Arrhythmia/Electrophysiology Derivation and Validation of a Simple Exercise-Based Algorithm for Prediction of Genetic Testing in Relatives of

Background—Genetic testing can diagnose long-QT syndrome (LQTS) in asymptomatic relatives of patients with an identified mutation; however, it is costly and subject to availability. The accuracy of a simple algorithm that incorporates resting and exercise ECG parameters for screening LQTS in asymptomatic relatives was evaluated, with genetic testing as the gold standard. Methods and Results—Asymptomatic first-degree relatives of genetically characterized probands were recruited from 5 centers. QT intervals were measured at rest, during exercise, and during recovery. Receiver operating characteristics were used to establish optimal cutoffs. An algorithm for identifying LQTS carriers was developed in a derivation cohort and validated in an independent cohort. The derivation cohort consisted of 69 relatives (28 with LQT1, 20 with LQT2, and 21 noncarriers). Mean age was 3518 years, and resting corrected QT interval (QTc) was 46639 ms. Abnormal resting QTc (females480 ms; males470 ms) was 100 % specific for gene carrier status, but was observed in only 48 % of patients; however, mutations were observed in 68 % and 42 % of patients with a borderline or normal resting QTc, respectively. Among these patients, 4-minute recovery QTc 445 ms correctly restratified 22 of 25 patients as having LQTS and 19 of 21 patients as being noncarriers. The combination of resting and 4-minute recovery QTc in a screening algorithm yielded a sensitivity of 0.94 and specificity of 0.90 for detecting LQTS carriers. When applied to the validation cohort (n152; 58 with LQT1, 61 with LQT2, and 33 noncarriers; QTc44347 ms), sensitivity was 0.92 and specificity was 0.82. Conclusions—A simple algorithm that incorporates resting and exercise-recovery QTc is useful in identifying LQTS i