Phenotypic clustering of repaired Tetralogy of Fallot using unsupervised machine learning.

Objective: Repaired Tetralogy of Fallot (rTOF), a complex congenital heart disease, exhibits substantial clinical heterogeneity. Accurate prediction of disease progression and tailored patient management remain elusive. We aimed to categorize rTOF patients into distinct phenotypes based on clinical variables and variables obtained from cardiac magnetic resonance (CMR) imaging.

Seasons of Kawasaki Disease during the COVID-19 pandemic.

The incidence of Kawasaki Disease has a peak in the winter months with a trough in late summer/early fall. Environmental/exposure factors have been associated with a time-varying incidence. These factors were altered during the COVID-19 pandemic.

Factors Associated With Shock at Presentation in Kawasaki Disease Versus Multisystem Inflammatory Syndrome in Children Associated With Covid-19.

Background: Although clinical overlap between Kawasaki disease (KD) and multisystem inflammatory syndrome in children (MIS-C) has been evident, information regarding those presenting with shock has been limited. We sought to determine associations with shock within and between diagnosis groups.

Methods: The International KD Registry enrolled contemporaneous patients with either KD or MIS-C from 39 sites in 7 countries from January 1, 2020, to January 1, 2023.

Unsupervised machine learning identifies distinct phenotypes in cardiac complications of pediatric patients treated with anthracyclines.

Background: Anthracyclines are essential in pediatric cancer treatment, but patients are at risk cancer therapy-related cardiac dysfunction (CTRCD). Standardized definitions by the International Cardio-Oncology Society (IC-OS) aim to enhance precision in risk assessment.

Objectives: Categorize distinct phenotypes among pediatric patients undergoing anthracycline chemotherapy using unsupervised machine learning.

Optimal Shunt Type for Norwood Procedure: Predicted Adverse Impact of Discordant Surgical Approach.

Background: The Single Ventricle Reconstruction (SVR) trial compared survival after Norwood procedure with either modified Blalock Taussig shunt (MBTS) or right ventricle pulmonary artery shunt (RVPAS).

Methods: Data from all 549 participants in the SVR trial were used to develop the MBTS TFSA algorithms, which predict the transplantation-free survival advantage (TFSA) after MBTS vs RVPAS at 1 and 6 years after Norwood procedure. Linear regression analysis of the MBTS TFSA values was performed to identify factors related to more optimal outcomes with MBTS at each timepoint.

Equivalency of Multiple Biomarkers to Clinical Pulmonary Arterial Hypertension Survival Risk Models.

Background: Risk assessment in pulmonary arterial hypertension (PAH) is fundamental to guiding treatment and improved outcomes. Clinical models are excellent at identifying high-risk patients, but leave uncertainty amongst moderate-risk patients.

Research Question: Can a multiple blood biomarker model of PAH, using previously described biomarkers, improve risk discrimination over current models?

Study Design And Methods: Using a multiplex enzyme-linked immunosorbent assay, we measured N-terminal pro-B-type natriuretic peptide (NT-proBNP), soluble suppressor of tumorigenicity, IL-6, endostatin, galectin 3, hepatoma derived growth factor, and insulin-like growth factor binding proteins (IGFBP1-7) in training (n = 1,623), test (n = 696), and validation (n = 237) cohorts.

Emerging Analytical Approaches for Personalized Medicine Using Machine Learning In Pediatric and Congenital Heart Disease.

Precision and personalized medicine, the process by which patient management is tailored to individual circumstances, are now terms that are familiar to cardiologists, despite it still being an emerging field. Although precision medicine relies most often on the underlying biology and pathophysiology of a patient's condition, personalized medicine relies on digital biomarkers generated through algorithms. Given the complexity of the underlying data, these digital biomarkers are most often generated through machine-learning algorithms.

Computational simulation of the potential improvement in clinical outcomes of cardiovascular diseases with the use of a personalized predictive medicine approach.

Importance And Objectives: The current medical paradigm of evidence-based medicine relies on clinical guidelines derived from randomized clinical trials (RCTs), but these guidelines often overlook individual variations in treatment effects. Approaches have been proposed to develop models predicting the effects of individualized management, such as predictive allocation, individualizing treatment allocation. It is currently unknown whether widespread implementation of predictive allocation could result in better population-level outcomes over guideline-based therapy.

The Lifelong Impact of Artificial Intelligence and Clinical Prediction Models on Patients With Tetralogy of Fallot.

Medical advancements in the diagnosis, surgical techniques, perioperative care, and continued care throughout childhood have transformed the outlook for individuals with tetralogy of Fallot (TOF), improving survival and shifting the perspective towards lifelong care. However, with a growing population of survivors, longstanding challenges have been accentuated, and new challenges have surfaced, necessitating a re-evaluation of TOF care. Availability of prenatal diagnostics, insufficient information from traditional imaging techniques, previously unforeseen medical complications, and debates surrounding optimal timing and indications for reintervention are among the emerging issues.

Cardiac Biomarkers Aid in Differentiation of Kawasaki Disease from Multisystem Inflammatory Syndrome in Children Associated with COVID-19.

Kawasaki disease (KD) and Multisystem Inflammatory Syndrome in Children (MIS-C) associated with COVID-19 show clinical overlap and both lack definitive diagnostic testing, making differentiation challenging. We sought to determine how cardiac biomarkers might differentiate KD from MIS-C. The International Kawasaki Disease Registry enrolled contemporaneous KD and MIS-C pediatric patients from 42 sites from January 2020 through June 2022.

Obesity and Outcomes of Kawasaki Disease and COVID-19-Related Multisystem Inflammatory Syndrome in Children.

Importance: Obesity may affect the clinical course of Kawasaki disease (KD) in children and multisystem inflammatory syndrome in children (MIS-C) associated with COVID-19.

Objective: To compare the prevalence of obesity and associations with clinical outcomes in patients with KD or MIS-C.

Design, Setting, And Participants: In this cohort study, analysis of International Kawasaki Disease Registry (IKDR) data on contemporaneous patients was conducted between January 1, 2020, and July 31, 2022 (42 sites, 8 countries).

Factors Associated With Acute Kidney Injury After Cardiopulmonary Bypass in Children.

Background: Acute kidney injury (AKI) is a common complication after cardiovascular surgery in children, noted in approximately 40% of children undergoing cardiopulmonary bypass (CPB). We sought to determine the risk factors including inflammatory and vascular endothelial markers associated with AKI in children undergoing cardiac surgery.

Methods: A secondary analysis of a prospective observational cohort study of paediatric patients with a cardiac defect requiring CPB and a weight of >2.

Socioeconomic Status and Kawasaki Disease Outcomes in a Single-Payer Health Care System.

Background: For patients with Kawasaki disease (KD), lower socioeconomic status (SES) may adversely affect the timeliness of presentation and initiation of intravenous immune globulin, and coronary artery outcomes. Multipayer systems have been shown to affect health care equity and access to health care negatively. We sought to determine the association of SES with KD outcomes in a single-payer health care system.

The relationships between acetylcholine-induced chest pain, objective measures of coronary vascular function and symptom status.

Background: Acetylcholine-induced chest pain is routinely measured during the assessment of microvascular function.

Aims: The aim was to determine the relationships between acetylcholine-induced chest pain and both symptom burden and objective measures of vascular function.

Methods: In patients with angina but no obstructive coronary artery disease, invasive studies determined the presence or absence of chest pain during both acetylcholine and adenosine infusion.

FGF21 and GDF15 are elevated in Barth Syndrome and are correlated to important clinical measures.

Barth Syndrome (BTHS) is a rare X-linked disorder that is caused by defects TAFAZZIN, which leads to an abnormal cardiolipin (CL) profile of the inner mitochondrial membrane and clinical features including cardiomyopathy, neutropenia and skeletal myopathy. The ratio of monolysocardiolipin (MLCL, the remodeling intermediate of cardiolipin) to remodeled CL is always abnormal in Barth Syndrome and 3-methylglutaconic acid is often elevated affected patients, however neither of these biomarkers has been shown to temporally correlate to clinical status. In this study, we measured plasma FGF21 and GDF15 levels in 16 individuals with Barth Syndrome and evaluated whether these biomarkers were correlated to the MLCL/CL ratio in patient bloodspots and clinical laboratory parameters indicative of organ involvement in Barth Syndrome including: neutrophil and monocyte counts, liver function, and cardiac function (NT-proBNP).

Kawasaki Disease in the Time of COVID-19 and MIS-C: The International Kawasaki Disease Registry.

Background: Patients with multisystem inflammatory syndrome in children (MIS-C) and Kawasaki disease (KD) have overlapping clinical features. We compared demographics, clinical presentation, management, and outcomes of patients according to evidence of previous SARS-CoV-2 infection.

Methods: The International Kawasaki Disease Registry (IKDR) enrolled KD and MIS-C patients from sites in North, Central, and South America, Europe, Asia, and the Middle East.

Data Quality Degradation on Prediction Models Generated From Continuous Activity and Heart Rate Monitoring: Exploratory Analysis Using Simulation.

Background: Limited data accuracy is often cited as a reason for caution in the integration of physiological data obtained from consumer-oriented wearable devices in care management pathways. The effect of decreasing accuracy on predictive models generated from these data has not been previously investigated.

Objective: The aim of this study is to simulate the effect of data degradation on the reliability of prediction models generated from those data and thus determine the extent to which lower device accuracy might or might not limit their use in clinical settings.

Identifying responders to elamipretide in Barth syndrome: Hierarchical clustering for time series data.

Background: Barth syndrome (BTHS) is a rare genetic disease that is characterized by cardiomyopathy, skeletal myopathy, neutropenia, and growth abnormalities and often leads to death in childhood. Recently, elamipretide has been tested as a potential first disease-modifying drug. This study aimed to identify patients with BTHS who may respond to elamipretide, based on continuous physiological measurements acquired through wearable devices.

COVID-19 Positive Versus Negative Complete Kawasaki Disease: A Study from the International Kawasaki Disease Registry.

To determine clinical differences for children with complete Kawasaki disease (KD) with and without evidence of preceding SARS-CoV-2 infection. From January 2020, contemporaneous patients with complete KD criteria were classified as either SARS-CoV-2 positive (KDCOVID+; confirmed household exposure, positive PCR and/or serology) or SARS-CoV-2 negative (KDCOVID-; negative testing and no exposure) and compared. Of 744 patients in the International Kawasaki Disease Registry, 52 were KDCOVID- and 61 were KDCOVID+.