Improving Long-QT syndrome Diagnosis by a Polynomial-Based T-wave Morphology Characterization

Abstract Background Diagnosing long-QT syndrome (LQTS) remains challenging due to a considerable overlap in QT-interval between LQTS and healthy subjects. Characterizing T-wave morphology might improve LQTS diagnosis. Objective To improve LQTS diagnosis by combining new polynomial-based T-wave morphology parameters with the corrected QT-interval (QTc), age and sex in a model. Methods A retrospective cohort consisting of 333 LQTS patients and 345 genotype-negative family members was used in this study. For each patient, a linear combination of the first 2 Hermite-Gauss (HG) polynomials was fitted to the STT-segments of an average complex of all precordial leads and limb leads I and II. The weight coefficients as well as the error of the best fit were used to characterize T-wave morphology. Subjects were classified as LQTS or controls by clinical QTc cut-offs and three support-vector-machine (SVM) models fed with different features. An external cohort consisting of 72 patients and 45 controls was finally used to check the robustness of the models. Results Baseline QTc cut-offs were specific but had low sensitivity in diagnosing LQTS. The model with T-wave morphology features, QTc, age and sex had the best overall accuracy (84%) followed by a model with QTc, age and sex (79%). The Extended model especially performed better in LQT-3 patients (69%). Conclusion T-wave morphologies can be characterized by fitting a linear combination of the first 2 HG-polynomials. Adding T-wave morphology characterizations to age, gender and QTc in an SVM-model improves LQTS diagnosis.