Citations

Abstract Background and Aims Patients with catecholaminergic polymorphic ventricular tachycardia (CPVT) are at risk for potentially life-threatening arrhythmic events (AEs) even while treated with β-blockers. The aim was to develop a model for individualized prediction of AEs in patients with RYR2-mediated CPVT on β-blocker monotherapy. Methods The derivation and independent validation cohorts included 743 and 129 patients, respectively. AEs were defined as arrhythmic syncope, appropriate implantable cardioverter–defibrillator shock, sudden cardiac arrest (SCA), and sudden cardiac death. Near-fatal or fatal AEs (nf/fAEs) included all AEs except for arrhythmic syncope. Prediction models using Cox regression were developed and internally and externally validated. Results A total of 102 (13.7%) patients in the derivation cohort and 24 (18.6%) patients in the validation cohort experienced ≥1 AE over a median follow-up of 5.1 [interquartile range (IQR), 7.7] and 2.4 (IQR, 4.4) years, respectively. Predictors of AE were arrhythmic syncope or SCA prior to diagnosis and age at β-blocker initiation. In the derivation and validation cohorts, the optimism-corrected C-indices of the models for AE were 0.67 [95% confidence interval (CI) 0.62–0.72] and 0.59 (95% CI 0.48–0.71), respectively. For nf/fAEs, ventricular arrhythmia severity before β-blocker initiation was a fourth independent predictor, and C-indices of the models in the derivation and validation cohorts were 0.74 (95% CI 0.68–0.80) and 0.60 (95% CI 0.47–0.72), respectively. In the derivation cohort, calibration slopes were 1.00 (95% CI 0.59–1.41) for AE and 1.00 (95% CI 0.69–1.32) for nf/fAE. Conclusions These externally validated risk prediction models using clinical parameters accurately distinguished CPVT patients on β-blocker monotherapy at low and high risk for future AEs while treated with β-blockers. These models provide guidance for implementation of clinical management therapies to prevent AEs in patients with CPVT.

Abstract Aims In long QT syndrome (LQTS), primary prevention improves outcome; thus, early identification is key. The most common LQTS phenotype is a foetal heart rate (FHR) < 3rd percentile for gestational age (GA) but the effects of cohort, genotype, variant, and maternal β-blocker therapy on FHR are unknown. We assessed the influence of these factors on FHR in pregnancies with familial LQTS and developed a FHR/GA threshold for LQTS. Methods and results In an international cohort of pregnancies in which one parent had LQTS, LQTS genotype, familial variant, and maternal β-blocker effects on FHR were assessed. We developed a testing algorithm for LQTS using FHR and GA as continuous predictors. Data included 1966 FHRs at 7–42 weeks’ GA from 267 pregnancies/164 LQTS families [220 LQTS type 1 (LQT1), 35 LQTS type 2 (LQT2), and 12 LQTS type 3 (LQT3)]. The FHRs were significantly lower in LQT1 and LQT2 but not LQT3 or LQTS negative. The LQT1 variants with non-nonsense and severe function loss (current density or β-adrenergic response) had lower FHR. Maternal β-blockers potentiated bradycardia in LQT1 and LQT2 but did not affect FHR in LQTS negative. A FHR/GA threshold predicted LQT1 and LQT2 with 74.9% accuracy, 71% sensitivity, and 81% specificity. Conclusion Genotype, LQT1 variant, and maternal β-blocker therapy affect FHR. A predictive threshold of FHR/GA significantly improves the accuracy, sensitivity, and specificity for LQT1 and LQT2, above the infant’s a priori 50% probability. We speculate this model may be useful in screening for LQTS in perinatal subjects without a known LQTS family history.

Background: Long QT syndrome (LQTS) is a rare genetic disorder and a major preventable cause of sudden cardiac death in the young. A causal rare genetic variant with large effect size is identified in up to 80% of probands (genotype positive) and cascade family screening shows incomplete penetrance of genetic variants. Furthermore, a proportion of cases meeting diagnostic criteria for LQTS remain genetically elusive despite genetic testing of established genes (genotype negative). These observations raise the possibility that common genetic variants with small effect size contribute to the clinical picture of LQTS. This study aimed to characterize and quantify the contribution of common genetic variation to LQTS disease susceptibility. Methods: We conducted genome-wide association studies followed by transethnic meta-analysis in 1656 unrelated patients with LQTS of European or Japanese ancestry and 9890 controls to identify susceptibility single nucleotide polymorphisms. We estimated the common variant heritability of LQTS and tested the genetic correlation between LQTS susceptibility and other cardiac traits. Furthermore, we tested the aggregate effect of the 68 single nucleotide polymorphisms previously associated with the QT-interval in the general population using a polygenic risk score. Results: Genome-wide association analysis identified 3 loci associated with LQTS at genome-wide statistical significance ( P <5×10 −8 ) near NOS1AP , KCNQ1 , and KLF12 , and 1 missense variant in KCNE1 (p.Asp85Asn) at the suggestive threshold ( P <10 −6 ). Heritability analyses showed that ≈15% of variance in overall LQTS susceptibility was attributable to common genetic variation ( h2SNP 0.148; standard error 0.019). LQTS susceptibility showed a strong genome-wide genetic correlation with the QT-interval in the general population (r g =0.40; P =3.2×10 −3 ). The polygenic risk score comprising common variants previously associated with the QT-interval in the general population was greater in LQTS cases compared with controls ( P <10−13), and it is notable that, among patients with LQTS, this polygenic risk score was greater in patients who were genotype negative compared with those who were genotype positive ( P <0.005). Conclusions: This work establishes an important role for common genetic variation in susceptibility to LQTS. We demonstrate overlap between genetic control of the QT-interval in the general population and genetic factors contributing to LQTS susceptibility. Using polygenic risk score analyses aggregating common genetic variants that modulate the QT-interval in the general population, we provide evidence for a polygenic architecture in genotype negative LQTS.

Background Mutations in genes encoding ion channels or sarcomeric proteins are an important cause of hereditary cardiac disease. However, the severity of the resultant disease varies considerably even among those with an identical mutation. Such clinical variation is often thought to be explained largely by differences in genetic background or ‘modifier genes’. We aimed to test the prediction that identical genetic backgrounds result in largely similar clinical expression of a cardiac disease causing mutation, by studying the clinical expression of mutations causing cardiac disease in monozygotic twins. Methods We compared first available clinical information on 46 monozygotic twin pairs and 59 control pairs that had either a hereditary cardiomyopathy or channelopathy. Results Despite limited power of this study, we found significant heritability for corrected QT interval (QTc) in long QT syndrome (LQTS). We could not detect significant heritability for structural traits, but found a significant environmental effect on thickness of the interventricular septum in hypertrophic cardiomyopathy. Conclusions Our study confirms previously found robust heritability for electrical traits like QTc in LQTS, and adds information on low or lacking heritability for structural traits in heritable cardiomyopathies. This may steer the search for genetic modifiers in heritable cardiac disease.

Background: Long-QT syndrome (LQTS), an inherited cardiac repolarization disorder, is an important cause of fetal and neonatal mortality. Detecting LQTS prenatally is challenging. A fetal heart rate (FHR) less than third percentile for gestational age is specific for LQTS, but the sensitivity is only ≈50%. Left ventricular isovolumetric relaxation time (LVIRT) was evaluated as a potential diagnostic marker for fetal LQTS. Methods and Results: LV isovolumetric contraction time, LV ejection time, LVIRT, cycle length, and FHR were measured using pulsed Doppler waveforms in fetuses. Time intervals were expressed as percentages of cycle length, and the LV myocardial performance index was calculated. Single measurements were stratified by gestational age and compared between LQTS fetuses and controls. Receiver-operator curves were performed for FHR and normalized LVIRT (N-LVIRT). A linear mixed-effect model including multiple measurements was used to analyze trends in FHR, N-LVIRT, and LV myocardial performance index. There were 33 LQTS fetuses and 469 controls included. In LQTS fetuses, the LVIRT was prolonged in all gestational age groups ( P <0.001), as was the N-LVIRT. The best cutoff to diagnose LQTS was N-LVIRT ≥11.3 at ≤20 weeks (92% sensitivity, 70% specificity). Simultaneous analysis of N-LVIRT and FHR improved the sensitivity and specificity for LQTS (area under the curve=0.96; 95% confidence interval, 0.82–1.00 at 21–30 weeks). N-LVIRT, LV myocardial performance index, and FHR trends differed significantly between LQTS fetuses and controls through gestation. Conclusions: The LVIRT is prolonged in LQTS fetuses. Findings of a prolonged N-LVIRT and sinus bradycardia can improve the prenatal detection of fetal LQTS.