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

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.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.

Emergency Department Volume and Delayed Diagnosis of Pediatric Appendicitis

“…The outcome was a probable delayed diagnosis of appendicitis using a previously validated algorithm that accurately identifies delays using administrative data and was separately validated in both pediatric and community EDs. 16,17 All potentially delayed diagnoses had 2 ED encounters within 7 days, with the second encounter resulting in a diagnosis of appendicitis. The algorithm assigns a probability of delay for each patient based on the interval between encounters, initial encounter diagnoses, presence of perforation at diagnosis, and other factors.…”

“…However, the approach used to identify patients with delay was previously shown to have high accuracy. 16 Additionally, the interval between ED encounters among delayed diagnosis patients, as well as their firstvisit diagnoses have face validity for patients likely to have experienced delay. Second, there may be residual confounding in the diagnosability of patients between EDs; patients visiting highervolume EDs may have had more clinically apparent appendicitis.…”

“…First, we did not evaluate clinical records of patients assigned as having delayed diagnosis, limiting our certainty about the presence of delay. However, the approach used to identify patients with delay was previously shown to have high accuracy 16 . Additionally, the interval between ED encounters among delayed diagnosis patients, as well as their first-visit diagnoses have face validity for patients likely to have experienced delay.…”

“…The algorithm was derived and validated in distinct cohorts of children, and the predicted probabilities of delay were shown to have acceptable accuracy. 16 We defined a probable delay as a likelihood of delay ≥ 75%. Children without a previous ED encounter within 7 days of the appendicitis diagnosis or those with a delay likelihood < 75% were considered not to have a delay.…”

“…The algorithm assigns a probability of delay for each patient based on the interval between encounters, initial encounter diagnoses, presence of perforation at diagnosis, and other factors. The algorithm was derived and validated in distinct cohorts of children, and the predicted probabilities of delay were shown to have acceptable accuracy 16 …”