ArticleBMJ open2023
Identification of delayed diagnosis of paediatric appendicitis in administrative data: a multicentre retrospective validation study.
Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Feasibility of Pediatric Diagnostic Quality Measurement in All United States Hospitals.Annals of emergency medicine · 2026Article
- Characterizing Inequities in Pediatric Appendicitis Delayed Diagnosis and Perforation.Journal of pediatrics. Clinical practice · 2024Article
- Disparities in Diagnostic Timeliness and Outcomes of Pediatric Appendicitis.JAMA network open · 2024Article
- Multicenter evaluation of a method to identify delayed diagnosis of diabetic ketoacidosis and sepsis in administrative data.Diagnosis (Berlin, Germany) · 2023Article
- Community Validation of an Approach to Detect Delayed Diagnosis of Appendicitis in Big Databases.Hospital pediatrics · 2023Article
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Authors and funding
9 authors.
Funding
Abstract
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. OUTCOME: Delayed 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.
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