The decision rule evaluated the likelihood of delayed diagnosis of appendicitis using variables contained in administrative data and based on investigators’ clinical expertise. These included age (<3 years, 3–10 years or ≥11 years), sex, history of a complex chronic condition,24 (link) revisit interval (days between initial and diagnosis encounters), diagnosis code for perforated appendicitis (ICD-9-CM 540.0–1, ICD-10-CM K35.2x, K35.32–33), length of stay of the diagnosis encounter (0–1, 2–3, 4–7 or>7 days), and individual presence or absence of specific diagnoses at the initial encounter including abdominal pain, constipation, dehydration, fever, gastroenteritis, genitourinary condition, head/ear/eye/nose/throat condition, leucocytosis, urinary tract infection, viral infection or none of the above (diagnosis codes in online supplemental table 2 ).
The full cohort was randomly divided into derivation (2/3) and validation (1/3) sets, stratified on the outcome. The decision rule was trained using only the derivation set. Variables were selected for inclusion in the decision rule using univariable logistic regressions. All variables associated with the outcome with p<0.20 were included in the decision rule. The final model underlying the decision rule was created using multivariable logistic regression within the derivation set using delayed diagnosis (determined by expert case review) as the outcome and all screened-in variables as predictors. The decision rule classified cases as delayed or not delayed using two thresholds: (1) a maximal accuracy threshold, based on the model predicted probability being greater than or equal to the value that maximises the proportion of correct classifications25 (link) and (2) a near-definite delay threshold if the predicted probability of delay was ≥90%.
The full cohort was randomly divided into derivation (2/3) and validation (1/3) sets, stratified on the outcome. The decision rule was trained using only the derivation set. Variables were selected for inclusion in the decision rule using univariable logistic regressions. All variables associated with the outcome with p<0.20 were included in the decision rule. The final model underlying the decision rule was created using multivariable logistic regression within the derivation set using delayed diagnosis (determined by expert case review) as the outcome and all screened-in variables as predictors. The decision rule classified cases as delayed or not delayed using two thresholds: (1) a maximal accuracy threshold, based on the model predicted probability being greater than or equal to the value that maximises the proportion of correct classifications25 (link) and (2) a near-definite delay threshold if the predicted probability of delay was ≥90%.
