Histologic severity of appendicitis can be predicted by computed tomography

Hypothesis: A regression model based on computed tomographic (CT) findings alone can accurately predict the histologic severity of acute appendicitis in patients who have a high disease likelihood. Design: Retrospective study. Setting: Mayo Clinic in Scottsdale, Ariz. Patients: Consecutive sample of 105 patients (50 women and 55 men, aged 15-89 years) undergoing nonincidental appendectomy within 3 days of nonfocused abdominal CT. Interventions: Computed tomographic scans and histologic features were retrospectively reinterpreted. Each patient's histologic and CT findings were scored by standardized criteria. An ordinal logistic regression model was constructed with a subset of CT findings that statistically correlated best with the final histologic features. Predicted severity values were then generated from the model. Main Outcome Measure: Agreement between predicted and actual histologic severity, using weighted K measurement. Results: Computed tomography variables used in the model were fat stranding, appendix diameter, dependent fluid, appendolithiasis, extraluminal air, and the radiologist's overall confidence score. The weighted K measurement of agreement between predicted and actual histologic severity was 0.75, with a 95% confidence interval between the values of 0.59 and 0.90. Conclusions: Computed tomographic findings, when used with the regression model developed from this pilot study, can accurately predict the histologic severity of acute appendicitis in patients initially seen with a high clinical suspicion of the disease. These findings provide a platform from which to prospectively test the model.