Objective To identify publishing trends within the field of Pediatric Emergency Medicine between 2004 and 2013. Methods We conducted a MEDLINE search of Pediatric Emergency Medicine articles, filtered by clinical trial, published between 2004 and 2013 in ten journals from the fields of Pediatrics, Emergency Medicine, General Medicine, and Pediatric Emergency Medicine. Each article was classified by journal type, study design, results (positive or negative/equivocal), age/type of subjects, and major topic (based on the objective of the study). Articles were stratified by publication time period (2004–2008 or 2009–2013) to analyze trends. Results A total of 464 articles were analyzed. The majority of articles were described as randomized controlled trials (47%) with negative/equivocal findings (70%). The most common major topics were pain management, asthma, sedation, bronchiolitis, resuscitation, simulation, and ultrasound. Over time, the percentage of articles published in Pediatrics and Pediatric Emergency Medicine journals increased (p=0.0499) and the percentage for all study designs increased except for randomized controlled trials (p=0.0089). There were no differences between the two publication time periods when stratified by results, age/type of subjects, and major topic. Conclusions By identifying these trends, we hope to encourage researchers to perform studies in the field of Pediatric Emergency Medicine where deficiencies lie and to guide pediatric health care professionals to where published, evidence-based studies can be found in the medical literature.

Background Electronic health record‐based clinical decision support (CDS) is a promising antibiotic stewardship strategy. Few studies have evaluated the effectiveness of antibiotic CDS in the pediatric emergency department (ED). Objective To compare the effectiveness of antibiotic CDS vs. usual care for promoting guideline‐concordant antibiotic prescribing for pneumonia in the pediatric ED. Design Pragmatic randomized clinical trial. Setting and Participants Encounters for children (6 months‐18 years) with pneumonia presenting to two tertiary care children s hospital EDs in the United States. Intervention CDS or usual care was randomly assigned during 4‐week periods within each site. The CDS intervention provided antibiotic recommendations tailored to each encounter and in accordance with national guidelines. Main Outcome and Measures The primary outcome was exclusive guideline‐concordant antibiotic prescribing within the first 24 h of care. Safety outcomes included time to first antibiotic order, encounter length of stay, delayed intensive care, and 3‐ and 7‐day revisits. Results 1027 encounters were included, encompassing 478 randomized to usual care and 549 to CDS. Exclusive guideline‐concordant prescribing did not differ at 24 h (CDS, 51.7% vs. usual care, 53.3%; odds ratio [OR] 0.94 [95% confidence interval [CI]: 0.73, 1.20]). In pre‐specified stratified analyses, CDS was associated with guideline‐concordant prescribing among encounters discharged from the ED (74.9% vs. 66.0%; OR 1.53 [95% CI: 1.01, 2.33]), but not among hospitalized encounters. Mean time to first antibiotic was shorter in the CDS group (3.0 vs 3.4 h; p = .024). There were no differences in safety outcomes. Conclusions Effectiveness of ED‐based antibiotic CDS was greatest among those discharged from the ED. Longitudinal interventions designed to target both ED and inpatient clinicians and to address common implementation challenges may enhance the effectiveness of CDS as a stewardship tool.

ObjectiveChest radiographs are frequently used to diagnose community-acquired pneumonia (CAP) for children in the acute care setting. Natural language processing (NLP)-based tools may be incorporated into the electronic health record and combined with other clinical data to develop meaningful clinical decision support tools for this common pediatric infection. We sought to develop and internally validate NLP algorithms to identify pediatric chest radiograph (CXR) reports with pneumonia.Materials and methodsWe performed a retrospective study of encounters for patients from six pediatric hospitals over a 3-year period. We utilized six NLP techniques: word embedding, support vector machines, extreme gradient boosting (XGBoost), light gradient boosting machines Naïve Bayes and logistic regression. We evaluated their performance of each model from a validation sample of 1,350 chest radiographs developed as a stratified random sample of 35% admitted and 65% discharged patients when both using expert consensus and diagnosis codes.ResultsOf 172,662 encounters in the derivation sample, 15.6% had a discharge diagnosis of pneumonia in a primary or secondary position. The median patient age in the derivation sample was 3.7 years (interquartile range, 1.4–9.5 years). In the validation sample, 185/1350 (13.8%) and 205/1350 (15.3%) were classified as pneumonia by content experts and by diagnosis codes, respectively. Compared to content experts, Naïve Bayes had the highest sensitivity (93.5%) and XGBoost had the highest F1 score (72.4). Compared to a diagnosis code of pneumonia, the highest sensitivity was again with the Naïve Bayes (80.1%), and the highest F1 score was with the support vector machine (53.0%).ConclusionNLP algorithms can accurately identify pediatric pneumonia from radiography reports. Following external validation and implementation into the electronic health record, these algorithms can facilitate clinical decision support and inform large database research.

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