SCN5A mutations in 442 neonates and children: genotype-phenotype correlation and identification of higher-risk subgroups.

Aims To clarify the clinical characteristics and outcomes of children with SCN5A-mediated disease and to improve their risk stratification. Methods and results A multicentre, international, retrospective cohort study was conducted in 25 tertiary hospitals in 13 countries between 1990 and 2015. All patients ≤16 years of age diagnosed with a genetically confirmed SCN5A mutation were included in the analysis. There was no restriction made based on their clinical diagnosis. A total of 442 children {55.7% boys, 40.3% probands, median age: 8.0 [interquartile range (IQR) 9.5] years} from 350 families were included; 67.9% were asymptomatic at diagnosis. Four main phenotypes were identified: isolated progressive cardiac conduction disorders (25.6%), overlap phenotype (15.6%), isolated long QT syndrome type 3 (10.6%), and isolated Brugada syndrome type 1 (1.8%); 44.3% had a negative electrocardiogram phenotype. During a median follow-up of 5.9 (IQR 5.9) years, 272 cardiac events (CEs) occurred in 139 (31.5%) patients. Patients whose mutation localized in the C-terminus had a lower risk. Compound genotype, both gain- and loss-of-function SCN5A mutation, age ≤1 year at diagnosis in probands and age ≤1 year at diagnosis in non-probands were independent predictors of CE. Conclusion In this large paediatric cohort of SCN5A mutation-positive subjects, cardiac conduction disorders were the most prevalent phenotype; CEs occurred in about one-third of genotype-positive children, and several independent risk factors were identified, including age ≤1 year at diagnosis, compound mutation, and mutation with both gain- and loss-of-function

Support vector machine-based assessment of the T-wave morphology improves long QT syndrome diagnosis

Aims: Diagnosing long QT syndrome (LQTS) is challenging due to a considerable overlap of the QTc-interval between LQTS patients and healthy controls. The aim of this study was to investigate the added value of T-wave morphology markers obtained from 12-lead electrocardiograms (ECGs) in diagnosing LQTS in a large cohort of gene-positive LQTS patients and gene-negative family members using a support vector machine. Methods and results: A retrospective study was performed including 688 digital 12-lead ECGs recorded from genotype-positive LQTS patients and genotype-negative relatives at their first visit. Two models were trained and tested equally: a baseline model with age, gender, RR-interval, QT-interval, and QTc-intervals as inputs and an extended model including morphology features as well. The best performing baseline model showed an area under the receiver-operating characteristic curve (AUC) of 0.821, whereas the extended model showed an AUC of 0.901. Sensitivity and specificity at the maximal Youden's indexes changed from 0.694 and 0.829 with the baseline model to 0.820 and 0.861 with the extended model. Compared with clinically used QTc-interval cut-off values (>480 ms), the extended model showed a major drop in false negative classifications of LQTS patients. Conclusion: The support vector machine-based extended model with T-wave morphology markers resulted in a major rise in sensitivity and specificity at the maximal Youden's index. From this, it can be concluded that T-wave morphology assessment has an added value in the diagnosis of LQTS

Determination and Interpretation of the QT Interval. Comprehensive Analysis of a Large Cohort of Long QT Syndrome Patients and Controls

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 3321 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, P0.96), 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

Syncope in Brugada syndrome: prevalence, clinical significance, and clues from history taking to distinguish arrhythmic from nonarrhythmic causes

Syncope in Brugada syndrome (BrS) patients is a sign of increased risk for sudden cardiac death and usually is ascribed to cardiac arrhythmias. However, syncope often occurs in the general population, mostly from nonarrhythmic causes (eg, reflex syncope). The purpose of this study was to distinguish arrhythmic events from nonarrhythmic syncope in BrS and to establish the clinical relevance of nonarrhythmic syncope. We reviewed the patient records of 342 consecutively included BrS patients and conducted systematic interviews in 141 patients with aborted cardiac arrest (ACA) or syncope. In total, 23 patients (7%) experienced ECG-documented ACA and 118 (34%) syncope; of these 118, 67 (57%) were diagnosed with suspected nonarrhythmic syncope. Compared to suspected nonarrhythmic syncope patients, ACA patients were older at first event (45 vs 20 years), were more likely to be male (relative risk 2.1) and to have urinary incontinence (relative risk 4.6), and were less likely to report prodromes. ACA was never triggered by hot/crowded surroundings, pain or other emotional stress, seeing blood, or prolonged standing. During follow-up (median 54 months), ACA rate was 8.7% per year among ACA patients and 0% per year among suspected nonarrhythmic syncope patients. Syncope, especially nonarrhythmic syncope, often occurs in BrS. The high incidence of nonarrhythmic syncope must be taken into account during risk stratification. Arrhythmic events and nonarrhythmic syncope may be distinguished by clinical characteristics (absence of prodromes and, particularly, specific triggers), demonstrating the importance of systematic history takin

Improving long QT syndrome diagnosis by a polynomial-based T-wave morphology characterization

BACKGROUND Diagnosing long QT syndrome (LQTS) remains challenging because of a considerable overlap in QT interval between patients with LQTS and healthy subjects. Characterizing T-wave morphology might improve LQTS diagnosis. OBJECTIVE The purpose of this study was 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 patients with LQTS 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 patients with LQTS or controls by clinical QTc cutoffs and 3 support vector machine 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 cutoffs 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 model with T-wave morphology features especially performed better in LQTS type 3 patients (69%). CONCLUSION T-wave morphologies can be characterized by fitting a linear combination of the first 2 Hermite-Gauss polynomials. Adding T-wave morphology characterization to age, sex, and QTc in a support vector machine model improves LQTS diagnosis

Improving long QT syndrome diagnosis by a polynomial-based T-wave morphology characterization

Background: Diagnosing long QT syndrome (LQTS) remains challenging because of a considerable overlap in QT interval between patients with LQTS and healthy subjects. Characterizing T-wave morphology might improve LQTS diagnosis. Objective: The purpose of this study was 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 patients with LQTS 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 patients with LQTS or controls by clinical QTc cutoffs and 3 support vector machine 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 cutoffs 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 model with T-wave morphology features especially performed better in LQTS type 3 patients (69%). Conclusion: T-wave morphologies can be characterized by fitting a linear combination of the first 2 Hermite-Gauss polynomials. Adding T-wave morphology characterization to age, sex, and QTc in a support vector machine model improves LQTS diagnosis

Growth of the aortic root in children and young adults with Marfan syndrome

OBJECTIVES: The primary aim was to gain insight into the growth of the aortic root in children and young adults with Marfan syndrome (MFS). Furthermore, we aimed to identify a clinical profile of patients with MFS who require an aortic root replacement at a young age with specific interest in age, sex, height and fibrillin-1 (FBN1) genotype. METHODS: Aortic root dimensions of 97 patients with MFS between 0 year and 20 years and 30 controls were serially assessed with echocardiography. Trends were analysed using a linear mixed-effect model. Additionally, including only patients with MFS, we allowed trends to differ by sex, aortic root replacement and type of FBN1 mutation. RESULTS: Average aortic root dilatation in patients with MFS became more pronounced after the age of 8 years. In the MFS cohort, male patients had a significantly greater aortic root diameter than female patients, which was in close relationship with patient height. There was no difference in aortic root growth between children with dominant negative (DN) or haploinsufficient FBN1 mutations. However, DN-FBN1 variants resulting in loss of cysteine content were associated with a more severe phenotype. Eleven children needed an aortic root replacement. Compared with patients with MFS without aortic root surgery, these children had a significantly larger aortic root diameter from an early age. CONCLUSIONS: This study provides clinically useful longitudinal growth charts on aortic root growth in children and young adults with MFS. Children requiring prophylactic aortic root replacement during childhood can be identified at a young age. Our growth charts can help clinicians in decision making with regard to follow-up and prophylactic therapy. Loss of cysteine content in the FBN1 protein was associated with larger aortic root dimensions

Effect of Age and Sex on the QTc Interval in Children and Adolescents With Type 1 and 2 Long-QT Syndrome

BACKGROUND: In congenital long-QT syndrome, age, sex, and genotype have been associated with cardiac events, but their effect on the trend in QTc interval has never been established. We, therefore, aimed to assess the effect of age and sex on the QTc interval in children and adolescents with type 1 (LQT1) and type 2 (LQT2) long-QT syndrome. METHODS AND RESULTS: QTc intervals of 12-lead resting electrocardiograms were determined, and trends over time were analyzed using a linear mixed-effects model. The study included 278 patients with a median follow-up of 4 years (interquartile range, 1-9) and a median number of 6 (interquartile range, 2-10) electrocardiograms per patient. Both LQT1 and LQT2 male patients showed QTc interval shortening after the onset of puberty. In LQT2 male patients, this was preceded by a progressive QTc interval prolongation. In LQT1, after the age of 12 years, male patients had a significantly shorter QTc interval than female patients. In LQT2, during the first years of life and from 14 to 26 years, male patients had a significantly shorter QTc interval than female patients. On the contrary, between 5 and 14 years, LQT2 male patients had significantly longer QTc interval than LQT2 female patients. CONCLUSIONS: There is a significant effect of age and sex on the QTc interval in long-QT syndrome, with a unique pattern per genotype. The age of 12 to 14 years is an important transitional period. In the risk stratification and management of long-QT syndrome patients, clinicians should be aware of these age-, sex-, and genotype-related trends in QTc interval and especially the important role of the onset of puberty