Identification of Concealed and Manifest Long QT Syndrome Using a Novel T Wave Analysis Program

Abstract

C ongenital long QT syndrome (LQTS) affects ≈1 in 2000 people and is an important cause of sudden cardiac death in the young. 1 There are 17 known LQTS-susceptibility genes (LQT1-17), but the most common types LQT1, LQT2, and LQT3 account for ≈75% to 80% of all congenital LQTS and over 95% of genetically proven cases.2 The current diagnostic approach is based on measurement of the heart rate-corrected QT interval (QTc) and clinical factors, such as the medical and family history and clinical presentation. However, unless the QTc is repeatedly ≥500 ms without alternative acquired factors present, the QTc from the resting 12-lead ECG is insufficient for diagnosis.3 Genetic testing plays an important role in the diagnosis, risk stratification, tailoring of genotypedirected therapies and for mutation-specific confirmatory testing of appropriate relatives once the index case's diseasecausing mutation has been identified. 4,5 However, despite its excellent diagnostic yield, some genetic test results are difficult to interpret, 6 and access to genetic testing in general can be hampered by insurance reimbursement considerations. Accordingly, there is ongoing need for more refined diagnosis and risk stratification based on readily available, noninvasive means.The 12-lead surface ECG is an inexpensive test that is performed widely and could be used as an efficient diagnostic © 2016 American Heart Association, Inc. Original ArticleBackground-Congenital long QT syndrome (LQTS) is characterized by QT prolongation. However, the QT interval itself is insufficient for diagnosis, unless the corrected QT interval is repeatedly ≥500 ms without an acquired explanation. Further, the majority of LQTS patients have a corrected QT interval below this threshold, and a significant minority has normal resting corrected QT interval values. Here, we aimed to develop and validate a novel, quantitative T wave morphological analysis program to differentiate LQTS patients from healthy controls. Methods and Results-We analyzed a genotyped cohort of 420 patients (22±16 years, 43% male) with either LQT1 (61%) or LQT2 (39%). ECG analysis was conducted using a novel, proprietary T wave analysis program that quantitates subtle changes in T wave morphology. The top 3 discriminating features in each ECG lead were determined and the lead with the best discrimination selected. Classification was performed using a linear discriminant classifier and validated on an untouched cohort. The top 3 features were Tpeak-Tend interval, T wave left slope, and T wave center of gravity x axis (last 25% of the T wave). Lead V6 had the best discrimination. It could distinguish 86.8% of LQTS patients from healthy controls. Moreover, it distinguished 83.33% of patients with concealed LQTS from controls, despite having essentially identical resting corrected QT interval values. Conclusions-T wave quantitative analysis on the 12-lead surface ECG provides an effective, novel tool to distinguish patients with either LQT1/LQT2 from healthy matched controls. It can provide guid...

Quantitative T‐wave morphology assessment from surface ECG is linked with cardiac events risk in genotype‐positive KCNH2 mutation carriers with normal QTc values

“…On the other hand, not all T wave morphology markers have performed as significant risk indicators in the context of LQTS with normal QTc values. The center of gravity x‐axis (last 25% of T‐wave) in lead I, for example, was an independent predictor of cardiac events in LQTS patients with prolonged QTc but not in unaffected mutation carriers …”

“…Earlier attempts to quantify T‐wave morphology in patients with LQTS involved VCG studies and standard 12‐lead ECG based measures such as measured T‐wave slope or T‐wave center of gravity . An advantage of VCG is its ability to address three‐dimensional T wave morphology while other T wave measures are bound to specific surface ECG leads.…”

“…An advantage of VCG is its ability to address three‐dimensional T wave morphology while other T wave measures are bound to specific surface ECG leads. For example, the significant association with cardiac events in LQTS patients with prolonged QTc values was shown regarding the slope of the T‐wave in lead V6 . The earlier reported VCG measures, however, have been limited to computed eigenvector values.…”

“…It is worth stressing that these associations were present in women but not in men who presented with low risk of cardiac events. As sensitivity analysis we repeated the above The hazard ratio for the genotype-negative control population versus the entire genotype negative cohort is also presented 27 for example, was an independent predictor of cardiac events in LQTS patients with prolonged QTc but not in unaffected mutation carriers. 27 Our study demonstrated that cardiac event risk stratification was similarly determined for female patients with LQT2 (with normal QTc values) by our quantitative parameter as by the T-wave morphology as read by electrophysiologist.…”

Automated QT analysis on Holter monitors in pediatric patients can differentiate long QT syndrome from controls

“…Sugrue et al. recently published promising findings using a novel T wave analytics tool and a standard, 10‐second, 12‐lead ECG that was able to differentiate LQTS from normal patients with 83% sensitivity and 91% specificity; however, again proprietary software was utilized that is not yet commercially available …”