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Adam J. Hansen, MD; Scott W. Young, MD; Giovanni De Petris, MD; Deron J. Tessier, MD; Jose L. Hernandez, BA; Daniel J. Johnson, MD

Arch Surg. 2004;139:1304-1308.

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 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 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.

Author Affiliations: Division of General Surgery, Departments of Surgery (Drs Hansen, Tessier, and Johnson), Radiology (Dr Young), Pathology (Dr De Petris), and Biostatistics (Mr Hernandez), Mayo Clinic in Scottsdale, Ariz.

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