Analysis of the pathogenic bacteria, drug resistance, and risk factors of postoperative infection in patients with non-small cell lung cancer

Development of a Nomogram for Predicting Surgical Site Infection in Patients with Resected Lung Neoplasm Undergoing Minimally Invasive Surgery

Background: Predictive models are necessary to target high-risk populations and provide precision interventions for patients with lung neoplasm who suffer from surgical site infections (SSI). Patients and Methods: This case control study included patients with lung neoplasm who underwent minimally invasive surgeries (MIS). Logistic regression was used to generate the prediction model of SSI, and a nomogram was created. A receiver operator characteristic (ROC) curve was used to examine the predictive value of the model. Results: A total of 151 patients with SSI were included, and 604 patients were randomly selected among the patients without SSI (ratio 4:1). Male gender (odds ratio [OR], 2.55; 95% confidence interval [CI], 1.57-4.15; p < 0.001), age >60 years (OR, 2.10; 95% CI, 1.29-3.44, p = 0.003), operation time >60 minutes (all categories, p < 0.05), treatments for diabetes mellitus (OR, 2.96; 95% CI, 1.75-4.98l; p < 0.001), and best forced expiratory volume in 1 second (FEV₁)/forced vital capacity (FVC; OR, 0.96; 95% CI, 0.94-0.99; p = 0.008) were independently associated with SSI. The model based on these variables showed an area under the curve (AUC) of 0.813 for predicting SSI. Conclusions: A nomogram predictive model was successfully established for predicting SSI in patients receiving MIS, with good predictive value.