Electrocardiogram-Based Deep Learning to Predict Mortality in Repaired Tetralogy of Fallot.

Background: Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis shows promise to predict mortality in adults with acquired cardiovascular diseases. However, its application to the growing repaired tetralogy of Fallot (rTOF) population remains unexplored.

Objectives: This study aimed to develop and externally validate an AI-ECG model to predict 5-year mortality in rTOF.

Deep Learning-Based Electrocardiogram Analysis Predicts Biventricular Dysfunction and Dilation in Congenital Heart Disease.

Background: Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis shows promise to detect biventricular pathophysiology. However, AI-ECG analysis remains underexplored in congenital heart disease (CHD).

Objectives: The purpose of this study was to develop and externally validate an AI-ECG model to predict cardiovascular magnetic resonance (CMR)-defined biventricular dysfunction/dilation in patients with CHD.

HVSMR-2.0: A 3D cardiovascular MR dataset for whole-heart segmentation in congenital heart disease.

Patients with congenital heart disease often have cardiac anatomy that deviates significantly from normal, frequently requiring multiple heart surgeries. Image segmentation from a preoperative cardiovascular magnetic resonance (CMR) scan would enable creation of patient-specific 3D surface models of the heart, which have potential to improve surgical planning, enable surgical simulation, and allow automatic computation of quantitative metrics of heart function. However, there is no publicly available CMR dataset for whole-heart segmentation in patients with congenital heart disease.

Development and Validation of a Mortality Risk Score for Repaired Tetralogy of Fallot.

Background: Robust risk assessment is crucial for the growing repaired tetralogy of Fallot population at risk of major adverse clinical outcomes; however, current tools are hindered by lack of validation. This study aims to develop and validate a risk prediction model for death in the repaired tetralogy of Fallot population.

Methods And Results: Patients with repaired tetralogy of Fallot enrolled in the INDICATOR (International Multicenter Tetralogy of Fallot Registry) cohort with clinical, arrhythmia, cardiac magnetic resonance, and outcome data were included.

Surgical debulking of large ventricular fibromas in children.

Objective: This study aims to provide an update on the clinical presentation, diagnostic workup, operative strategies, and midterm outcomes in children undergoing ventricular fibroma resection.

Methods: Single-center, retrospective cohort study of patients undergoing ventricular fibroma resection between 2000 and 2023.

Results: Among 52 patients, median age at surgery was 2.

The effects of pregnancy in subjects with repaired tetralogy of Fallot.

Background: Previous reports reveal inconsistent findings of right ventricular (RV) changes following pregnancy in subjects with repaired tetralogy of Fallot (rTOF).

Methods: A two-center, retrospective cohort study which included women with rTOF who completed pregnancy that were matched to nulliparous women with rTOF by age at the time of baseline cardiac magnetic resonance (CMR), RV ejection fraction (RVEF), and indexed RV end-diastolic volume (RVEDVi). Pre-pregnancy and postpartum cardiac magnetic resonance (CMR) were analyzed and compared to sequential CMR of nulliparous subjects with rTOF.

Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling.

Background: Artificial intelligence-enhanced ECG analysis shows promise to detect ventricular dysfunction and remodeling in adult populations. However, its application to pediatric populations remains underexplored.

Methods: A convolutional neural network was trained on paired ECG-echocardiograms (≤2 days apart) from patients ≤18 years of age without major congenital heart disease to detect human expert-classified greater than mild left ventricular (LV) dysfunction, hypertrophy, and dilation (individually and as a composite outcome).

Right Heart Remodeling After Pulmonary Valve Replacement in Patients With Pulmonary Atresia or Critical Stenosis With Intact Ventricular Septum.

Background Patients with pulmonary atresia or critical pulmonary stenosis with intact ventricular septum (PA/IVS) and biventricular circulation may require pulmonary valve replacement (PVR). Right ventricular (RV) remodeling after PVR is well described in tetralogy of Fallot (TOF); we sought to investigate RV changes in PA/IVS using cardiac magnetic resonance imaging. Methods and Results A retrospective cohort of patients with PA/IVS who underwent PVR at Boston Children's Hospital from 1995 to 2021 with cardiac magnetic resonance imaging before and after PVR was matched 1:3 with patients with TOF by age at PVR.

Impact of Right Ventricular Surface Area-to-Volume Ratio on Ventricular Remodeling After Pulmonary Valve Replacement.

Optimal reverse remodeling of the right ventricle (RV), a sentinel goal of pulmonary valve replacement (PVR) in patients with repaired tetralogy of Fallot, is not fully predicted by volume-based pre-PVR parameters. Our objectives were to characterize novel geometric RV parameters in patients receiving PVR and in controls, and to identify associations between these parameters and chamber remodeling post-PVR. Secondary analysis was performed on cardiac magnetic resonance (CMR) data from 60 patients enrolled in a randomized trial of PVR with and without surgical RV remodeling.

Machine Learning for Prediction of Adverse Cardiovascular Events in Adults With Repaired Tetralogy of Fallot Using Clinical and Cardiovascular Magnetic Resonance Imaging Variables.

Background: Existing models for prediction of major adverse cardiovascular events (MACE) after repair of tetralogy of Fallot have been limited by modest predictive capacity and limited applicability to routine clinical practice. We hypothesized that an artificial intelligence model using an array of parameters would enhance 5-year MACE prediction in adults with repaired tetralogy of Fallot.

Methods: A machine learning algorithm was applied to 2 nonoverlapping, institutional databases of adults with repaired tetralogy of Fallot: (1) for model development, a prospectively constructed clinical and cardiovascular magnetic resonance registry; (2) for model validation, a retrospective database comprised of variables extracted from the electronic health record.

Improved Outcomes After Pulmonary Valve Replacement in Repaired Tetralogy of Fallot.

Background: The impact of pulmonary valve replacement (PVR) on major adverse clinical outcomes in patients with repaired tetralogy of Fallot (rTOF) is unknown.

Objectives: The purpose of this study was to determine whether PVR is associated with improved survival and freedom from sustained ventricular tachycardia (VT) in rTOF.

Methods: A PVR propensity score was created to adjust for baseline differences between PVR and non-PVR patients enrolled in INDICATOR (International Multicenter TOF Registry).

A comparison between the apical and subcostal view for three-dimensional echocardiographic assessment of right ventricular volumes in pediatric patients.

Background: Accurate measurement of ventricular volumes is an important clinical imaging goal. Three-dimensional echocardiography (3DEcho) is used increasingly as it is more available and less costly than cardiac magnetic resonance (CMR). For the right ventricle (RV), the current practice is to acquire 3DEcho volumes from the apical view.

Exercise in patients with repaired tetralogy of Fallot: a systematic review and meta-analysis.

Objective: Children and adults with repaired tetralogy of Fallot (rTOF) have an impaired exercise capacity, a less active lifestyle and an increased long-term risk of adverse outcomes compared with healthy peers. This study aimed to summarise the current evidence for the effectiveness and safety of exercise training interventions in patients with rTOF.

Methods: PubMed/MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science, Scopus and reference lists of relevant articles were searched for prospective studies published by November 2021.

Preterm Birth Is Associated With Adverse Cardiac Remodeling and Worse Outcomes in Patients With a Functional Single Right Ventricle.

Objective: To assess the effects of preterm birth on cardiac structure and function and transplant-free survival in patients with hypoplastic left heart syndrome and associated anomalies throughout the staged palliation process.

Study Design: Data from the Single Ventricle Reconstruction trial were used to assess the impact of prematurity on echocardiographic measures at birth, Norwood, Stage II, and 14 months in 549 patients with a single functional right ventricle. Medical history was recorded once a year using medical records or telephone interviews.

Managing uncertainty in decision-making of common congenital cardiac defects.

Decision-making in congenital cardiac care, although sometimes appearing simple, may prove challenging due to lack of data, uncertainty about outcomes, underlying heuristics, and potential biases in how we reach decisions. We report on the decision-making complexities and uncertainty in management of five commonly encountered congenital cardiac problems: indications for and timing of treatment of subaortic stenosis, closure or observation of small ventricular septal defects, management of new-onset aortic regurgitation in ventricular septal defect, management of anomalous aortic origin of a coronary artery in an asymptomatic patient, and indications for operating on a single anomalously draining pulmonary vein. The strategy underpinning each lesion and the indications for and against intervention are outlined.

Ventricular arrhythmias after fibroma resection: Are patients still at risk?

Background: Ventricular fibromas frequently present with life-threatening ventricular tachycardia (VT) or ventricular fibrillation (VF) in children. The long-term risk of sustained ventricular arrhythmias after surgical resection is unknown.

Objectives: The aims of this study were to quantify the VT/VF risk after surgical resection and to examine the results of early (during index surgical hospitalization) and late (≥3 months after surgery) postoperative ventricular stimulation (V-stim) studies.

Outcomes of Repair of Congenital Aortic Valve Lesions Using Autologous Pericardium vs Porcine Intestinal Submucosa.

Background: Outcomes following congenital aortic valve (AoV) repair are plagued by progressive dysfunction of currently available leaflet substitute materials.

Objectives: We compared the long-term outcomes of congenital AoV repair using porcine intestinal submucosa vs autologous pericardium (AP).

Methods: This was a single-center retrospective review of all patients who underwent congenital AoV repair with either porcine intestinal submucosa or AP from October 2009 to March 2013.

SCMR expert consensus statement for cardiovascular magnetic resonance of acquired and non-structural pediatric heart disease.

Cardiovascular magnetic resonance (CMR) is widely used for diagnostic imaging in the pediatric population. In addition to structural congenital heart disease (CHD), for which published guidelines are available, CMR is also performed for non-structural pediatric heart disease, for which guidelines are not available. This article provides guidelines for the performance and reporting of CMR in the pediatric population for non-structural ("non-congenital") heart disease, including cardiomyopathies, myocarditis, Kawasaki disease and systemic vasculitides, cardiac tumors, pericardial disease, pulmonary hypertension, heart transplant, and aortopathies.

Learned iterative segmentation of highly variable anatomy from limited data: Applications to whole heart segmentation for congenital heart disease.

Training deep learning models that segment an image in one step typically requires a large collection of manually annotated images that captures the anatomical variability in a cohort. This poses challenges when anatomical variability is extreme but training data is limited, as when segmenting cardiac structures in patients with congenital heart disease (CHD). In this paper, we propose an iterative segmentation model and show that it can be accurately learned from a small dataset.