Details

Autor(en) / Beteiligte

• Michelson, Kenneth A
• Bachur, Richard G
• Dart, Arianna H
• Chaudhari, Pradip P
• Cruz, Andrea T
• Grubenhoff, Joseph A
• Reeves, Scott D
• Monuteaux, Michael C
• Finkelstein, Jonathan A

Titel

Identification of delayed diagnosis of paediatric appendicitis in administrative data: a multicentre retrospective validation study

Ist Teil von

• BMJ open, 2023-02, Vol.13 (2), p.e064852-e064852

Ort / Verlag

England: British Medical Journal Publishing Group

Erscheinungsjahr

2023

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• 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.