Date of Award

2023

Degree Name

Master of Medicine/Surgery

Schools and Centres

Medicine

First Supervisor

Professor Jamie Vandeberg

Second Supervisor

Dr Adam Hill

Abstract

Prolongation of the corrected QT interval (QTc) is key in diagnosing long QT syndrome (LQTS), however 25-50% of individuals with congenital LQTS (cLQTS) demonstrate a normal resting QTc. T wave morphology (TWM) can distinguish cLQTS subtypes but its role in acquired LQTS (aLQTS) requires further elucidation. Beyond the challenge of concealed LQTS, utilising the QTc is complicated by limitations related to overcorrection at fast and slow heart rates, due to assumptions based on non-physiological QT rate dependence. QT-RR dynamics enable improved heart rate correction and have been shown to distinguish cLQTS from controls, however RR dynamics have not yet been applied to TWM. Thus, the purpose of this body of work is two-fold. Firstly, we sought to identify the specific methodologies of TWM analysis, and the T wave biomarkers used to both identify LQTS subtypes and assess torsadogenic risk. Secondly, we aimed to evaluate whether the RR relationship for the QT and selected T wave electrocardiogram (ECG) parameters acquired from Holter ECG data can differentiate cLQTS from wild type (WT) controls.

A literature review was performed with 17 studies meeting criteria for inclusion. TWM measurements included T wave amplitude, duration, magnitude, Tpeak-Tend, QTpeak, left and right slope, centre of gravity (COG), sigmoidal and polynomial classifiers, repolarising integral, morphology combination score (MCS) and principal component analysis (PCA); and vectorcardiographic biomarkers. cLQTS were distinguished from controls by sigmoidal and polynomial classifiers, MCS, QTpeak (QTp), Tpeak-Tend (TpTe), left slope; and COG x axis. MCS detected aLQTS more significantly than QTc. Flatness, asymmetry and notching, J-Tpeak; and Tpeak-Tend correlated with QTc in aLQTS.

Multichannel block in aLQTS was identified by early repolarisation (ERD30%) and late repolarisation (LRD30%), with ERD reflecting hERGspecific blockade. Cardiac events were predicted in cLQTS by T wave flatness, notching and inversion in leads II and V5, left slope in lead V6; and COG last 25% in lead I. T wave right slope in lead I and T-roundness achieved this in aLQTS. RR dynamics were subsequently analysed using Holter ECGs acquired from the Telemetric and Holter ECG Warehouse (THEW) database for WT, LQTS1, LQTS2 7 and LQTS3 subjects. Automated ECG processing was undertaken to select ECG parameters, including the QT, T wave height (Th), QTp and TpTe. Each parameter was graphed against the RR interval, enabling evaluation of linear parameters and correlation-based analyses. QT-RR slope dynamics distinguished WT from all cLQTS subtypes (P

In conclusion, numerous TWM ECG biomarkers capable of supplementing QTc assessment have been identified, with the ability to differentiate genotypes, detect concealed LQTS, differentiate aLQTS from cLQTS; and determine multichannel versus hERG channel blockade. Further, we have produced the initial report on RR slope dynamics based on Holter recordings which integrates both QT-RR slope data and that of T wave parameters, which shows individualised RR slope dynamics can distinguish cLQTS subtypes from WT for the QT interval and LQTS2 from WT for the Th, QTp and TpTe. Interestingly, our results also suggest that inclusion of Th and Th-RR slope dynamics with QT-RR data may enhance diagnosis and risk stratification.

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