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已发表论文
腹腔镜胆囊切除术后肺部感染危险因素分析及列线图预测模型构建
Authors Huang X, Li Y, Huang X, Chen Y
Received 31 July 2025
Accepted for publication 16 January 2026
Published 22 January 2026 Volume 2026:19 557317
DOI https://doi.org/10.2147/IDR.S557317
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Hazrat Bilal
Xiaomei Huang, Yiping Li, Xiaoliang Huang, Yijian Chen
Department of Anesthesiology, Ganzhou People’s Hospital, Ganzhou, Jiangxi Province, 341000, People’s Republic of China
Correspondence: Yijian Chen, Department of Anesthesiology, Ganzhou People’s Hospital, 16 Meiguan Avenue, Zhanggong District, Ganzhou City, Jiangxi Province, 341000, People’s Republic of China, Email Gl190341000@126.com
Objective: To explore the risk factors of pulmonary infection after laparoscopic cholecystectomy and construct a nomogram prediction model.
Methods: From July 2022 to April 2025, 594 patients who underwent laparoscopic cholecystectomy in our hospital were included and randomly separated into a modeling group of 416 cases and a validation group of 178 cases in a 7:3 ratio. The modeling group was separated into an infection group of 50 cases and a non infection group of 366 cases based on the occurrence of postoperative pulmonary infection. The operative time, preoperative albumin (Alb), preoperative WBC and other data were recorded. Single factor and multiple factor logistic regression analyses were performed to determine risk factors. R software was performed to construct nomogram prediction models. Receiver Operating Characteristic(ROC) curve was used to evaluate discrimination, the calibration curve was used to evaluate calibration, and the clinical decision curve was used to evaluate net benefit.
Results: Parameters like male sex, diabetes, long operative time, and high preoperative WBC were independent risk factors for pulmonary infection after laparoscopic cholecystectomy, while high preoperative Alb was an independent protective factor (P< 0.05). The Hosmer-Lemeshow test for the modeling and validation groups showed P=0.43,0.35; the Area Under the Curve(AUC) values of the ROC curve were 0.94 (95% CI: 0.90~0.98) and 0.93 (95% CI: 0.89~0.97). In the calibration curve results, the predicted probability was basically consistent with the actual probability. In the clinical decision curve results, the nomogram prediction model provided greater net benefits within the threshold probability ranges of 2%~83% and 3%~86%.
Conclusion: The nomogram model constructed in this study can be effectively used to evaluate the individual risk of pulmonary infection after laparoscopic cholecystectomy.
Keywords: laparoscopic cholecystectomy, pulmonary infection, risk factors, nomogram
Department of Anesthesiology, Ganzhou People’s Hospital, Ganzhou, Jiangxi Province, 341000, People’s Republic of China
Correspondence: Yijian Chen, Department of Anesthesiology, Ganzhou People’s Hospital, 16 Meiguan Avenue, Zhanggong District, Ganzhou City, Jiangxi Province, 341000, People’s Republic of China, Email Gl190341000@126.com
Objective: To explore the risk factors of pulmonary infection after laparoscopic cholecystectomy and construct a nomogram prediction model.
Methods: From July 2022 to April 2025, 594 patients who underwent laparoscopic cholecystectomy in our hospital were included and randomly separated into a modeling group of 416 cases and a validation group of 178 cases in a 7:3 ratio. The modeling group was separated into an infection group of 50 cases and a non infection group of 366 cases based on the occurrence of postoperative pulmonary infection. The operative time, preoperative albumin (Alb), preoperative WBC and other data were recorded. Single factor and multiple factor logistic regression analyses were performed to determine risk factors. R software was performed to construct nomogram prediction models. Receiver Operating Characteristic(ROC) curve was used to evaluate discrimination, the calibration curve was used to evaluate calibration, and the clinical decision curve was used to evaluate net benefit.
Results: Parameters like male sex, diabetes, long operative time, and high preoperative WBC were independent risk factors for pulmonary infection after laparoscopic cholecystectomy, while high preoperative Alb was an independent protective factor (P< 0.05). The Hosmer-Lemeshow test for the modeling and validation groups showed P=0.43,0.35; the Area Under the Curve(AUC) values of the ROC curve were 0.94 (95% CI: 0.90~0.98) and 0.93 (95% CI: 0.89~0.97). In the calibration curve results, the predicted probability was basically consistent with the actual probability. In the clinical decision curve results, the nomogram prediction model provided greater net benefits within the threshold probability ranges of 2%~83% and 3%~86%.
Conclusion: The nomogram model constructed in this study can be effectively used to evaluate the individual risk of pulmonary infection after laparoscopic cholecystectomy.
Keywords: laparoscopic cholecystectomy, pulmonary infection, risk factors, nomogram