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The European Congenital Heart Surgeons Association (ECHSA) Congenital Database (CD) is the second largest clinical pediatric and congenital cardiac surgical database in the world and the largest in Europe, where various smaller national or regional databases exist. Despite the dramatic increase in interventional cardiology procedures over recent years, only scattered national or regional databases of such procedures exist in Europe. Most importantly, no congenital cardiac database exists in the world that seamlessly combines both surgical and interventional cardiology data on an international level; therefore, the outcomes of surgical and interventional procedures performed on the same or similar patients cannot easily be tracked, assessed, and analyzed. In order to fill this important gap in our capability to gather and analyze information on our common patients, ECHSA and The Association for European Paediatric and Congenital Cardiology (AEPC) have embarked on a collaborative effort to expand the ECHSA-CD with a new module designed to capture data about interventional cardiology procedures. The purpose of this manuscript is to describe the concept, the structure, and the function of the new AEPC Interventional Cardiology Part of the ECHSA-CD, as well as the potentially valuable synergies provided by the shared interventional and surgical analyses of outcomes of patients. The new AEPC Interventional Cardiology Part of the ECHSA-CD will allow centers to have access to robust surgical and transcatheter outcome data from their own center, as well as robust national and international aggregate outcome data for benchmarking. Each contributing center or department will have access to their own data, as well as aggregate data from the AEPC Interventional Cardiology Part of the ECHSA-CD. The new AEPC Interventional Cardiology Part of the ECHSA-CD will allow cardiology centers to have access to aggregate cardiology data, just as surgical centers already have access to ag

Background: We have previously shown that the machine learning methodology of optimal classification trees (OCTs) can accurately predict risk after congenital heart surgery (CHS). We have now applied this methodology to define benchmarking standards after CHS, permitting case-adjusted hospital-specific performance evaluation. Methods: The European Congenital Heart Surgeons Association Congenital Database data subset (31 792 patients) who had undergone any of the 10 “benchmark procedure group” primary procedures were analyzed. OCT models were built predicting hospital mortality (HM), and prolonged postoperative mechanical ventilatory support time (MVST) or length of hospital stay (LOS), thereby establishing case-adjusted benchmarking standards reflecting the overall performance of all participating hospitals, designated as the “virtual hospital.” These models were then used to predict individual hospitals’ expected outcomes (both aggregate and, importantly, for risk-matched patient cohorts) for their own specific cases and case-mix, based on OCT analysis of aggregate data from the “virtual hospital.” Results: The raw average rates were HM = 4.4%, MVST = 15.3%, and LOS = 15.5%. Of 64 participating centers, in comparison with each hospital's specific case-adjusted benchmark, 17.0% statistically (under 90% confidence intervals) overperformed and 26.4% underperformed with respect to the predicted outcomes for their own specific cases and case-mix. For MVST and LOS, overperformers were 34.0% and 26.4%, and underperformers were 28.3% and 43.4%, respectively. OCT analyses reveal hospital-specific patient cohorts of either overperformance or underperformance. Conclusions: OCT benchmarking analysis can assess hospital-specific case-adjusted performance after CHS, both overall and patient cohort-specific, serving as a tool for hospital self-assessment and quality improvement.

Objective: Risk assessment tools typically used in congenital heart surgery (CHS) assume that various possible risk factors interact in a linear and additive fashion, an assumption that may not reflect reality. Using artificial intelligence techniques, we sought to develop nonlinear models for predicting outcomes in CHS. Methods: We built machine learning (ML) models to predict mortality, postoperative mechanical ventilatory support time (MVST), and hospital length of stay (LOS) for patients who underwent CHS, based on data of more than 235,000 patients and 295,000 operations provided by the European Congenital Heart Surgeons Association Congenital Database. We used optimal classification trees (OCTs) methodology for its interpretability and accuracy, and compared to logistic regression and state-of-the-art ML methods (Random Forests, Gradient Boosting), reporting their area under the curve (AUC or c-statistic) for both training and testing data sets. Results: Optimal classification trees achieve outstanding performance across all three models (mortality AUC = 0.86, prolonged MVST AUC = 0.85, prolonged LOS AUC = 0.82), while being intuitively interpretable. The most significant predictors of mortality are procedure, age, and weight, followed by days since previous admission and any general preoperative patient risk factors. Conclusions: The nonlinear ML-based models of OCTs are intuitively interpretable and provide superior predictive power. The associated risk calculator allows easy, accurate, and understandable estimation of individual patient risks, in the theoretical framework of the average performance of all centers represented in the database. This methodology has the potential to facilitate decision-making and resource optimizatio

We sought to determine the indications, type, and outcomes of reoperations on the aortic root after repair of tetralogy of Fallot (TOF). Eleven centers belonging to the European Congenital Heart Surgeons Association contributed to the data collection process. We included 36 patients who underwent surgical procedures on the aortic root, including surgery on the aortic valve and ascending aorta, between January 1975 and December 2017. Original diagnoses included TOF-pulmonary stenosis (n = 18) and TOF-pulmonary atresia (n = 18). The main indications for reoperation were aortic insufficiency (n = 19, 53%), aortic insufficiency and dilatation of the ascending aorta (n = 10, 28%), aortic root dilatation (n = 4, 11%), and ascending aorta dilatation (n = 3, 8%). The median age at reoperation was 30.4 years (interquartile range 20.3-45.3 years), and mechanical aortic valve replacement was the most common procedure performed. Five patients died early after reoperation (14%), and larger ascending aorta diameters were associated with early mortality (P = .04). The median age at the last follow-up was 41.4 years (interquartile range 24.5-51.6 years). Late death occurred in five patients (5/31, 16%). Most survivors (15/26, 58%) were asymptomatic at the last clinical examination (New York Heart Association, NYHA class I). The remaining patients were NYHA class II (n = 7) and III (n = 3). The most common symptoms were fatigue (n = 5), dyspnea (n = 4), and exercise intolerance (n = 3). Reoperations on the aortic root are infrequent but may become necessary late after TOF repair. The main indications for reoperation are aortic insufficiency, either isolated or associated with a dilatation of the ascending aorta. The surgical risk at reoperation was high and the presence of ascending aorta dilation is related to higher mortality.