Risk Factors Analysis and Nomogram Prediction Model Construction for Pulmonary Infection After Laparoscopic Cholecystectomy.

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.