A Nomogram for Predicting Pulmonary Complications Following Laparoscopic Surgery in Elderly Patients After the COVID-19 Pandemic.

Background: Postoperative pulmonary complications (PPCs) are common and serious after laparoscopic surgery, better than cardiac complications in predicting long-term mortality after non-cardiac surgery. In the post-epidemic era, the specific incidence of PPCs and their risk factors remain unclear.

Methods: This two-center retrospective study used the eligible patients' demographics and clinical characteristics to develop a prediction model. These patients who had undergone laparoscopic surgery from January 2023 to April 2024 were randomized into the training set and validation set. The main outcome was the incidence of PPCs. The multi-input processing method was used for missing data imputation. The variables with a P-value ≤0.05 and the covariates considered meaningful in clinical practice in univariate logistic regression analysis were subjected to multivariate logistic regression analysis to determine the independent risk factors of PPCs. The ROC, AUC, calibration curve, and clinical decision curve analysis of both sets were used to evaluate the model's predictive accuracy.

Results: 278 patients (21.21%) developed PPCs. Surgical site and the comorbidities (except pulmonary diseases), pulmonary abnormalities, 24-h white blood cell count, and 24-h neutrophil percentage before surgery were independent risk factors for PPCs and used for the establishment of a nomogram prediction model for PPC risk in elderly patients. The AUC value, sensitivity, and specificity were 0.88, 75.4%, and 87.6% respectively in the model's ROC curve. Internal verification (AUC: 0.86) confirmed the model's good calibration and discrimination abilities. Clinical decision curve analysis showed that the model had a positive clinical net benefit within the risk threshold range of 0%~30%.

Conclusion: This study identified the high-risk individuals of PPCs in elderly patients. PPC risk in elderly patients after laparoscopic surgery could be effectively reduced by optimizing surgical site selection, controlling preoperative comorbidities, adjusting preoperative lung conditions, and monitoring preoperative 24-h white blood cell count and neutrophil percentage.