Risk factors for postoperative pulmonary infection in patients with non-small cell lung cancer: analysis based on regression models and construction of a nomogram prediction model.

objectiveThis study was designed to analyze risk factors for postoperative pulmonary infection (PPI) in patients with non-small cell lung cancer (NSCLC) based on regression models and to construct a corresponding nomogram prediction model.

methodsA total of 244 patients with NSCLC who received surgical treatment from June 2015 to January 2017 were retrospectively analyzed. According to the PPI, they were assigned to a pulmonary infection group (n=27) or non-pulmonary infection group (n=217). The independent risk factors for PPI in NSCLC patients were screened through least absolute shrinkage and selection operator (LASSO) and logistic regression analysis, and a corresponding nomogram prediction model was constructed.

resultsA total of 244 NSCLC patients were included, including 27 with PPI (11.06%). According to LASSO regression-based screening, age, diabetes mellitus (DM), tumor node metastasis (TNM) staging, chemotherapy regimen, chemotherapy cycle, post-chemotherapy albumin (g/L), pre-chemotherapy KPS and operation time were all significant and found to be the influencing factors for PPI. The risk model constructed based on LASSO was 0.0035770333 + (0.0020227686* age) + (0.057554487* DM) + (0.016365428* TNM staging) + (0.048514458* chemotherapy regimen) + (0.00871801* chemotherapy cycle) + (-0.002096683* post-chemotherapy albumin (g/L) + (-0.00090206* pre-chemotherapy Karnofsky performance score (KPS)) + (0.000296876* operation time). The pulmonary infection group got significantly higher risk scores than the non-pulmonary infection group (P<0.0001). According to receiver operating characteristic (ROC) curve-based analysis, the area under the curve (AUC) of risk score in predicting pulmonary infection was 0.894. Based on 4 independent predictors, a risk-prediction nomogram model was constructed to predict pulmonary infection in NSCLC patients after surgery. The internal verification C-index was 0.900 (95% CI: 0.839-0.961), and the calibration curves were well fitted with the ideal ones.

conclusionThe prediction model based on a regression model for PPI in NSCLC patients demonstrates good prediction efficiency, which is conducive to early screening of high-risk patients and further improvement of treatment regimen.