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

Abstract

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.

Keywords: QT adaptation; QT hysteresis; quantitative ECG; repolarization.

Figure 1

Flowchart of searching and filtering of articles

Figure 2

A simple mathematical model of QT/RR hysteresis. (a) The RR interval time course follows two exponential curves with time constant τ_RR corresponding to exercise and recovery. (b) The effective RR interval is a linear combination of preceding RR intervals with exponentially decaying weights with time constant τ_m. (c) The QT interval is computed as a linear function of the effective RR (Sagie et al. QT/RR relation). (d) Resulting QT/RR loop. The arrows indicate time evolution. The dashed line is the static QT/RR curve. Analytical formulas for the main QT‐hys quantifiers in the context of this simple model are shown on the right‐hand side. Calculation details are available upon request to the corresponding author