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Updated: Jan 8, 2026

Murine Appendectomy Model of Chronic Colitis Associated Colorectal Cancer by Precise Localization of Caecal Patch
Published on: August 24, 2019
A new score for predicting incidental appendiceal neoplasms in patients aged ≥40 years with acute appendicitis: a
Hikmet Pehlevan Özel1, Sadettin Er1, İbrahim Doğan2
1Department of General Surgery, Ankara Bilkent City Hospital, Ankara-Türkiye.
Background:
This multicenter retrospective cohort study aimed to develop a novel five-parameter scoring model to predict incidental appendiceal neoplasia in patients aged ≥40 years diagnosed with acute appendicitis. Previous literature has reported a significant increase in the risk of neoplasia, particularly after age 40, and this cut-off value was used as the basis for our study design.
Methods:
A multicenter retrospective cohort analysis was conducted across six tertiary hospitals between January 2019 and December 2024. Adult patients aged ≥40 years who underwent appendectomy with preoperative contrast-enhanced computed tomography (CT) and had available laboratory data were included. Predictive variables were identified using multivariate logistic regression. The scoring system incorporated age, female sex, appendix diameter, absence of CT wall enhancement, and low neutrophil count. Diagnostic performance was assessed via receiver operating characteristic (ROC) analysis.
Results:
Of 2,143 patients analyzed, 122 (5.7%) had incidental neoplasia. The scoring system yielded an area under the ROC curve of 0.641 (95% confidence interval: 0.594-0.690). Using a cutoff score ≥3, sensitivity was 56.6%, specificity 65.4%, positive predictive value 9%, and negative predictive value 96.1%. Patients with a maximum score of 5 had a 33.3% incidence of neoplasia.
Conclusion:
This five-parameter scoring system demonstrates a high negative predictive value and may help identify low-risk patients in whom standard appendectomy can be safely performed. This model, which allows low-risk patients to be safely excluded thanks to its high negative predictive value, provides an innovative contribution to clinical decision-support systems.
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