BMC gastroenterology · 2024
Predictive model for identification of gangrenous or perforated appendicitis in adults: a multicenter retrospective study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Liang Yun, Sailai Maimaitiaili, Ding Rui, Yimamu Baihitiyaer, Kazi Tayierjiang, He Ming, Liu Zehui, Lin Junyu, Liu Yile, Deng Chaolun, Huang Jiangtao, Zhang Xingwei, Chen Zheng, Su Yonghui
Аннотация
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.
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.