Sex Differences and Utility of Treadmill Testing in Long-QT Syndrome

Abstract

BACKGROUND: Diagnosis of congenital long-QT syndrome (LQTS) is complicated by phenotypic ambiguity, with a frequent normal-to-borderline resting QT interval. A 3-step algorithm based on exercise response of the corrected QT interval (QTc) was previously developed to diagnose patients with LQTS and predict subtype. This study evaluated the 3-step algorithm in a population that is more representative of the general population with LQTS with milder phenotypes and establishes sex-specific cutoffs beyond the resting QTc. METHODS AND RESULTS: We identified 208 LQTS likely pathogenic or pathogenic KCNQ1 or KCNH2 variant carriers in the Canadian NLQTS (National Long-QT Syndrome) Registry and 215 unaffected controls from the HiRO (Hearts in Rhythm Organization) Registry. Exercise treadmill tests were analyzed across the 5 stages of the Bruce protocol. The predictive value of exercise ECG characteristics was analyzed using receiver operating characteristic curve analysis to identify optimal cutoff values. A total of 78% of male carriers and 74% of female carriers had a resting QTc value in the normal-to-borderline range. The 4-minute recovery QTc demonstrated the best predictive value for carrier status in both sexes, with better LQTS ascer-tainment in female patients (area under the curve, 0.90 versus 0.82), with greater sensitivity and specificity. The optimal cutoff value for the 4-minute recovery period was 440 milliseconds for male patients and 450 milliseconds for female patients. The 1-minute recovery QTc had the best predictive value in female patients for differentiating LQTS1 versus LQTS2 (area under the curve, 0.82), and the peak exercise QTc had a marginally better predictive value in male patients for subtype with (area under the curve, 0.71). The optimal cutoff value for the 1-minute recovery period was 435 milliseconds for male patients and 455 milliseconds for femal patients. CONCLUSIONS: The 3-step QT exercise algorithm is a valid tool for the diagnosis of LQTS in a general population with more frequent ambiguity in phenotype. The algorithm is a simple and reliable method for the identification and prediction of the 2 major genotypes of LQTS.