Bias of QT dispersion

QT Dispersion: Does It Change after Percutaneous Coronary Intervention?

BACKGROUND

Myocardial ischemia is one of several causes of prolonged QT dispersion. The aim of this study was to evaluate the effect that percutaneous coronary intervention has on the depolarization and repolarization parameters of surface electrocardiography in patients with chronic stable angina.

METHODS

We assessed the effects of full revascularization in patients with chronic stable angina and single-vessel disease who underwent percutaneous coronary intervention. Twelve-lead electrocardiograms were recorded before intervention and 24 hours subsequently. We measured parameters including QRS duration, QT and corrected QT durations, and JT and corrected JT duration in both electrocardiograms and compared the values.

RESULTS

There were significant differences between the mean QRS interval (0.086 ± 0.01 sec vs. 0.082 ± 0.01 second; p value = 0.01), mean corrected QT dispersion (0.080 ± 0.04 sec vs. 0.068 ± 0.04 sec; p value = 0.001), and mean corrected JT dispersion (0.074 ± 0.04 sec vs. 0.063 ± 0.04 sec; p value = 0.001) before and after percutaneous coronary intervention. No significant differences were found between the other ECG parameters.

CONCLUSION

Our data indicate that the shortening of corrected QT dispersion and corrected JT dispersion in patients undergoing percutaneous coronary intervention is prominent.

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.

Evaluation of electrocardiogram T-wave dispersion measurement methods

Methods for measuring the T-wave dispersion are difficult to improve, because the exact result is unknown. This study describes a flexible and cheap method for analysing the measurement problems. The analysis consisted of measuring the T-wave dispersion of electrocardiograms and vector loops with simulated dispersion. The vector loops were simulated using two primary T-waves that were identical, quasi-monophasic and located in orthogonal leads. The dispersion was the time delay between the primary T-waves. The electrocardiograms were projections of the vector loops on approximations to the orientations of leads in the standard 12-lead ECG. This simulation model was validated by matching simulated ECGs and vector loops with measured ECGs and vector loops drawn from a population of 247 patients with acute myocardial infarction. The analysis of the measurement problems was demonstrated by measuring an approximation to the classical QT-dispersion in ECGs with simulated dispersion in the interval 0-100 ms, in steps of 2 ms. This showed that the QT-dispersion was unable to measure the simulated dispersion accurately in the clinically relevant interval of 50-100 ms. The results indicated that the low prognostic accuracy of the QT-dispersion could be due to an inaccurate measurement method.