ纸速记录时为佳,在Ｉｎｔｒａｏｂｓｅｒｖｅｒ和Ｉｎｔｅｒｏｂｓｅｒｖｅｒ的相关系数分别为０．７５,０．６８（Ｐ均＜０．０５）。１００例正常人在同步５０ｍｍ／ｓ纸速下记录心电图测定的ＱＴｄ为２７±１２ｍｓ,９５％分布范围为３～５１ｍｓ。ＱＴｄ在男、女性别及各年龄段之间差异无显著性。结果提示用不同走纸速度测量正常和异常心电图的ＱＴｄ有助于提高其重复性。ＱＴｄ＜５０ｍｓ为正常范围。     

Comparing Methods of Measurement for Detecting Drug‐Induced Changes in the QT Interval: Implications for Thoroughly Conducted ECG Studies

Background: The aim of this study was to compare the reproducibility and sensitivity of four commonly used methods for QT interval assessment when applied to ECG data obtained after infusion of ibutilide. Methods: Four methods were compared: (1) 12‐lead simultaneous ECG (12‐SIM), (2) lead II ECG (LEAD II), both measured on a digitizing board, (3) 3‐LEAD ECG using a manual tangential method, and (4) a computer‐based, proprietary algorithm, 12SL? ECG Analysis software (AUT). QT intervals were measured in 10 healthy volunteers at multiple time points during 24 hours at baseline and after single intravenous doses of ibutilide 0.25 and 0.5 mg. Changes in QT interval from baseline were calculated and compared across ECG methods, using Bland–Altman plots. Variability was studied using a mixed linear model. Results: Baseline QT values differed between methods (range 376–395 ms), mainly based on the number of leads incorporated into the measurement, with LEAD II and 3‐LEAD providing the shortest intervals. The 3‐LEAD generated the largest QT change from baseline, whereas LEAD II and 12‐SIM generated essentially identical result within narrow limits of agreement (0.4 ms mean difference, 95% confidence interval ± 20.5 ms). Variability with AUT (standard deviation 15.8 ms for within‐subject values) was clearly larger than with 3‐LEAD, LEAD II, and 12‐SIM (9.6, 10.0, and 11.3 ms). Conclusion: This study demonstrated significant differences among four commonly used methods for QT interval measurement after pharmacological prolongation of cardiac repolarization. Observed large differences in variability of measurements will have a substantial impact on the sample size required to detect QT prolongation in the range that is currently advised in regulatory guidance.     

Use of lead adjustment formulas for QT dispersion after myocardial infarction.

OBJECTIVE--To determine whether lead adjustment formulas for correcting QT dispersion measurements are appropriate in patients after myocardial infarction. DESIGN--Retrospective analysis of QTc dispersion measurements in 461 electrocardiograms (ECGs). Data are presented as uncorrected QTc dispersion "adjusted" for a number of measurable leads and coefficient of variation of QTc intervals for ECGs in which between six and 12 leads had a QT interval that could be measured accurately. PATIENTS--Patients were drawn from the placebo arm of the second Leicester Intravenous Magnesium Intervention Trial. Some 163 patients who subsequently died and an equal number of known survivors had ECGs recorded on day 2 or 3 of acute myocardial infarction. ECGs were also available in 135 of these patients from at least 1 month postinfarct. RESULTS--The most common lead in which a QT interval measurement was omitted was aVR (n = 176), the least common lead was V3 (n = 13). The longest QTc interval measured was most usually in lead V4 (n = 72) and the shortest in lead V1 (n = 67). As the number of measurable leads decreased there was a small, nonsignificant increase in QTc dispersion from 12 lead to eight lead ECGs (mean (SD) 100 (35.5) v 109.5 (47.9) ms). Lead adjusted QTc dispersion (QTc dispersion/square root of the number of measurable leads) showed a large, significant increase when the number of measurable leads decreased from 12 to eight (28.9 (10.3) v 38.7 (16.1) ms, P < 0.001). A similar trend was seen for coefficient of variation of QTc intervals (standard deviation of QTc intervals/mean QTc interval 64.3 (2.19) v 8.45 (3.94)%, P < 0.001). CONCLUSIONS--Lead adjustment formulas for QT dispersion are not appropriate in patients with myocardial infarction. Large differences in lead adjusted QTc dispersion are produced, dependent on the number of measurable leads, for very small differences in QTc dispersion. It is recommended that QT dispersion is presented as unadjusted QT and QTc dispersion, stating the mean (SD) of the number of leads in which a QT interval was measured.     

Reproducibility and automatic measurement of QT dispersion

This study investigated interobserver (two observers) and intrasubject(two measurements) reproducibility of QT dispersion from abnormalelectrocardiograms in patients with previous myocardial infarction,and compared a user-interactive with an automatic measurementsystem. Standard 12-lead electrocardiograms, recorded at 25mm. s^–1, were randomly chosen from 70 patients followingmyocardial infarction. These were scanned into a personal computer,and specially designed software skeletonized and joined eachimage. The images were then available for user-interactive (mouseand computer screen), or automatic measurements using a speciallydesigned algorithm. For all methods reproducibility of the RRinterval was excellent (mean absolute errors 3–4 ms, relativeerrors 0·3–0·5%). Reproducibility of themean QT interval was good; intrasubject error was 6 ms (relativeerror 1·4%), interobserver error was 7 ms (1·8%),and observers' vs automatic measurement errors were 10 and 11ms (2·5, 2·8%). However QTc dispersion measurementshad large errors for all methods; intrasubject error was 12ms (17·3%), interobserver error was 15 ms (22·1%),and observers' vs automatic measurement were errors 30 and 28ms (35·4, 31·9%). QT dispersion measurements relyon the most difficult to measure QT intervals, resulting ina problem of reproducibility. Any automatic system must notonly recognize common T wave morphologies, but also these moredifficult T waves, if it is to be useful for measuring QT dispersion.The poor reproducibility of QT dispersion limits its role asa useful clinical tool, particularly as a predictor of events.     

Heart rate–corrected QT interval in men increases during winter months

BACKGROUND: Sudden cardiac death increases during winter months in both men and women. The heart rate-corrected QT (QTc) interval exhibits circadian variation. However, little is known about QTc interval variation with month of year. OBJECTIVE: We sought to determine whether the QTc interval varies with month of year. METHODS: We retrospectively analyzed a database of 24,370 electrocardiograms (ECGs) to determine seasonal variation in QTc intervals. The analysis data set included 7,976 baseline ECGs, one each for 3,700 men and 4,276 women. ECGs selected for analysis were normal, recorded in regions north of the equator, and taken on subjects >or=18 years old. The QT correction for heart rate (HR) was performed using QTc = QT*(HR/60)(0.4). The monthly mean QTc intervals were compared, for men and women separately, using a one-way analysis of variance with the Bonferroni correction for multiple comparisons. RESULTS: Subject ages ranged from 18 to 95 years. The monthly mean QTc intervals were consistently greater for women than for men by 5.2 +/- 2.3 ms. After correction for multiple comparisons, the difference between the greatest and least monthly mean QTc interval was 6.1 +/- 1.5 ms (P <.01) for men and 3.5 ms (nonsignificant) for women. The maximum monthly mean QTc interval of 413 +/- 18 ms (n = 560; P <.05) occurred in October for men and of 417 +/- 16 ms (n = 350) in March for women, but it was not significant. CONCLUSIONS: Significant seasonal variation in QTc interval exists among male subjects >or=18 years of age with normal baseline ECGs, with the QTc interval being longest in October. No significant variation was seen for women.     

Reproducibility of QT Parameters Derived from 24‐hour Ambulatory ECG Recordings in Healthy Subjects

Aim: To estimate the reproducibility of QT parameters derived from 24‐hour ambulatory ECG recordings. Method: Ten healthy volunteers aged 25 to 41 years participated. In two 24‐hour ambulatory ECG recordings obtained 1 day apart, the QT interval was measured manually at stable heart rates in approximately 16 periods during daytime and 6 periods during nighttime. The association between the QT and RR interval was described by linear regression for day and nighttime separately and the following QT parameters were calculated: the QT interval at heart rate 60 beats/min during daytime (QT(60)day), slope(day), slope(night), and the difference in QT(60) between day and nighttime (ΔQT(60)). The QT parameters were assessed four times for each participant to discriminate method inaccuracy from day to day variation. The reproducibility was estimated as the coefficient of repeatability, the relative error, and the ratio between within‐subject variability and between‐subject variability. Results: The coefficient of repeatability, the relative error and the ratio, respectively, were 19 ms, 1.8% and 0.5 for QT(60)day, 0.076, 21% and 0.68 for slope(day), 0.116, 43% and 1.37 for slope(night), and 37 ms, 325% and 1.19 for ΔQT(60) when estimating the overall day to day reproducibility. Inaccuracy of QT measurement accounted for approximately 40% of this variation, whereas the error caused by selecting segments was small. Conclusion: QT(60)day has a high reproducibility and may with advantage replace the conventional QT interval measured on a resting ECG. To assess QT dynamics, the slope of the regression line during daytime is suitable and the short term reproducibility acceptable for clinical trials. Regarding slope(night) and ΔQT(60), the variation is high and the parameters should be used with caution. A.N.E. 2001;6(1):24–31     

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. Ann Noninvasive Electrocardiol 2011;16(1):13–24     

QT Measurement: A Comparison of Three Simple Methods

Background: QT interval and QT dispersion are useful noninvasive measurements in clinical cardiology and can be measured by several methods. The comparative variability of these methods, however, is not well defined. Methods: We evaluated the intra- and interobserver variability of three simple methods of QT measurement: (1) ruler method: use of a 0.5-mm scale precision ruler to measure QT with end of T wave determined by extrapolating its slope to baseline; (2) caliper method: use of a standard electrocardiogram (ECG) caliper and the standard ECG paper scale with QT determined by visual inspection; (3) computer method: use of a digitized computer software program with QT determined by cursor set manually by the user. QT intervals from 11 patients (total 44 ECG leads) in sinus rhythm without conduction defect were measured by five blinded, trained observers at two time points (a week apart) in a crossover manner. Results: The mean difference in intraobserver measurements were 6 ± 2, 12 ± 12, and 27 ± 2 ms by the ruler, caliper, and computer methods, respectively (P > 0.01, ruler vs caliper or computer). The mean differences in interobserver measurements were 13 ± 3, 16 ± 4, and 29 ± 3 ms for the same methods, respectively (P > 0.01, ruler vs caliper, computer). Enlargement of the ECG to 200% did not reduce the measurement variability. Conclusion: The ruler method as described yielded the lowest variability in QT measurement.     

Automated versus Manual Measurement of the QT Interval and Corrected QT Interval

Background: The International Conference on Harmonization E14 Guideline specifies detailed assessment of QT interval or corrected QT interval prolongation when developing new drugs. We recently devised new software to precisely measure the QT interval. Methods and Results: The QT intervals of all leads for a selected single heart beat were compared between automated measurement with the new software from Fukuda Denshi and manual measurement. With both automated and manual measurement, QT intervals obtained by the tangent method were shorter than those obtained by the differential threshold method, but the extent of correction was smaller. QT interval data obtained by the differential threshold method were more similar to values obtained by visual measurement than were data obtained by the tangent method, but the extent of correction was larger. Variability was related to the T‐wave amplitude and to setting the baseline and tangent in the tangent method, while skeletal muscle potential noise affected the differential threshold method. Drift, low‐amplitude recordings, and T‐wave morphology were problems for both methods. Among the 12 leads, corrections were less frequent for leads II and V₃–V₆. Conclusion: We conclude that, for a thorough assessment of the QT/QTc interval, the tangent method or the differential threshold method appears to be suitable because of smaller interreader differences and better reproducibility. Correction of data should be done by readers who are experienced in measuring the QT interval. It is also important for electrocardiograms to have little noise and for a suitable heart rate and appropriate leads to be selected. Ann Noninvasive Electrocardiol 2011;16(2):156–164     

Seasonal variability of the QT dispersion in healthy subjects

We studied the seasonal variability of QT dispersion in 25 healthy subjects, aged 36 +/- 5 (25 to 46) years. Four seasonal 12-lead rest electrocardiograms (ECGs) recorded at a double amplitude were performed at 25 mm/s at intervals of roughly 3 months. To avoid possible confusion from the circadian rhythm of QT dispersion, subsequent ECGs were recorded within 30 minutes of the reference summer one. The QT dispersion was calculated as the difference between the longest and the shortest mean QT intervals. There was a seasonal variability in the QT dispersion (P =.001), with the largest QT dispersion occurring in winter (66 +/- 21 ms) and the smallest one in spring (48 +/- 18 ms). In conclusion, there exists a seasonal variability of QT dispersion in healthy subjects and such variability should be taken into consideration in comparison of the QT dispersion.     

Circadian Pattern of QT/RR Adaptation in Patients with and Without Sudden Cardiac Death after Myocardial Infarction

Background: Abnormalities in the adaptation of the QT interval to changes in the RR interval may facilitate the development of ventricular arrhythmias. Methods: This study sought to evaluate the dynamic relation between the QT and RR intervals in patients after acute myocardial infarction. The study population consisted of 14 patients after myocardial infarction (age 60 ± 7 years, 12 men) who died suddenly (SCD victims) within 1 year after the myocardial infarction and 14 pair-matched age, sex, left ventricular ejection fraction, infarct site, thrombolytic therapy) patients who remained event-free after myocardial infarction (Ml survivors) for at least 3 years. Fourteen normal subjects were studied as controls (age 55 ± 9 years, 11 men). QT and RR intervals were measured on a beat-to-beat basis automatically with a visual control from 24-hour ambulatory ECGs using Reynolds Pathfinder 700. Mean hourly values of the QT/RR slope (QT =α+βRR) and corrected QT interval at 1000 ms of RR interval (QT_1s) were derived for each subject using an inhouse program (QT_1s=α+1000β). The dynamics of the QT/RR slope and QT_1s were assessed on the basis of hourly mean values. The circadian rhythm of ventricular repolarization (QT_1s and QT/RR slope) was examined by harmonic regression analysis. Results: There was a trend towards a significant difference in 24-hour mean value of QT_1s between study groups (408 ± 26 ms vs 381 ± 43 ms and 386 ± 22 ms, P = 0.06), and a significant difference was found between SCD victims and normal subjects (408 ± 26 vs 386 ± 22 ms, P = 0.02). The QT_1s differed significantly between study groups (P = 0.038) only during the day time (09:00–19:00 hour), when QT_1s was significantly longer in SCD victims than in normal subjects (409 ± 33 vs 380 ± 27 ms, P = 0.02) and tended to be longer than in Ml survivors (409 ± 33 vs 379 ± 42 ms, P = 0.08). The 24-hour mean value of QT/RR slope was significantly different between study groups (P = 0.04), with a significantly steeper slope in SCD victims than in normal subjects (0.15 ± 0.07 vs 0.09 ± 0.02, P = 0.008). During day time, the QT/RR slope differed significantly between study groups (P = 0.04), while the difference was less marked at night (P = 0.08). The slope was significantly steeper in SCD victims than in normal subjects during both day and night (P < 0.05). A marked circadian variation of QT_1s was observed in normal subjects, which was blunted in Ml survivors and SCD victims. Conclusions: Abnormal repolarization behaviors, characterized by longer QT_1s and impaired adaptation of QT to variations in RR intervals, were found in SCD victims. Hence, lethal ventricular tachyarrhythmias might be provoked by the altered repolarization dynamics in patients after myocardial infarction. A.N.E. 1999;4(3):286–294