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ObjectiveAppendiceal diameter is a primary sonographic determinant of paediatric appendicitis. We sought to determine if the diagnostic performance of outer appendiceal diameter differs based on age or with the addition of secondary sonographic findings.MethodsWe retrospectively reviewed patients aged less than 19 years who presented to the Boston Children’s Hospital ED and had an ultrasound (US) for the evaluation of appendicitis between November 2015 and October 2018. Our primary outcome was the presence of appendicitis. We analysed the cases to evaluate the optimal outer appendiceal diameter as a predictor for appendicitis stratified by age (ResultsOverall, 945 patients met criteria for inclusion, of which 43.9% had appendicitis. Overall, appendiceal diameter as a continuous measure demonstrated excellent test performance across all age groups (area under the curve (AUC) >0.95) but was most predictive of appendicitis in the youngest age group (AUC=0.99 (0.98–1.00)). Although there was no significant difference in optimal diameter threshold between age groups, both 7- and 8-mm thresholds were more predictive than 6 mm across all groups (pConclusionsAppendiceal diameter as a continuous measure was more predictive of appendicitis in the youngest group. Across all age groups, the optimal diameter threshold was 7 mm for the diagnosis of paediatric appendicitis. The addition of individual or combination secondary sonographic findings increases diagnostic performance.

ObjectiveTo derive and validate a tool that retrospectively identifies delayed diagnosis of appendicitis in administrative data with high accuracy.DesignCross-sectional study.SettingFive paediatric emergency departments (EDs).Participants669 patients under 21 years old with possible delayed diagnosis of appendicitis, defined as two ED encounters within 7 days, the second with appendicitis.OutcomeDelayed diagnosis was defined as appendicitis being present but not diagnosed at the first ED encounter based on standardised record review. The cohort was split into derivation (2/3) and validation (1/3) groups. We derived a prediction rule using logistic regression, with covariates including variables obtainable only from administrative data. The resulting trigger tool was applied to the validation group to determine area under the curve (AUC). Test characteristics were determined at two predicted probability thresholds.ResultsDelayed diagnosis occurred in 471 (70.4%) patients. The tool had an AUC of 0.892 (95% CI 0.858 to 0.925) in the derivation group and 0.859 (95% CI 0.806 to 0.912) in the validation group. The positive predictive value (PPV) for delay at a maximal accuracy threshold was 84.7% (95% CI 78.2% to 89.8%) and identified 87.3% of delayed cases. The PPV at a stricter threshold was 94.9% (95% CI 87.4% to 98.6%) and identified 46.8% of delayed cases.ConclusionsThis tool accurately identified delayed diagnosis of appendicitis. It may be used to screen for potential missed diagnoses or to specifically identify a cohort of children with delayed diagnosis.

Objectives The presence of critically ill patients may impact care for other ED patients. We sought to evaluate whether the presence of a critically ill child was associated with the time to (1) receipt of the first medication among other patients, and (2) administration of diagnosis-specific medications. Methods We performed a retrospective cohort study of all paediatric ED visits over 3 years. Patients were exposed if they arrived during the first hour of a critically ill patient's care. The primary outcome was the time from arrival to first medication administration. Secondary outcomes were time to corticosteroids in asthma and time to antibiotics for fever/neutropenia. We modelled times to medication using median regression, adjusting for demographics, arrival time and weekday, and census (number of patients in the ED). Results We analysed 170 112 visits. Median times to first medication for those exposed to 0, 1 and >1 simultaneous critically ill patients were 90 min (IQR 54–146), 96 min (IQR 58–157) and 113 min (IQR 72–166), respectively (p

BackgroundDelayed diagnoses of serious emergency conditions can lead to morbidity in children, but are challenging to identify and measure. We developed and piloted an automated tool for identifying delayed diagnosis of two serious conditions commonly seen in the ED using administrative data.MethodsWe identified cases with a final diagnosis of appendicitis or sepsis in a freestanding children’s hospital from 2008 to 2018, with any hospital ED encounter within the preceding 7 days. Two investigators reviewed a subset of these cases using the electronic health records (EHR) to determine if there was a delayed diagnosis and interrater reliability was assessed using the intraclass correlation coefficient (ICC). An automated tool was applied to the same cases to assess its positive predictive value (PPV) to identify those with a delayed diagnosis, using the manual chart review as the gold standard. The tool used number of days since visit, presence of a related diagnosis on the initial visit, and whether or not the patient was discharged.ResultsPrevious ED encounters preceded 91/3703 (2.5%) appendicitis cases and 159/1754 (9.1%) sepsis cases; 78 cases of each were sampled for review. In manual review, 73.4% and 22.8% were thought to have delayed diagnoses; reviewer agreement was excellent (appendicitis ICC 0.77, 95% CI 0.62 to 0.86 and sepsis ICC 0.77, 95% CI 0.43 to 0.89). The PPVs of the automated tool for determination of delayed diagnosis for appendicitis within 1, 3 or 7 days were 96.2%, 95.1% and 93.6%, respectively. For sepsis, the PPVs were 71.4%, 63.6% and 41.2% within 1, 3 or 7 days, respectively.ConclusionsThis automated tool performed well compared with expert EHR review. Performance was stronger for appendicitis. Further tool refinement could improve performance.