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BackgroundOxygen saturation (Spo2) 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.Methods and resultsSix sites prospectively enrolled newborns with and without CCHD and recorded simultaneous pre- and postductal pulse oximetry. We focused on models at 1 versus 2 time points and with/without pulse delay for our ML algorithms. The sensitivity, specificity, and area under the receiver operating characteristic curve were compared between the Spo2-alone and ML algorithms. A total of 523 newborns were enrolled (no CHD, 317; CHD, 74; CCHD, 132, of whom 21 had isolated CoA). When applying the Spo2-alone algorithm to all patients, 26.2% of CCHD would be missed. We narrowed the sample to patients with both 2 time point measurements and pulse-delay data (no CHD, 65; CCHD, 14) to compare ML performance. Among these patients, sensitivity for CCHD detection increased with both the addition of pulse delay and a second time point. All ML models had 100% specificity. With a 2-time-points+pulse-delay model, CCHD sensitivity increased to 92.86% (P=0.25) compared with Spo2 alone (71.43%), and CoA increased to 66.67% (P=0.5) from 0. The area under the receiver operating characteristic curve for CCHD and CoA detection significantly improved (0.96 versus 0.83 for CCHD, 0.83 versus 0.48 for CoA; both P=0.03) using the 2-time-points+pulse-delay model compared with Spo2 alone.ConclusionsML pulse oximetry that combines oxygenation, perfusion data, and pulse delay at 2 time points may improve detection of CCHD and CoA within 48 hours after birth.RegistrationURL: https://www.clinicaltrials.gov/study/NCT04056104?term=NCT04056104&rank=1; Unique identifier: NCT04056104.

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. Larger studies are needed to determine whether an association between Pfizer-BioNTech COVID-19 vaccine and myocarditis exists.

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. We then incorporated the trained ML models into the current SpO2-alone screening algorithm. Our proposed enhanced CCHD screening system, which adds the ML model, improved sensitivity by approximately 10 percentage points compared to the current standard SpO2-alone method with minimal to no impact on specificity.Clinical relevance- This establishes proof of concept for a ML algorithm that combines pulse oximetry features to improve detection of CCHD with little impact on false positive rate.

The autoimmune disease systemic lupus erythematosus has a complex environmental and multifactorial genetic basis. Genome-wide association studies have recently identified numerous disease-associated polymorphisms, but it remains unclear in which cells and during which step of pathogenesis specific polymorphisms interact to cause disease. Using a mouse model in which the same activating mutation (CD45E613R) causes distinct genetic background-dependent disease phenotypes, we performed a screen for genetic modifiers of autoreactivity between anti-nuclear Ab (ANA)-resistant CD45E613R.B6 and ANA-permissive CD45E613R.BALB/c mice. Within a novel autoreactivity-associated locus on chromosome 9, we identify a putative modifier, TLR9. Validating a role for TLR9 in modifying autoreactivity in the context of the CD45E613R mutation, manipulation of TLR9 gene dosage eliminates ANA in CD45E613R.BALB/c mice, but confoundingly permits ANA in CD45E613R.B6 mice. We demonstrate that sensitivity to ANA is modulated by strength of TLR9 signal, because stronger TLR9(B6) signals, but not weaker TLR9(BALB/c) signals, negatively regulate CD45E613R B cell development during competitive reconstitution at the central tolerance checkpoint. Our results identify a novel autoreactivity-associated locus and validate Tlr9 as a candidate gene within the locus. We further demonstrate a novel role for TLR9 signal strength in central tolerance, providing insight into the interplay of disease-associated polymorphisms at a discrete step of systemic lupus erythematosus pathogenesis.

Problem: 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. In addition, from an organizational perspective oversight of education and patient care is often done by separate entities, and not infrequently is there a sense of competing interests. Intervention: We aimed to design and pilot a holistic approach to assessment and review of CLEs and establish whether such a formative assessment process could be used to engage stakeholders from education, departmental, and health systems leadership in improvement of CLEs. Utilizing concepts of implementation science, we planned and executed a holistic assessment process for CLEs, monitored the impact of the assessment, and reflected on the process. We focused the assessment on four pillars characterizing exemplary learning environments: 1) Environment is inclusive, promotes diversity and collaboration; 2) Focus on continuous quality improvement; 3) Alignment between work and learning; and 4) Integration of education and healthcare mission. Context: At our institution, medical trainees rotate through several different health systems, but clinical and educational leadership converge at the departmental level. We therefore focused this proof-of-concept project on two large clinical departments at our institution, centering on medical learners from undergraduate and graduate medical education. For each department, a small team of champions helped create an assessment grid based on the four pillars and identified existing quantitative evaluation data sources. Champions subsequently collected qualitative data through observations, focus groups, and interviews to fill any gaps in available quantitative data. Impact: The project teams shared reports summarizing findings and recommendations with departmental, clinical, and educational leadership. Subsequent meetings with these stakeholders led to actionable plans for improvement as well as sustained structures for collaborative work between the different stakeholder groups. Lessons Learned: This project demonstrated the feasibility and effectiveness of collating, analyzing, and sharing data from various sources in engaging different stakeholder groups to initiate actionable improvement plans. Collating quantitative data from existing resources was a powerful way to demonstrate common issues in CLEs, and qualitative data provided further detail to inform improvement initiatives. Other institutions can adapt this approach to guide assessment and quality improvement of CLEs. As a next step, we are creating a comprehensive learning environment scorecard to allow for comparison of clinical learning environment quality across institutions and over time. [ABSTRACT FROM AUTHOR]

The cooperative activity of protein tyrosine kinases and phosphatases plays a central role in regulation of I cell receptor (TCR) signal strength. Perturbing this balance, and thus the threshold for TCR signals, has profound impacts on T cell development and function. We previously generated mice containing a point mutation in the juxtamembrane wedge of the receptor-like protein tyrosine phosphatase CD45. Demonstrating the critical negative regulatory function of the wedge, the CD45 E613R (WEDGE) mutation led to a lymphoproliferative disorder (LPD) and a lupus-like autoimmune syndrome. Using genetic, cellular, and biochemical approaches, we now demonstrate that the CD45 wedge influences T cell development and function. Consistent with increased TCR signal strength, WEDGE mice have augmented positive selection and enhanced sensitivity to the CD4-mediated disease experimental autoimmune encephalitis (EAE). These correspond with hyperresponsive calcium and pERK responses to TCR stimulation in thymocytes, but surprisingly, not in peripheral T cells, where these responsesare actually depressed. Together, the data support a role for the CD45 wedge in regulation of T cell responses in vivo and suggest that its effects depend on cellular context. [ABSTRACT FROM AUTHOR]

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. We then incorporated the trained ML models into the current SpO2-alone screening algorithm. Our proposed enhanced CCHD screening system, which adds the ML model, improved sensitivity by approximately 10 percentage points compared to the current standard SpO2-alone method with minimal to no impact on specificity.Clinical relevance- This establishes proof of concept for a ML algorithm that combines pulse oximetry features to improve detection of CCHD with little impact on false positive rate.