Community Validation of an Approach to Detect Delayed Diagnosis of Appendicitis in Big Databases

Hosp Pediatr. 2023 Jul 1;13(7):e170-e174. doi: 10.1542/hpeds.2023-007204.


Background: Detection of delayed diagnosis using administrative databases may illuminate the healthcare settings at highest risk. A method for detection of delays in claims has been validated in children's hospitals. We sought to further validate the method in community emergency departments (EDs).

Methods: We studied patients <21 years old diagnosed with appendicitis from 2008 to 2019 in 8 eastern Massachusetts EDs. Eligible patients had 2 ED encounters within 7 days, the second with an appendicitis diagnosis. Delayed diagnosis was evaluated in medical records by trained reviewers. A previously validated trigger tool was applied to participants' electronic medical record data. The tool used data elements included in administrative data, including initial encounter diagnoses, time between encounters, presence of medical complexity, and ultimate length of stay. The tool assigned a probability of delayed diagnosis for each patient. Test characteristics at 4 confidence thresholds were determined, and the area under the receiver operating curve was calculated.

Results: We analyzed 68 children with 2 encounters leading to a diagnosis of appendicitis (i.e., possible delay). When assigning a delayed diagnosis prediction to patients at 4 thresholds of confidence (>0%, >50%, >75%, and >90% confident), the positive predictive values were respectively 74%, 89%, 92%, and 89%; the negative predictive values were respectively 100%, 57%, 50%, and 33%. The area under the receiver operating curve was 0.837 (95% confidence interval 0.719-0.954).

Conclusions: A trigger tool that identifies delays in diagnosis using only administrative data in community EDs has a high positive predictive value for true delay. The tool may be applied in community EDs.

MeSH terms

  • Adult
  • Appendicitis* / diagnosis
  • Child
  • Databases, Factual
  • Delayed Diagnosis*
  • Electronic Health Records
  • Emergency Service, Hospital
  • Humans
  • Predictive Value of Tests
  • Retrospective Studies
  • Young Adult