
Categorization and theoretical comparison of quantitative methods for assessing QT/RR hysteresis
Author(s) -
Gravel Hugo,
Curnier Daniel,
Dahdah Nagib,
Jacquemet Vincent
Publication year - 2017
Publication title -
annals of noninvasive electrocardiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.494
H-Index - 48
eISSN - 1542-474X
pISSN - 1082-720X
DOI - 10.1111/anec.12463
Subject(s) - categorization , qt interval , repolarization , medicine , statistics , computer science , cognitive psychology , artificial intelligence , mathematics , psychology , electrophysiology
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 II a and II b 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.