Background and Aims: This study describes outcomes in a large contemporary national cohort of children and adolescents with long QT syndrome (LQTS). Methods: Data analysis of 548 paediatric LQTS patients at 12 German tertiary care centres. Primary outcome was the occurrence of a major arrhythmic event (MAE) defined as sudden cardiac death (SCD), aborted cardiac arrest (ACA) and appropriate implantable cardioverter-defibrillator (ICD) therapy before age 18 years. Results: Patients (48.9% male) presented at a median age of 6.3 (interquartile range 0.5–11.7) years with incidental findings (26.5%), symptoms (31.3%), and for cascade screening (42.2%). Primary outcome was reached in 58 patients (10.6%): SCD in 7 (1.3%), ACA in 40 (7.3%), and appropriate ICD therapy in 11 (2.0%), with an overall event rate of 1.0 per 100 patient-years at risk. During follow-up (before 18 years of age), 92.1% of patients received cardiac medication. An ICD was implanted in 73 patients (13.3%) of whom 25 (34.2%) received appropriate therapy. In multivariate analysis, the likelihood of experiencing a MAE was greater in patients with LQT3 [hazard ratio (HR) 2.5, 95% confidence interval (CI) 1.1–5.6, P = .03], syncope (HR 3.0, 95% CI 1.7–5.5, P < .01), absence of cardiac medication (HR 9.5, 95% CI 5.3–17.3, P < .01), and QTc ≥500 ms (HR 2.9, 95% CI 1.4–6.3, P < .01). In subanalyses, a pathogenic variant in the KCNH2 pore region of LQT2 patients and younger age at symptom presentation were identified as risk factors. Conclusions: Paediatric LQTS patients are at risk for life-threatening arrhythmias at <18 years of age and require a meticulous personalized clinical approach.

Background: The objective of this IRB-approved retrospective monocentric study was to identify risk factors for mortality after surgery for congenital heart defects (CHDs) in pediatric patients using machine learning (ML). CHD belongs to the most common congenital malformations, and remains the leading mortality cause from birth defects. Methods: The most recent available hospital encounter for each patient with an age <18 years hospitalized for CHD-related cardiac surgery between the years 2011 and 2020 was included in this study. The cohort consisted of 1302 eligible patients (mean age [SD]: 402.92 [±562.31] days), who were categorized into four disease groups. A random survival forest (RSF) and the ‘eXtreme Gradient Boosting’ algorithm (XGB) were applied to model mortality (incidence: 5.6% [n = 73 events]). All models were then applied to predict the outcome in an independent holdout test dataset (40% of the cohort). Results: RSF and XGB achieved average C-indices of 0.85 (±0.01) and 0.79 (±0.03), respectively. Feature importance was assessed with ‘SHapley Additive exPlanations’ (SHAP) and ‘Time-dependent explanations of machine learning survival models’ (SurvSHAP(t)), both of which revealed high importance of the maximum values of serum creatinine observed within 72 h post-surgery for both ML methods. Conclusions: ML methods, along with model explainability tools, can reveal interesting insights into mortality risk after surgery for CHD. The proposed analytical workflow can serve as a blueprint for translating the analysis into a federated setting that builds upon the infrastructure of the German Medical Informatics Initiative.

Background: The generation of quality metrics for paediatric heart centre programmes frequently relies on registry data, with all the known benefits and disadvantages. This retrospective monocentric study introduces an algorithm capable of processing unedited clinical data to identify mortality risk factors following paediatric cardiac surgery. Methods: Patients who had undergone cardiac surgery in the department during the period from 2011 to 2020 were included when aged < 18 years. Congenital heart disease (CHD) was categorised into four diagnosis groups through hierarchical integration of the index surgery and CHD diagnosis. We evaluated preoperative, demographic, periprocedural, and postsurgical risk factors. Results: A total of 1700 patients with 2157 hospitalization encounters were included. The risk factors for elevated mortality with the highest degree of significance were extracorporeal membrane oxygenation (hazard ratio 13.97, p < 0.001), weight < 2500 g, patients in the univentricular heart group I, and the creatinine ratio. Conclusions: Beyond confirming established predictors such as ECMO and low body weight < 2500 g, the present analysis highlights the creatinine ratio as a strong laboratory-based predictor of mortality. The applied framework serves as a foundational step towards enabling the real-time utilisation of raw datasets across multiple centres, thereby supporting privacy-preserving and efficient quality metric assessment as well as enhanced risk stratification.

Introduction: Diagnosis and treatment of children and adolescents with suspected Brugada syndrome (BrS) is challenging. Aim of the study was to evaluate the applicability of current diagnostic criteria for pediatric patients with suspected BrS in the German multicenter study COGIA and to assess clinical outcome. Methods: Data analysis of 76 pediatric patients from 12 German tertiary care centers. Primary outcome was defined by the occurrence of a major arrhythmic event (MAE), secondary outcome by symptoms, pharmacotherapy and implantation of devices. MAE was defined as sudden cardiac death (SCD), aborted cardiac arrest (ACA) and appropriate implantable cardioverter‐defibrillator (ICD) therapy. Results: Criteria for BrS diagnosis were fulfilled in 39/76 pediatric patients (51.3%) according to ESC Guidelines, including 34 patients (44.7%) according to Shanghai Score. Genetic testing identified a (likely) pathogenic SCN5A variant in 31/66 tested children (47.0%) (14/30 with clinical diagnosis (46.7%) and 17/36 without clinical diagnosis (47.2%)). Twenty‐four patients (31.6%) showed a spontaneous type 1 ECG pattern, in 6 patients (7.9%) it was induced by fever. Ajmaline challenge was positive in 19/25 patients (76.0%). Syncope occurred in 15 patients (19.7%) and an ICD was implanted in 7 pediatric patients (9.2%). Four patients (5.3%) experienced a MAE (ACA), all of them fulfilling diagnostic criteria. “Symptoms at first consultation” was identified as an independent risk factor for MAE in the whole cohort (HR = 11.3, p = 0.04). Conclusion: Establishing the diagnosis of BrS remains challenging in the pediatric cohort. MAE occurred in 5.3% of patients with suspected BrS. Symptoms at first consultation increased risk for MAE. Clinical Trial Registration: The study is registered at the German Clinical Trial Register (DRKS) of the Federal Institute for Drugs and Medical Devices (BfArM), DRKS‐ID DRKS00028138.