QT hysteresis index improves the power of treadmill exercise test in the screening of coronary artery disease

Sex and Rate Change Differences in QT/RR Hysteresis in Healthy Subjects

While it is now well-understood that the extent of QT interval changes due to underlying heart rate differences (i.e., the QT/RR adaptation) needs to be distinguished from the speed with which the QT interval reacts to heart rate changes (i.e., the so-called QT/RR hysteresis), gaps still exist in the physiologic understanding of QT/RR hysteresis processes. This study was designed to address the questions of whether the speed of QT adaptation to heart rate changes is driven by time or by number of cardiac cycles; whether QT interval adaptation speed is the same when heart rate accelerates and decelerates; and whether the characteristics of QT/RR hysteresis are related to age and sex. The study evaluated 897,570 measurements of QT intervals together with their 5-min histories of preceding RR intervals, all recorded in 751 healthy volunteers (336 females) aged 34.3 ± 9.5 years. Three different QT/RR adaptation models were combined with exponential decay models that distinguished time-based and interval-based QT/RR hysteresis. In each subject and for each modelling combination, a best-fit combination of modelling parameters was obtained by seeking minimal regression residuals. The results showed that the response of QT/RR hysteresis appears to be driven by absolute time rather than by the number of cardiac cycles. The speed of QT/RR hysteresis was found decreasing with increasing age whilst the duration of individually rate corrected QTc interval was found increasing with increasing age. Contrary to the longer QTc intervals, QT/RR hysteresis speed was faster in females. QT/RR hysteresis differences between heart rate acceleration and deceleration were not found to be physiologically systematic (i.e., they differed among different healthy subjects), but on average, QT/RR hysteresis speed was found slower after heart rate acceleration than after rate deceleration.

Can QT interval prolongation or dispersion detected in a positive exercise ECG test predict critical coronary artery disease?

Introduction

Exercise electrocardiography (EET) is frequently used in coronary artery disease, but the specificity of this test is very low. In the literature, parameters such as QT prolongation and QT dispersion which show coronary artery disease and arrhythmia were not sufficiently investigated using EET. The aim of this study was to investigate whether QT interval prolongation or dispersion (QT disp) in a positive EET test could predict critical coronary artery disease (CAD).

Material and methods

Patients with a positive exercise test were included in the study. Data regarding QT, QTc (corrected QT interval) and QT disp values before, during and after EET were noted. Critical coronary artery occlusions (≥ 70%) was recorded from coronary angiographic images. Patients were divided into two groups (critical CAD and non-critical CAD).

Results

A total of 192 patients were found to be eligible for the study. There were 126 patients in the non-critical CAD group (group 1) and 66 patients in the critical CAD group (group 2). Recovery QTc, peak QT disp, and recovery QT disp were significantly increased in group 2 (p < 0.001 for each). Also, target heart rate (p = 0.012), basal systolic blood pressure (p = 0.005) and diastolic blood pressure (p < 0.001) were significantly higher in group 1. Recovery QTc (OR = 1.051) and recovery QT disp (OR = 1.117) were determined as the independent predictors for critical CAD. The ROC analysis results indicated that critical CAD could be diagnosed with 90% sensitivity when the recovery QTc cut-off value was set as 404 ms.

Conclusions

In patients with positive EET, prolonged QTc and QT disp values measured during the recovery period would predict critical CAD. Thus, the clinical accuracy of EET may be enhanced.

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.