Abstract

Background

Gangrene and perforation are severe complications of acute appendicitis, associated with a higher mortality rate compared to uncomplicated appendicitis. Accurate preoperative identification of Gangrenous or perforated appendicitis (GPA) is crucial for timely surgical intervention.

Methods

This retrospective multicenter study includes 796 patients who underwent appendectomy. Univariate and multivariate logistic regression analyses are used to develop a nomogram model for predicting GPA based on laboratory tests and computed tomography (CT) findings. The model is validated using an external dataset.

Results

Seven independent predictors were included in the nomogram: white blood cell count, lymphocyte count, D-dimer, serum glucose, albumin, maximum outer diameter of the appendix, and presence of appendiceal fecalith. The nomogram achieved good discrimination and calibration in both the training and testing sets. In the training set, the AUC was 0.806 (95%CI: 0.763–0.849), and the sensitivity and specificity were 82.1% and 66.9%, respectively. The Hosmer-Lemeshow test showed good calibration (P = 0.7378). In the testing set, the AUC was 0.799 (95%CI: 0.741–0.856), and the sensitivity and specificity were 70.5% and 75.3%, respectively. Decision curve analysis (DCA) confirmed the clinical utility of the nomogram.

Conclusion

The laboratory test-CT nomogram model can effectively identify GPA patients, aiding in surgical decision-making and improving patient outcomes.

Details

Title
Predictive model for identification of gangrenous or perforated appendicitis in adults: a multicenter retrospective study
Author
Liang, Yun; Sailai, Maimaitiaili; Yimamu, Rui Dingihitiyaer; kazi, Tayierjiang; He, Ming; Liu, Zehui; Lin, Junyu; Liu, Yile; Deng, Chaolun; Huang, Jiangtao; Zhang, Xingwei; Chen, Zheng; Su, Yonghui
Pages
1-10
Section
Research
Publication year
2024
Publication date
2024
Publisher
BioMed Central
e-ISSN
1471230X
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3115121914
Copyright
© 2024. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.