To reduce QT measurement error, a new method was tested in which high-gain, high-speed, simultaneous 12-lead electrocardiographic (ECG) recordings were obtained during a single cardiac cycle. To increase its predictive power, the utility of combining QTD with the QRS duration for predicting susceptibility to ventricular tachyarrhythmia (VT) was analyzed. A total of 113 patients referred for electrophysiological study underwent baseline simultaneous 12-lead ECG followed by electrophysiological study to determine VT inducibility. Twenty-six patients had inducible VT while 87 patients did not. QT intervals and the width of QRS complex were measured from a single cardiac cycle with high-gain (8 times normal) and high-speed (100 mm/second) 12-lead ECG recordings. This method resulted in 100% QT interval identification throughout all 12 leads for every patient. Receiver-operator characteristic curves (ROC) and the areas under the ROC curves (AUC) were used to quantitatively analyze the performance of four ECG variables (QTD3, QTD12, QTD12 + QRS and QTD3 + QRS). All four ECG variables were significantly increased in the patients with inducible VT as compared to those without inducible VT. The QTD3 algorithm was less useful than QTD12 in predicting inducible VT; however, the addition of QRS duration to all QTD algorithms enhanced VT detection. In conclusion, we found: 1) QRS duration has an incremental benefit in the detection of VT when combined with QTD; 2) QTD12 + QRS duration provided the highest predictive power among the four tested algorithms; 3) high-gain, high-speed 12-lead ECG recordings reduced QT measurement error. QT dispersion (QTD) has been proposed as an indicator of risk for ventricular tachyarrhythmias (VT) in different clinical settings (long QT syndrome,2,3 congestive heart failure4 and post-myocardial infarction5-9). However, a major limitation in the use of QTD is the intra- and inter-observer variability of the measurement, 10-13 as well as its relatively low predictive power compared with other clinical indexes (late potentials, T-wave alternans and left ventricular ejection fraction). 14 Previously, Cohen and colleagues demonstrated the utility of combining QTD with QRS duration in predicting susceptibility to VT. 14 However, this technique was limited by the measurement of QT intervals from different cardiac cycles using a standard, 3-channel, 12-lead electrocardiogram (ECG). In addition, patients with bundle branch block were not excluded in the study and the contribution of QRS to the overall detection performance may have been obscured. The main objective of the present study was to examine the utility of combining QRS duration with QTD, measured from either 12-lead or 3-lead ECG, for predicting susceptibility to VT and to quantitatively compare their predictive performances. One hundred and twenty-eight consecutive patients were referred for an electrophysiological study (EPS) to assess for VT. Fifteen patients with bundle branch block were excluded. The remaining 113 patients (65 men and 48 women; mean age ± SD, 66 ± 14 years), regardless of their cardiac substrates and initial presentations to electrophysiological study, were enrolled in this study as shown in Table 1. All patients underwent a baseline simultaneous 12-lead ECG using a Prucka electrophysiology recording system (Prucka Inc., Houston, Texas) in the electrophysiology laboratory prior to programmed electrical stimulation off antiarrhythmic drugs. The Prucka system uses 12 channels to record a 12-lead ECG simultaneously; therefore, ECG variables can be measured in a single cardiac cycle. In addition, the recorded ECGs are stored in a digital format via computer hard disk and can be played back and displayed on a computer screen for review and analysis in a variety of gains and sweep speeds. All ECG variables were manually measured from the computer screen directly using an internal electronic caliper provided by the Prucka recording system. The electronic caliper is a software tool for measuring intervals on the computer screen directly without the need to print out paper strips. ECG measurements included QRS duration (QRS), QTD (QTmax-QTmin) in all 12 leads (QTD12) or in 3 quasi-orthogonal leads (leads I, aVF, V1) (QTD3). QTD3 + QRS and QTD12 + QRS were calculated based on QTD12, QTD3 and QRS measurements. All the ECG variables were measured in a single cardiac cycle with high gain (8 times normal) and high speed (100 mm/second) to facilitate more precise discrimination. QRS duration was measured from the lead with the earliest onset to the lead with the latest offset from all 12 leads. The end of the T-wave was measured where it intersected the isoelectric TP baseline. In cases of low T-wave amplitude, gain was further increased (16 times or 32 times normal) so that the end of the T-wave could be visually identified. In the presence of a U-wave, the end of the T-wave was obtained from the nadir between the T- and U-wave peaks. In the presence of biphasic T-waves, the intersection of the late stage of the T-wave either above or below the isoelectric TP baseline was used as the end of the T-wave. All measurements were performed by a single reader (WQ) who was blinded to the clinical characteristics of the patient. Programmed electrical stimulation was performed using a standard protocol of up to three extra stimuli from two right ventricular sites (apex, outflow tract or septum using two different drive cycle lengths). Pacing stimuli were two milliseconds in duration and twice the diastolic threshold. VT susceptibility was defined as spontaneous and/or inducible sustained VT. Sustained VT was defined as VT of >= 30 seconds in duration or requiring cardioversion because of hemodynamic instability. To measure the predictive power of the addition of QRS to QTD, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was computed for each of the four standard ECG algorithms as a predictor of VT and for QTD with QRS as combined predictors of VT. Receiver operator characteristic curves were determined. The areas below the ROC curves were simply calculated using the trapezoidal rule. 18 To minimize the estimation error of the area, the maximum (Table 2) possible number of points were used to construct the ROC curves. A comparison of the areas of the four receiver operator characteristic curves was utilized in order to help define the optimal VT detection algorithm. All data are presented as means ± SEM unless otherwise noted and statistical comparisons of the categorical variables between inducible and non-inducible patients were done by the Chi-square analysis (with p 16 as well as Cohen and colleagues,14 compared QTD measured from a small set of ECG leads (such as the three quasi-orthogonal leads of aVF, I and V1) with those measured from standard 12 leads. Their results demonstrated the consistency in discrimination of positive and negative outcomes using these two different measurements. However, it is not certain if a reduced set of ECG leads in QTD measurement could replace a standard 12-lead measurement. In the present study, we also demonstrated that QTD3 and QTD12 consistently predicted susceptibility to VT. However, we also demonstrated that QTD12 was a better discriminator of VT than QTD3 as evaluated by many criteria, such as sensitivity, AUC, positive and negative predictive values. The QTD12 method yielded a 16% higher sensitivity, a 9% higher AUC, a 12% higher positive predictive value and a 3.6% higher negative predictive value as compared to the QTD3 method (Table 3 and Figure 2). In the present study, we used a high-gain and high-speed digital ECG that resulted in 100% QT interval identification in all 12 leads of every ECG as compared to a mean of 9.9 leads as reported by Glancy and colleagues.16 This may be the major contributing factor that makes QTD12 superior to QTD3 in the present study. QTD reflects the regional discrepancies of ventricular recovery time. Therefore, the leads picking up maximum and minimum QT intervals may vary from patient to patient depending on their heart substrates. In the current study, the maximum QT intervals were heavily concentrated on V2, I and V6 (38.3%) while the minimum QT intervals were heavily concentrated on V1, aVL and V2 (53.2%). As a consequence, QTD measured from the quasi-orthogonal leads (I, aVF and V1) may miss a large percentage of maximum and minimum QT intervals. Besides the different measurement technique used in these studies, the discrepancy may also reflect different patient populations. Cohen and colleagues demonstrated14 that the QTD3 + QRS duration algorithm was as good as the QTD12 + QRS duration method for predicting VT. The present study also demonstrated that QRS duration enhanced the performance of QTD in predicting inducibility of VT. However, QTD12 + QRS duration performed better than QTD3 + QRS duration, as shown in their AUC measurements; AUC for QTD12 + QRS (0.708) was 5% greater than that for QTD3 + QRS (0.715). This is in line with the fact that QTD12 was superior to QTD3 if they were used alone. If the partial area of the AUC of the two ROC curves between the false-positive rate range of 0.20 and 0.60 in Figure 1 could be legitimately tested, the results would be significant as extensively investigated by McClish20 and Obuchowshki.21 Of course, the p-value for the comparison is only meaningful if the false-positive rate range is chosen prior to rather than after inspection of the ROCs. Thus, this p-value is not calculated, but only noted that the results were suggestive of increased clinical utility for the combination QTD + QRS as a combined predictor of VT when compared to QTD alone. QRS and QTD reflect different electrophysiological mechanisms. However, it is not certain if QRS and QTD are independent indicators of susceptibility to VT. Brembilla-Perrot19 observed a strong correlation between inducibility of sustained VT and QRS duration. In the present study, we demonstrated that QRS duration may be a weak indicator of VT as compared to QTD. QTD12 yields 13.6% higher AUC compared to the QRS duration measurement. Ducceschi and co-workers15 observed a significant correlation between QTD and the presence of late potentials, as well as QTD and the mean QT interval. In the present study, we also observed a close correlation between QTD and mean QT interval (r = 0.52; p 17 However, we did not observe a close relationship between QTD and QRS duration (r = 0.19; p = 0.04; Figure 3B). Our results also suggest that QRS duration and QTD are rather independent indicators of susceptibility to VT. This may be explained by the fact that QRS and QTD do not share the same electrophysiological mechanism; QRS duration is an index of conduction time in the ventricle while the QTD represents a global measurement of heterogeneity in ventricular repolarization. In conclusion, QTD12 alone appears to be superior to QTD3 for discrimination of VT inducibility as supported by the logistic regression for the statistical utility of adding QRS to QTD. In addition, QTD12 + QRS provided the highest predictive power amongst all four tested algorithms for identifying VT. Also, the high-gain, high-speed, simultaneous 12-lead ECG recording appears to provide more reliable QTD measurements. However, it will be necessary to examine a second, independent sample to prospectively test the hypothesis that QRS contributes to increased clinical utility as well.