Machine Learning-Based Critical Congenital Heart Disease Screening Using Dual-Site Pulse Oximetry Measurements.

Background: Oxygen saturation (Spo) screening has not led to earlier detection of critical congenital heart disease (CCHD). Adding pulse oximetry features (ie, perfusion data and radiofemoral pulse delay) may improve CCHD detection, especially coarctation of the aorta (CoA). We developed and tested a machine learning (ML) pulse oximetry algorithm to enhance CCHD detection.

Myocarditis in children after COVID-19 vaccine.

Three healthy adolescents presented with myocarditis confirmed on cardiac magnetic resonance imaging after receiving Pfizer-BioNTech COVID-19 vaccine. All patients were hemodynamically stable and had good short-term outcomes. Long-term outcomes are yet to be determined.

Comprehensive Assessment of Clinical Learning Environments to Drive Improvement: Lessons Learned from a Pilot Program.

: Recognition of the importance of clinical learning environments (CLEs) in health professions education has led to calls to evaluate and improve the quality of such learning environments. As CLEs sit at the crossroads of education and healthcare delivery, leadership from both entities should share the responsibility and accountability for this work. Current data collection about the experience and outcomes for learners, faculty, staff, and patients tends to occur in fragmented and siloed ways, and available tools to assess clinical learning environments are limited in scope.

A Measurement-Based Protocol Improves Interrater Agreement and Accuracy of Right Ventricular Systolic Pressure Assessment by Echocardiography in Children: A Call for Quality Improvement.

Background: Echocardiographic measurements carry the promise of improving interrater (IR) agreement over subjective assessment. The aim of this study was to assess the effect of implementing a measurement-based protocol on IR agreement and accuracy in reporting of right ventricular (RV) systolic pressure in children. The effect of this reporting protocol on IR agreement in reporting RV dilation, hypertrophy, and systolic function was also evaluated.

Enhanced Critical Congenital Cardiac Disease Screening by Combining Interpretable Machine Learning Algorithms.

Critical Congenital Heart Disease (CCHD) screening that only uses oxygen saturation (SpO2), measured by pulse oximetry, fails to detect an estimated 900 US newborns annually. The addition of other pulse oximetry features such as perfusion index (PIx), heart rate, pulse delay and photoplethysmography characteristics may improve detection of CCHD, especially those with systemic blood flow obstruction such as Coarctation of the Aorta (CoA). To comprehensively study the most relevant features associated with CCHD, we investigated interpretable machine learning (ML) algorithms by using Recursive Feature Elimination (RFE) to identify an optimal subset of features.