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BACKGROUND. The molecular signature of pediatric acute respiratory distress syndrome (ARDS) is poorly described, and the degree to which hyperinflammation or specific tissue injury contributes to outcomes is unknown. Therefore, we profiled inflammation and tissue injury dynamics over the first 7 days of ARDS, and associated specific biomarkers with mortality, persistent ARDS, and persistent multiple organ dysfunction syndrome (MODS). METHODS. In a single-center prospective cohort of intubated pediatric patients with ARDS, we collected plasma on days 0, 3, and 7. Nineteen biomarkers reflecting inflammation, tissue injury, and damage- associated molecular patterns (DAMPs) were measured. We assessed the relationship between biomarkers and trajectories with mortality, persistent ARDS, or persistent MODS using multivariable mixed effect models. RESULTS. In 279 patients (64 [23%] nonsurvivors), hyperinflammatory cytokines, tissue injury markers, and DAMPs were higher in nonsurvivors. Survivors and nonsurvivors showed different biomarker trajectories. IL-1[alpha], soluble tumor necrosis factor receptor 1, angiopoietin 2 (ANG2), and surfactant protein D increased in nonsurvivors, while DAMPs remained persistently elevated. ANG2 and procollagen type III N-terminal peptide were associated with persistent ARDS, whereas multiple cytokines, tissue injury markers, and DAMPs were associated with persistent MODS. Corticosteroid use did not impact the association of biomarker levels or trajectory with mortality. CONCLUSIONS. Pediatric ARDS survivors and nonsurvivors had distinct biomarker trajectories, with cytokines, endothelial and alveolar epithelial injury, and DAMPs elevated in nonsurvivors. Mortality markers overlapped with markers associated with persistent MODS, rather than persistent ARDS. FUNDING. NIH (K23HL-136688, R01-HL148054)., Introduction Acute respiratory distress syndrome (ARDS) is a heterogeneous condition of proteinaceous pulmonary edema causing acute life-threatening hypoxemia. Primarily described for adults (1, 2), pediatric ARDS has a distinct epidemiology [...]

Opportunities for education in clinical informatics exist throughout the spectrum of formal education extending from high school to postgraduate training. However, physicians in residency represent an underdeveloped source of potential informaticians. Despite the rapid growth of accredited fellowship programs since clinical informatics became a board-eligible subspecialty in 2011, few resident physicians are aware of their role at the intersection of clinical medicine and health information technology or associated opportunities. In an effort to educate and engage residents in clinical informatics, Children's Hospital of Philadelphia has developed a three-pronged model: (1) an elective rotation with hands-on project experience; (2) a longitudinal experience that offers increased exposure and mentorship; and (3) a resident founded and led working group in clinical informatics. We describe resident participation in these initiatives and lessons learned, as well as resident perceptions of how these components have positively influenced informatics knowledge and career choices. Since inception of this model, five residents have pursued the clinical informatics fellowship. This educational model supports resident involvement in hospital-wide informatics efforts with tangible projects and promotes wider engagement through educational opportunities commensurate with the resident's level of interest., Competing Interests: None., (Schattauer GmbH Stuttgart.)

Viral testing for pediatric inpatients with respiratory symptoms is common, with considerable associated charges. In an attempt to reduce testing volumes, we studied whether data available at the time of admission could aid in identifying children with low likelihood of having a particular viral origin of their symptoms, and thus safely forgo broad viral testing. We collected clinical data for 1,685 pediatric inpatients receiving respiratory virus testing from 2010-2012. Machine-learning on the data allowed us to construct pre-test models predicting whether a patient would test positive for a particular virus. Text mining improved the predictions for one viral test. Cost-sensitive models optimized for test sensitivity showed reasonable test specificities and an ability to reduce test volume by up to 46% for single viral tests. We conclude that diverse forms of data in the electronic medical record can be used productively to build models that help physicians reduce testing volumes.