Are serial holter QT, late potential, and wavelet measurement clinically useful?

Abstract

Clinical centers are increasingly using new techniques such as Holter QT, late potential, and wavelet measurements. However, we lack validated databases for the assessment of the performance of the signal-processing methods and their reproducibility. Failure of the QT interval to adapt to changes in the heart rate is considered to be a more meaningful parameter than QT prolongation itself. In this study, different factors that may affect the reproducibility of QT and QTm (onset of the QRS to the maximum of T) measurement are analyzed: the incidence of sympathetic tone and parasympathetic activity on low- and high-frequency QT variability, the very low frequency dependency of the QT interval to changes in the R-R interval, changes in the heart's position, and measurement errors. Typical root-mean-square values of the beat-to-beat measurement errors in upright-position Holter recordings are only 1.5 ms for QT versus 3.4 ms for QTm. Although the dependence of the QT interval on the heart rate is well established, the method for rate correction of the QT interval remains controversial. None of the formulas for heart rate adjustment of the QT previously proposed provide complete correction for all of the rate influences involved due to “memory phenomenon”; that is, there is a time delay, ranging up to 3–4 minutes, between a change in heart rate and the subsequent change in the QT interval. This problem has been solved by developing patient-specific neural networks that are trained to “identify” the dynamic behavior of the QT interval (or QTm) as a function of the R-R interval in order to predict the beat-to-beat changes of the QT interval as a function of the measured beat-to-beat changes of the R-R interval. Computing the differences between the predicted and the measured QT interval will allow for the detection of any significant deviations, both in the steady-state and transient conditions. Recent developments in the analysis of the high-resolution electrocardiogram (HRECG) in the time domain and frequency domain, with emphasis on the assessment of the reproducibility of late potential and wavelet measurements, are also reported in this study. The two main causes of variability in HRECG analysis are physiology and, for time-domain analysis, intermanufacturer variability. Physiologic changes can be overcome by standardizing the clinical protocols and repeating the recordings. The most important technical requirement for the proper use of late potentials is to standardize the algorithm for the detection of QRS offset among different late potential analyzing machines so that clinical data can be exchanged. The recently introduced wavelet transform provides a fruitful alternative to the more classical time-domain methods. Preliminary results show an 8 to 15% performance improvement over conventional time-domain analysis for the stratification of the HRECG after myocardial infarction. Reproducibility is excellent, up to 100%, but needs to be assessed on larger populations matched for age, sex, and pathology.