title: Histologic severity of appendicitis can be predicted by computed tomography.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  Retrospective study.
  
  
  METHODS
  Mayo Clinic in Scottsdale, Ariz.
  
  
  METHODS
  Consecutive sample of 105 patients (50 women and 55 men, aged 15-89 years) undergoing nonincidental appendectomy within 3 days of nonfocused abdominal CT.
  
  
  METHODS
  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.
  
  
  METHODS
  Agreement between predicted and actual histologic severity, using weighted kappa 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 kappa 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.
authors.affiliation: Division of General Surgery, Departments of Surgery, Radiology, Pathology, and Biostatistics, Mayo Clinic in Scottsdale, Ariz, USA.
authors.forename: Adam J
authors.initials: AJ
authors.lastname: Hansen
authors.forename: Scott W
authors.initials: SW
authors.lastname: Young
authors.forename: Giovanni
authors.initials: G
authors.lastname: De Petris
authors.forename: Deron J
authors.initials: DJ
authors.lastname: Tessier
authors.forename: Jose L
authors.initials: JL
authors.lastname: Hernandez
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Johnson
mesh_terms: D000208:Acute Disease; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001064:Appendicitis / Q000000981:diagnostic imaging* / Q000473:pathology / Q000601:surgery; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D014057:Tomography, X-Ray Computed