Abstract
Introduction: Congenital long QT syndrome (LQTS) is a sudden cardiac death-predisposing genetic disorder that has a characteristic electrocardiographic signature. Specific LQTS genotypes have been associated classically with different qualitative electrocardiographic ST - T wave patterns; however, clinical recognition of these patterns could be improved by an automated and quantitative method. We therefore aimed to use a novel, quantitative T wave morphological analysis technique from 12-lead surface ECGs to identify genotype specific LQTS ECG patterns. Methods: We analyzed a genotyped cohort of 420 patients (22 ± 15.7years, 43% male) with either LQT1 (61%) or LQT2 (39%). ECG analysis was conducted using a custom T wave program to automatically detect subtle changes in T wave morphology across all 12 Leads. Classification was performed using the top three T wave features in a linear discriminant classifier by 10x10 cross-validation. Results: The top three discriminant features were (1) a steeper T wave left slope for LQT1 in lead II (2950 vs 1700 mV/ms, p < 0.0001), (2) a greater center of gravity in 1st segment of T wave in lead aVR in LQT1 (0.248 vs 0.209, p<0.0001) and (3) a smaller Tpeak - Tend/QT ratio in lead aVR in LQT1 (0.20 vs 0.24, p<0.0001). Using these top three features, we were able to successfully differentiate LQT1 and LQT2 in 79% of cases (figure 1). Conclusions: An automated morphological T wave analysis was able to discriminate between the two most common LQTS genotypes. This novel method enhances phenotypic characterization of LQT and could have a significant clinical impact on the diagnosis and management of patients with suspected LQTS.
Published Version
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