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

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). 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. 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 (FEV)/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. A nomogram predictive model was successfully established for predicting SSI in patients receiving MIS, with good predictive value.