Metabolomic and Immunologic Discriminators of MIS-C at Emergency Room Presentation.

Multisystem Inflammatory Syndrome in Childhood (MIS-C) follows SARS-CoV-2 infection and frequently leads to intensive care unit admission. The inability to rapidly discriminate MIS-C from similar febrile illnesses delays treatment and leads to misdiagnosis. To identify diagnostic discriminators at the time of emergency department presentation, we enrolled 104 children who met MIS-C screening criteria, 14 of whom were eventually diagnosed with MIS-C. Before treatment, we collected breath samples for volatiles and peripheral blood for measurement of plasma proteins and immune cell features. Clinical and laboratory features were used as inputs for a machine learning model to determine diagnostic importance. MIS-C was associated with significant changes in breath volatile organic compound (VOC) composition as well as increased plasma levels of secretory phospholipase A2 (PLA2G2A) and lipopolysaccharide binding protein (LBP). In an integrated model of all analytes, the proportion of TCRVß21.3+ non-naive CD4 T cells expressing Ki-67 had a high sensitivity and specificity for MIS-C, with diagnostic accuracy further enhanced by low sodium and high PLA2G2A. We anticipate that accurate diagnosis will become increasingly difficult as MIS-C becomes less common. Clinical validation and application of this diagnostic model may improve outcomes in children presenting with multisystem febrile illnesses.

Developmental progression of the nasopharyngeal microbiome during childhood and association with the lower airway microbiome.

Background: The upper (URT) and lower (LRT) respiratory tract feature distinct environments and responses affecting microbial colonization but investigating the relationship between them is technically challenging. We aimed to identify relationships between taxa colonizing the URT and LRT and explore their relationship with development during childhood. Methods: We employed V4 16S rDNA sequencing to profile nasopharyngeal swabs and tracheal aspirates collected from 183 subjects between 20 weeks and 18 years of age. These samples were collected prior to elective procedures at the Children's Hospital of Philadelphia over the course of 20 weeks in 2020, from otherwise healthy subjects enrolled in a study investigating potential reservoirs of SARS-CoV-2. Findings: After extraction, sequencing, and quality control, we studied the remaining 124 nasopharyngeal swabs and 98 tracheal aspirates, including 85 subject-matched pairs of samples. V4 16S rDNA sequencing revealed that the nasopharynx is colonized by few, highly-abundant taxa, while the tracheal aspirates feature a diverse assembly of microbes. While no taxa co-occur in the URT and LRT of the same subject, clusters of microbiomes in the URT correlate with clusters of microbiomes in the LRT. The clusters identified in the URT correlate with subject age across childhood development. Interpretations: The correlation between clusters of taxa across sites may suggest a mutual influence from either a third site, such as the oropharynx, or host-extrinsic, environmental features. The identification of a pattern of upper respiratory microbiota development across the first 18 years of life suggests that the patterns observed in early childhood may extend beyond the early life window.

Deep learning imaging features derived from kidney ultrasounds predict chronic kidney disease progression in children with posterior urethral valves.

BACKGROUND: We sought to use deep learning to extract anatomic features from postnatal kidney ultrasounds and evaluate their performance in predicting the risk and timing of chronic kidney disease (CKD) progression for boys with posterior urethral valves (PUV). We hypothesized that these features would predict CKD progression better than clinical characteristics such as nadir creatinine alone. METHODS: We performed a retrospective cohort study of boys with PUV treated at two pediatric health systems from 1990 to 2021. Features of kidneys were extracted from initial postnatal kidney ultrasound images using a deep learning model. Three time-to-event prediction models were built using random survival forests. The Imaging Model included deep learning imaging features, the Clinical Model included clinical data, and the Ensemble Model combined imaging features and clinical data. Separate models were built to include time-dependent clinical data that were available at 6 months, 1 year, 3 years, and 5 years. RESULTS: Two-hundred and twenty-five patients were included in the analysis. All models performed well with C-indices of 0.7 or greater. The Clinical Model outperformed the Imaging Model at all time points with nadir creatinine driving the performance of the Clinical Model. Combining the 6-month Imaging Model (C-index 0.7; 95% confidence interval [CI] 0.6, 0.79) with the 6-month Clinical Model (C-index 0.79; 95% CI 0.71, 0.86) resulted in a 6-month Ensemble Model that performed better (C-index 0.82; 95% CI 0.77, 0.88) than either model alone. CONCLUSIONS: Deep learning imaging features extracted from initial postnatal kidney ultrasounds may improve early prediction of CKD progression among children with PUV. A higher resolution version of the Graphical abstract is available as Supplementary information.