Dynamic QT/RR coupling in patients with pacemakers

Bivariate Entropy Analysis of Electrocardiographic RR-QT Time Series

QT interval variability (QTV) and heart rate variability (HRV) are both accepted biomarkers for cardiovascular events. QTV characterizes the variations in ventricular depolarization and repolarization. It is a predominant element of HRV. However, QTV is also believed to accept direct inputs from upstream control system. How QTV varies along with HRV is yet to be elucidated. We studied the dynamic relationship of QTV and HRV during different physiological conditions from resting, to cycling, and to recovering. We applied several entropy-based measures to examine their bivariate relationships, including cross sample entropy (XSampEn), cross fuzzy entropy (XFuzzyEn), cross conditional entropy (XCE), and joint distribution entropy (JDistEn). Results showed no statistically significant differences in XSampEn, XFuzzyEn, and XCE across different physiological states. Interestingly, JDistEn demonstrated significant decreases during cycling as compared with that during the resting state. Besides, JDistEn also showed a progressively recovering trend from cycling to the first 3 min during recovering, and further to the second 3 min during recovering. It appeared to be fully recovered to its level in the resting state during the second 3 min during the recovering phase. The results suggest that there is certain nonlinear temporal relationship between QTV and HRV, and that the JDistEn could help unravel this nuanced property.

Clinical applications of QT/RR hysteresis assessment: A systematic review

BACKGROUND

QT/RR hysteresis (QT-hys) is an index of the time accommodation of ventricular repolarization to heart rate changes. This report comprehensively reviews studies addressing QT-hys as a biomarker of medical conditions.

METHODS

This is a secondary analysis of data from a recent systematic review pertaining to methods of assessment of QT-hys. Articles included in the former review were filtered in order to select original articles investigating the association of QT-hys with medical conditions in humans.

RESULTS

Nineteen articles fulfilled our inclusion criteria. Given the heterogeneity of the methods and investigated conditions, no pooled analysis of data could be implemented. QT-hys was mostly studied as a risk marker of severe arrhythmias, as a predictor of the long QT syndrome (LQTS) phenotypes and genotypes and as a marker of exercise-induced ischemia. An increased QT-hys appears to be implicated in arrhythmogenesis, although the evidence in this regard relies on few human studies. An augmented QT-hys was reported in the LQTS, predominantly in the LQT2 genotype, but conflicting results were obtained between studies using different methods of assessment. In addition, QT-hys appears to be a useful marker of stress-induced myocardial ischemia in patients suspected of coronary artery disease.

CONCLUSIONS

QT-hys evaluation has potential clinical utility in at least some clinical conditions. Further studies of the clinical validity of QT-hys assessment are warranted, particularly condition specific studies based on QT-hys evaluation methods that provide separate estimates of QT-hys and QT/RR dependency.

Theoretical and experimental comparison of lag-based and time-based exponential moving average models of QT hysteresis

OBJECTIVE

In the electrocardiogram, adaptation of the QT interval to variations in heart rate is not instantaneous. Quantification of this hysteresis phenomenon relies on mathematical models describing the relation between the RR and QT time series. These models reproduce hysteresis through an effective RR interval computed as a linear combination of the history of past RR intervals. This filter depends on a time constant parameter that may be used as a biomarker.

APPROACH

The most common hysteresis model is based on an autoregressive filter with an impulse response that decreases exponentially with the beat number (lag-based model). Recognizing that the QT time series is unevenly spaced, we propose two exponential moving average filters (time-based models) to define the effective RR interval: one with an impulse response that decreases exponentially with time in seconds, and one with a step response that relaxes exponentially with time in seconds. These two filters are neither linear nor time-invariant. Recurrence formulas are derived to enable efficient implementation.

MAIN RESULTS

Application to clinical signals recorded during tilt table test, exercise and 24 h Holter demonstrates that the three models perform similarly in terms of goodness-of-fit. When comparing the hysteresis time constant in two conditions with different heart rates, however, the time-based models are shown to reduce the bias on the hysteresis time constant caused by heart rate acceleration and deceleration.

SIGNIFICANCE

Time-based models should be considered when intergroup differences in both heart rate and QT hysteresis are expected.

Categorization and theoretical comparison of quantitative methods for assessing QT/RR hysteresis

BACKGROUND

In the human electrocardiogram, there is a lag of adaptation of the QT interval to heart rate changes, usually termed QT/RR hysteresis (QT-hys). Subject-specific quantifiers of QT-hys have been proposed as potential biomarkers, but there is no consensus on the choice of the quantifier.

METHODS

A comprehensive literature search was conducted to identify original articles reporting quantifiers of repolarization hysteresis from the surface ECG in humans.

RESULTS

Sixty articles fulfilled our inclusion criteria. Reported biomarkers were grouped under four categories. A simple mathematical model of QT/RR loop was used to illustrate differences between the methods. Category I quantifiers use direct measurement of QT time course of adaptation. They are limited to conditions where RR intervals are under strict control. Category IIa and IIb quantifiers compare QT responses during consecutive heart rate acceleration and deceleration. They are relevant when a QT/RR loop is observed, typically during exercise and recovery, but are not robust to protocol variations. Category III quantifiers evaluate the optimum RR memory in dynamic QT/RR relationship modeling. They estimate an intrinsic memory parameter independent from the nature of RR changes, but their reliability remains to be confirmed when multiple memory parameters are estimated. Promising approaches include the differentiation of short-term and long-term memory and adaptive estimation of memory parameters.

CONCLUSION

Model-based approaches to QT-hys assessment appear to be the most versatile, as they allow separate quantification of QT/RR dependency and QT-hys, and can be applied to a wide range of experimental settings.

Measure of the QT-RR dynamic coupling in patients with the long QT syndrome

BACKGROUND

The patients with the long QT syndrome type-1 (LQT-1) have an impaired adaptation of the QT interval to heart rate changes. Yet, the description of the dynamic QT-RR coupling in genotyped LQT-1 has never been thoroughly investigated.

METHOD

We propose a method to model the dynamic QT-RR coupling by defining a transfer function characterizing the relationship between a QT interval and its previous RR intervals measured from ambulatory Holter recordings. Three parameters are used to characterize the QT-RR coupling: a fast gain (Gain(F) ), a slow gain (Gain(L) ), and a time constant (τ). We investigated the values of these parameters across genders, and in genotyped LQT-1 patients with normal QTc interval duration (QTc < 470 ms).

RESULTS

The QT-RR dynamic profiles are significantly different between LQT-1 patients (97) and controls (154): LQT-1 have longer QTc interval (453 ± 35 vs. 384 ± 26 ms, P < 0.0001), and an increased dependency of the QT interval to previous RR changes revealed by a larger Gain(L) (0.22 ± 0.06 vs. 0.18 ± 0.07, P < 0.0001) and Gain(F) (0.05 ± 0.02 vs. 0.03 ± 0.01, P < 0.0001). Importantly, LQT-1 patients have a faster QT dynamic response to previous RR changes described by τ: 122 ± 44 vs. 172 ± 92 beats (P < 0.0001). This faster QT dynamic response of the QT-RR dynamic coupling remained in LQT-1 patients with QTc in a normal range (<430 ms).

CONCLUSIONS

The measurement of QT-RR dynamic coupling could be used in patients suspected to carry a concealed form of the LQT-1 syndrome, or to provide insights into the types of arrhythmogenic triggers a patient may be prone to.

Measurement and regulation of cardiac ventricular repolarization: from the QT interval to repolarization morphology

Ventricular repolarization (VR) is a crucial step in cardiac electrical activity because it corresponds to a recovery period setting the stage for the next heart contraction. Small perturbations of the VR process can predispose an individual to lethal arrhythmias. In this review, I aim to provide an overview of the methods developed to analyse static and dynamic aspects of the VR process when recorded from a surface electrocardiogram (ECG). The first section describes the list of physiological and clinical factors that can affect the VR. Technical aspects important to consider when digitally processing ECGs are provided as well. Special attention is given to the analysis of the effect of heart rate on the VR and its regulation by the autonomic nervous system. The final section provides the rationale for extending the analysis of the VR from its duration to its morphology. Several modelling techniques and measurement methods will be presented and their role within the arena of cardiac safety will be discussed.