Construction and external validation of Lasso-logistic regression prediction model for pulmonary infection after radical lung cancer surgery

Objective: To construct a Lasso-logistic regression prediction model for pulmonary infection after radical lung cancer surgery and conduct external validation.Methods: A total of 730 lung cancer patients who underwent radical surgery for lung cancer were randomly divided into a training group (n=511) and a validation group (n=219) in a 7:3 ratio.The incidence of pulmonary infection within 3 days after surgery in the training group and the validation group was calculated, and the clinical data of patients with and without pulmonary infection were compared.In the training group, the influencing factors of pulmonary infection after lung cancer surgery were analyzed using the Lasso-logistic regression model, and the Lasso-logistic regression nomogram prediction model for pulmonary infection was constructed based on the influencing factors and was externally validated in the validation group.Results: The incidence of postoperative pulmonary infection in 730 lung cancer patients in this study was 17.81%, with 17.42% in the training group and 18.72% in the validation group, and there was no statistical significance between groups (P> 0.05).In the training group and the validation group, the age, smoking history, tumor stage, diabetes, chronic obstructive pulmonary disease (COPD), drainage tube retention time, operation time, mechanical ventilation time, serum C-reactive protein (CRP), procalcitonin (PCT), serum amyloid A (SAA), high mobility group protein B1 (HMGB1), soluble CD14 (sCD14), and CD4⁺/CD8⁺levels of patients with and without pulmonary infection were compared, and the differences were statistically significant(P< 0.05).The Lasso-logistic regression model analysis showed that age, COPD, mechanical ventilation time, CRP, PCT, SAA, HMGB1, sCD14, and CD4⁺/CD8⁺were used as predictive factors for lung infection in lung cancer patients after radical surgery, resulting in excellent model performance with minimal influencing factors; the Lassologistic regression nomogram prediction model for lung infection was constructed based on the 9 predictive factors screened out by Lasso-logistic regression.External validation showed that the area under the curve(AUC)of the model for predicting pulmonary infection was 0.921 (95% CI:0.818-0.984), with predictive sensitivity and specificity of 91.83% and 97.26%, respectively.This model had good predictive performance, and its calibration for predicting pulmonary infection was good, which was well consistent with actual observation results.Conclusion: The incidence of pulmonary infection after lung cancer radical surgery is high, and there are many influencing factors.The Lasso-logistic regression nomogram prediction model for pulmonary infection based on the screened relevant predictive factors has good predictive value in predicting the risk of pulmonary infection, which is helpful to guide clinical prevention of postoperative pulmonary infection.