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AimsDiagnosis of Long QT syndrome (LQTS) is based on prolongation of the QT interval corrected for heart rate (QTc) on surface ECG and genotyping. However, up to 25% of genotype positive patients have a normal QTc interval. We recently showed that individualized QT interval (QTi) derived from 24 h holter data and defined as the QT value at the intersection of an RR interval of 1,000 ms with the linear regression line fitted through QT-RR data points of each individual patient was superior over QTc to predict mutation status in LQTS families. This study aimed to confirm the diagnostic value of QTi, fine-tune its cut-off value and evaluate intra-individual variability in patients with LQTS.MethodsFrom the Telemetric and Holter ECG Warehouse, 201 recordings from control individuals and 393 recordings from 254 LQTS patients were analysed. Cut-off values were obtained from ROC curves and validated against an in house LQTS and control cohort.ResultsROC curves indicated very good discrimination between controls and LQTS patients with QTi, both in females (AUC 0.96) and males (AUC 0.97). Using a gender dependent cut-off of 445 ms in females and 430 ms in males, a sensitivity of 88% and specificity of 96% were achieved, which was confirmed in the validation cohort. No significant intra-individual variability in QTi was observed in 76 LQTS patients for whom at least two holter recordings were available (483 ± 36 ms vs. 489 ± 42 ms, p = 0.11).ConclusionsThis study confirms our initial findings and supports the use of QTi in the evaluation of LQTS families. Using the novel gender dependent cut-off values, a high diagnostic accuracy was achieved.

Background: Symptomatic children with catecholaminergic polymorphic ventricular tachycardia (CPVT) are at risk for recurrent arrhythmic events. Beta-blockers (BBs) decrease this risk, but studies comparing individual BBs in sizeable cohorts are lacking. We aimed to assess the association between risk for arrhythmic events and type of BB in a large cohort of symptomatic children with CPVT. Methods: From two international registries of patients with CPVT, RYR2 variant-carrying symptomatic children (defined as syncope or sudden cardiac arrest prior to BB initiation and age at start of BB therapy

Additional file 1: Table S1. Demographics of the study cohort - Amsterdam data. Table S2. Distribution of different types of genetic variants for LQTS 1, 2 and 3 – Amsterdam data. Table S3. Prevalence of specific genetic variants for LQTS 1, 2 and 3 - Amsterdam data. Table S4. Single Lead First ECG model performances for LQTS1 - Amsterdam data. The mean of the collected metrics and the corresponding standard deviation (SD) of the 5-fold cross-validation is reported. The DL models were trained using single leads of the first acquired ECG of control and LQTS1 patients. Table S5. Single Lead First ECG model performances for LQTS2 - Amsterdam data. The mean of the collected metrics and the corresponding standard deviation (SD) of the 5-fold cross-validation is reported. The DL models were trained using single leads of the first acquired ECG of control and LQTS2 patients. Table S6. Single Lead First ECG model performances for LQTS3 - Amsterdam data. The mean of the collected metrics and the corresponding standard deviation (SD) of the 5-fold cross-validation is reported. The DL models were trained using single leads of the first acquired ECG of control and LQTS3 patients. Table S7. Model performances for LQTS 1, 2, and 3 - Amsterdam data. The performance of the DL models for patients whose QTc is within the 10th-90th percentile of the overlapping QTc region is reported. The QTc ranges analyzed are 398-475 ms, 403-477 ms, and 403-475 ms for LQTS 1, 2, and 3, respectively. The automatically measured QTc was used (thresholds used to define prolonged QTc: ≥ 450 ms for males, QTc ≥ 460 ms for females). The mean of the collected metrics and the corresponding standard deviation (SD) of the 5-fold cross-validation is reported. First ECG approach: the DL models were trained using the first acquired 12-lead ECGs. All ECG approach: the DL models were trained using all acquired 12-lead ECGs (not only the first acquired) per patient. Table S8. Demographics of the study cohort (Leuven data). Table S9. Distribution of different types of genetic variants for LQTS 1 and 2 – Leuven data. Table S10. Prevalence of specific genetic variants for LQTS 1 and 2 - Leuven data. Fig. S1. 1DCNN architecture. Schematic representation of the proposed 1DCNN architecture. The model was trained using 12-lead ECGs or single lead. The number 12 in the second column represents the 12-lead ECGs, while the number 1 in the bracket represents the use of the single lead. All 12 leads individually were used. Fig. S2. ECG distribution. Bar plots showing the number of ECGs for LQTS 1, 2, and 3 patients used in the ALL ECG approach. Fig. S3. Probability score distribution of control and LQTS patients. Box plots showing the probability score distribution for A) LQTS1, B) LQTS2, and C) LQTS3 and control patients obtained from the DL models trained on the first acquired 12-lead ECGs. The red dotted line represents the threshold used for the ECG classification. Fig. S4. Probability score distribution of control and LQTS patients. Box plots showing the probability score distribution of A) LQTS1, B) LQTS2, and C) LQTS3 and control patients obtained from the DL models trained on all acquired 12-lead ECGs. The red dotted line represents the threshold used for the ECG classification. Fig. S5. QTc value distribution (Amsterdam data). Top: QTc value distributions of LQTS versus control patients. Bottom: table showing the mean, the lower, and the upper limit of the above QTc value distributions. The automatically measured QTc was used (thresholds used to define prolonged QTc: ≥ 450 ms for males, QTc ≥ 460 ms for females).

Aims We identified the first Belgian SCN5A founder mutation, c.4813 + 3_4813 + 6dupGGGT. To describe the clinical spectrum and disease severity associated with this mutation, clinical data of 101 SCN5A founder mutation carriers and 46 non-mutation carrying family members from 25 Belgian families were collected. Methods and results The SCN5A founder mutation was confirmed by haplotype analysis. The clinical history and electrocardiographic parameters of the mutation carriers and their family members were gathered and compared. A cardiac electrical abnormality was observed in the majority (82%) of the mutation carriers. Cardiac conduction defects, defined as PR or QRS prolongation on electrocardiogram (ECG), were most frequent, occurring in 65% of the mutation carriers. Brugada syndrome (BrS) was the second most prevalent phenotype identified in 52%, followed by atrial dysrythmia in 11%. Overall, 33% of tested mutation carriers had a normal sodium channel blocker test. Negative tests were more common in family members distantly related to the proband. Overall, 23% of the mutation carriers were symptomatic, with 8% displaying major adverse events. As many as 13% of the patients tested with a sodium blocker developed ventricular arrhythmia. One family member who did not carry the founder mutation was diagnosed with BrS. Conclusion The high prevalence of symptoms and sensitivity to sodium channel blockers in our founder population highlights the adverse effect of the founder mutation on cardiac conduction. The large phenotypical heterogeneity, variable penetrance, and even non-segregation suggest that other genetic (and environmental) factors modify the disease expression, severity, and outcome in these families.