Fabio Badilini

Relation between ventricular repolarization duration and cardiac cycle length during 24-hour Holter recordings. Findings in normal patients and patients with long QT syndrome.

BackgroundThe interval from the R wave to the maximum amplitude of the T wave (RTm) contains the heart rate dependency of ventricular repolarization. Methods and ResultsA computer algorithm was developed to quantify the RTm and preceding RR intervals for each of more than 50,000 beats on 24-hour ambulatory electrocardiographic (Holter) recordings to evaluate the dynamic relation between repolarization duration and cycle length. The relation of RTm to the preceding RR interval (RTm/RR slope) was determined by the best-fit linear regression equation between these two parameters. Eleven normal subjects and 16 patients with long QT syndrome (LQTS) were investigated. Six of the normal subjects had Holter recordings obtained before and after βblocker therapy. βBlockers were associated with a significant (p = 0.005) reduction in the RTm/RR slope from 0.13±0.02 to 0.10±0.02. The mean value of the RTmIRR slope was significantly (p = 0.003) larger in the LQTS patients (0.21±0.08) than in normal subjects (0.14±0.03). ConclusionsThese findings indicate that 1) quantification of the dynamic relation between ventricular repolarization and RR cycle length can be obtained on a large number of Holter-recorded heart beats; 2) βblockers reduce the RTm/RR slope in normal patients; and 3) LQTS patients have an exaggerated delay in repolarization at long RR cycle lengths.

Dispersion of Ventricular Repolarization Reality? Illusion? Significance?

The QT interval is considered to be a surrogate of cellular action potential duration. However, it yields a limited view of the complex electrogenesis of the ventricular repolarization (VR). Evidence of T-wave end inequality among surface ECG traces back to Wilson et al,1 and it was recently revived by the concept of dispersion.2 Because of its apparent simplicity, QT dispersion became fashionable, and a growing literature is now devoted to its potential prognostic interest. The study by Zabel et al3 seems timely to temper the enthusiasm. In a prospectively collected cohort of patients, it offers evidence that avoiding technical biases of measurement, QT dispersion is not a prognostic marker. This contrasts with the confirmation that ejection fraction, heart rate variability, and simply heart rate are indeed good predictors of events. It suggests some reflections about the correct and comprehensive use of a concept that still needs to be validated. ### Measurement of QT Duration QT interval can be measured manually or with dedicated algorithms. The performances of the 2 approaches were compared in a remarkable study conducted by the late Jos Willems.4 The aim was to assess the diagnostic performances of computerized systems.5 In view of the interobserver and intraobserver variability in determining wave recognition points, an elaborate reviewing scheme was devised to obtain a group estimate that should define the “truth,” ultimately serving as a standard for computer measurement. Five experts defined individually the P- and QRS-wave onset and offset and the T-wave offset. The database was formed of 250 digitized (500 Hz) 12 standard leads and Frank XYZ leads, including a 25% proportion of normal hearts and a variety of ventricular hypertrophies and infarctions but no bundle-branch block or long-QT syndrome. The interexpert variation was expressed as 2 SD of the difference between the median and the individual …

Circadian modulation of QT rate dependence in healthy volunteers: Gender and age differences

QT rate dependence is known to be linked with both circadian variations of the autonomic tone and gender. However, age and heart rate variability (HRV) influences are not well established. The QT/RR relationship was evaluated, separately during the day and at night, on 24-hour electrocardiogram in 60 healthy subjects (30 men) divided into three homogeneous groups (group 1, 20-29; group 2 30-39; group 3, 40-50 years). QT rate dependence was larger during the day in both genders. Women showed stronger QT rate dependence (0.195 during the day vs. 0.154 in men P< .0001). The circadian modulation decreased with increasing age (day/night slope differences: group 1, 0.038; group 2, 0.031; group 3, 0.001; analysis of variance P<.05). In addition, QT rate dependence increased as mean RR decreased (r = -0.58, P<.0001) and decreased as HRV parameters increased. Multiple influences on QT rate dependence can be found: not only circadian and gender modulation, but also age, heart rate, and HRV interventions.

Methodologies to characterize the QT/corrected QT interval in the presence of drug-induced heart rate changes or other autonomic effects

This White Paper, written collaboratively by members of the Cardiac Safety Research Consortium from academia, industry, and regulatory agencies, discusses different methods to characterize the QT effects for drugs that have a substantial direct or indirect effect on heart rate. Descriptions and applications are provided for individualized QT-R-R correction, Holter bin, dynamic QT beat-to-beat, pharmacokinetic-pharmacodynamic modeling, and QT assessment at constant heart rate. Most of these techniques are optimally performed using continuous electrocardiogram data obtained in clinical studies designed to characterize a drugs effect on the QT interval. An important study design element is the collection of drug-free data over a range of heart rates seen on treatment. The range of heart rates is increased at baseline by using ambulatory electrocardiogram recordings in addition to those collected under semisupine, resting conditions. Discussions in this study summarize areas of emerging consensus and other areas in which consensus remains elusive and provide suggestions for additional research to further increase our knowledge and understanding of this topic.

Quantitative Aspects of Ventricular Repolarization

Introduction: QT dispersion assesses repolarization inhomogeneity on 12‐lead standard ECG. However, the implications of the electrical cardiac vector during the repolarization phase (the T wave loop) with the genesis of this phenomenon are unknown.

Influence of Atropine on Fractal and Complexity Measures of Heart Rate Variability

Background: Measurement of short‐term fractal‐like correlation properties of heart rate dynamics has been shown to be a useful prognostic indicator of adverse events in cardiac patients. Complexity measurements of heart rate variability (HRV) have already provided important information in many cardiac conditions. However, data on the physiological background of these newer nonlinear measures of HRV are limited.

Analyses of Dynamic Beat-to-Beat QT-TQ Interval (ECG Restitution) Changes in Humans under Normal Sinus Rhythm and Prior to an Event of Torsades de Pointes during QT Prolongation Caused by Sotalol

Background: Restitution through intracardiac pacing has been used to assess arrhythmia vulnerability. We examined whether analyses of sequential beat‐to‐beat QT and TQ interval measures can be used to quantify ECG restitution changes under normal sinus rhythm.

Heart Rate Variability in Passive Tilt Test: Comparative Evaluation of Autoregressive and FFT Spectral Analyses

The dynamic response of the autonomic nervous system during tilting is assessed by changes in the low (LF) and high frequency (HF) components of the RR series power spectral density (PSD). Although results of many studies are consistent, some doubts related to different methodologies remain. Specifically, the respective relevance of autoregressive (AR) and fast Fourier transform (FFT) methods is often questioned. Reat‐to‐beat RR series were recorded during 90° passive tilt in 18 healthy subfects (29 ± 5 years, eight females). FFT‐based (50% overlap, Manning window) and AR‐based (Levinson‐Durbin algorithm) PSDs were calculated on the same RR intervals. Powers in very low frequency (VLF: < 0.04 Hz), LF (0.04–0.15 Hz), and HF (0.15–0.40 Hz) bands were calculated either by spectrum integration (FFT and ARIN), by considering the highest AR component in each band (ARHP), or by summation of all AR components (ARAP). LF and HF raw powers (ms2) were normalized by total power (%P) and by total power after removal of the VLF component (nu). AR and FFT total powers were not different, regardless of body position. In supine condition, when compared to ARHP and ARAP. FFT underestimated VLF and overestimated LF, whereas in tilt position FFT overestimated HF and underestimated LF. However, supine/tilt trends were consistent in all methods showing a clear reduction of HF and a less marked increase of LF. Both normalization procedures provided a significant LF increase and further magnified the HF decrease. Results obtained with ARIN were remarkably close to those obtained with FFT. In conclusion, significant differences between AR and FFT spectral analyses do exist, particularly in supine position. Nevertheless, dynamic trends provided by the two approaches are consistent. Normalization is necessary to evidence the LF increase during tilt.

Beat-to-beat quantification and analysis of ST displacement from Holter ECGs: a new approach to ischemia detection

The authors present an algorithm which develops a set of beat-to-beat time series. The algorithm provides the support for a complete and operator-independent offline analysis of ambulatory electrocardiogram (ECG) recordings, spanning heart rate variability, repolarization duration variability, and detection of transient myocardial ischemia. The algorithm uses variables extracted by the frequency distributions of ST segment displacements. A logistic regression, applied to the data extracted from 20 certified normal and 24 certified ischemic Holter ECGs, enhanced the classification performance of ST segment depression in identifying ischemic episodes. Sensitivity and specificity reached 95.8% and 90%, respectively.<<ETX>>

Cubic Spline Baseline Estimation In Ambulatory ECg Recordings For The Measurement Of ST Segment Displacements

Correction of baseline wander is one of the major problems in ambulatory ECG analysis. In this work, the isoelectric line of the ECG signal is estimated by interpolating consecutive isoelectric points with third order lines. A fiducial point is found with minimization techniques in a 100 msec window before the peak of the R wave. The definition of a continuous line permits synchronous measurements at any point of the ST segment. The technique (called cubic spline interpolation) allows the development of a reliable beat-by-beat ST segment displacement signal.