BACKGROUND: Long QT syndrome (LQTS) is associated with potentially fatal arrhythmias. Treatment is very effective, but its diagnosis may be challenging. Importantly, different methods are used to assess the QT interval, which makes its recognition difficult. QT experts advocate manual measurements with the tangent or threshold method. However, differences between these methods and their performance in LQTS diagnosis have not been established. We aimed to assess similarities and differences between these 2 methods for QT interval analysis to aid in accurate QT assessment for LQTS. METHODS: Patients with a confirmed pathogenic variant in KCNQ1(LQT1), KCNH2(LQT2), or SCN5A(LQT3) genes and their family members were included. Genotype-positive patients were identified as LQTS cases and genotype-negative family members as controls. ECGs were analyzed with both methods, providing inter-and intrareader validity and diagnostic accuracy. Cutoff values based on control population's 95th and 99th percentiles, and LQTS-patients' 1st and 5th percentiles were established based on the method to correct for heart rate, age, and sex. RESULTS: We included 1484 individuals from 265 families, aged 33±21 years and 55% females. In the total cohort, QT Tangent was 10.4 ms shorter compared with QT Threshold (95% limits of agreement±20.5 ms, P<0.0001). For all genotypes, QT Tangent was shorter than QT Threshold (P<0.0001), but this was less pronounced in LQT2. Both methods yielded a high interand intrareader validity (intraclass correlation coefficient >0.96), and a high diagnostic accuracy (area under the curve >0.84). Using the current guideline cutoff (QTc interval 480 ms), both methods had similar specificity but yielded a different sensitivity. QTc interval cutoff values of QT Tangent were lower compared with QT Threshold and different depending on the correction for heart rate, age, and sex. CONCLUSION: The QT interval varies depending on the method used for its assessment, yet both methods have a high validity and can both be used in diagnosing LQTS. However, for diagnostic purposes current guideline cutoff values yield different results for these 2 methods and could result in inappropriate reassurance or treatment. Adjusted cutoff values are therefore specified for method, correction formula, age, and sex. In addition, a freely accessible online probability calculator for LQTS (www.QTcalculator.org) has been made available as an aid in the interpretation of the QT interval.

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Fetal biometry and Doppler flow patterns are within normal ranges in fetuses with CHD, but show trends over time. Fetal head growth is not associated with the cerebral blood flow pattern or placental function and HC is not influenced by the cerebral arterial oxygen saturation. This article is protected by copyright. All rights reserved.

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The LVIRT is prolonged in LQTS fetuses. Findings of a prolonged N-LVIRT and sinus bradycardia can improve the prenatal detection of fetal LQTS.

BackgroundTo evaluate QT-interval dynamics in patients and in drug safety analysis, beat-to-beat QT-interval measurements are increasingly used. However, interobserver differences, aberrant T-wave morphologies and changes in heart axis might hamper accurate QT-interval measurements.ObjectiveTo develop and validate a QT-interval algorithm robust to heart axis orientation and T-wave morphology that can be applied on a beat-to-beat basis.MethodsAdditionally to standard ECG leads, the root mean square (ECGRMS), standard deviation and vectorcardiogram were used. QRS-onset was defined from the ECGRMS. T-wave end was defined per individual lead and scalar ECG using an automated tangent method. A median of all T-wave ends was used as the general T-wave end per beat.Supine-standing tests of 73 patients with Long-QT syndrome (LQTS) and 54 controls were used because they have wide ranges of RR and QT-intervals as well as changes in T-wave morphology and heart axis orientation. For each subject, automatically estimated QT-intervals in three random complexes chosen from the low, middle and high RR range, were compared with manually measured QT-intervals by three observers.ResultsAfter visual inspection of the randomly selected complexes, 21 complexes were excluded because of evident noise, too flat T-waves or premature ventricular beats. Bland-Altman analyses of automatically and manually determined QT-intervals showed a bias of <4ms and limits of agreement of ±25ms. Intra-class coefficient indicated excellent agreement (>0.9) between the algorithm and all observers individually as well as between the algorithm and the mean QT-interval of the observers.ConclusionOur automated algorithm provides reliable beat-to-beat QT-interval assessment, robust to heart axis and T-wave morphology.