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...