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 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.
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 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: Maxim Avanesov; Nis Jesper Wiese; Murat Karul; Helena Guerreiro; Sarah Keller; Philip Busch; Frank Jacobsen; Gerhard Adam; Jin Yamamura Journal: Eur Radiol Date: 2018-03-14 Impact factor: 5.315
Authors: P Youatou Towo; A S E Ramadan; W Ngatchou; J N Djiélé; A Etienne; E Capelluto; Pr P Mols Journal: Eur J Trauma Emerg Surg Date: 2012-09-01 Impact factor: 3.693
Authors: W J Bom; M D Bolmers; S L Gans; C C van Rossem; A A W van Geloven; P M M Bossuyt; J Stoker; M A Boermeester Journal: BJS Open Date: 2021-03-05