Perioperative ΔNLR and ΔPNI independently predict postoperative pulmonary complications in non-small cell lung cancer: a difference analysis-based nomogram.

Abstract

This study investigated whether perioperative dynamic changes in inflammatory and immunonutritional markers could predict postoperative pulmonary complications (PPCs) in non-small cell lung cancer (NSCLC). We retrospectively enrolled 550 NSCLC patients who underwent radical resection, randomly split at a 7:3 ratio into training (n = 384) and validation (n = 166) sets. PPCs occurred in 126 patients (22.91%), with pneumonia being the most common type. Perioperative ΔWBC, ΔNLR, ΔPLR, ΔCAR, ΔLCR, ΔAGR, and ΔPNI were calculated as the values on postoperative day 1 minus the preoperative values. Through univariate screening, Spearman correlation analysis, variance inflation factor testing, LASSO regression, and multivariate logistic regression, five independent predictors were identified: age, lobectomy, number of lymph nodes dissected, ΔNLR, and ΔPNI. A nomogram incorporating these variables was constructed, achieving an AUC of 0.888 (95% CI: 0.843-0.934) in the training set and 0.887 (95% CI: 0.831-0.942) in the validation set, outperforming the ARISCAT score and single ΔPNI. Calibration was satisfactory (validation set Hosmer-Lemeshow test, P = 0.897; Brier score, 0.106), and decision curve analysis confirmed clinical net benefit. Risk stratification using the nomogram cutoff (0.23) effectively discriminated high-risk patients with significantly worse clinical outcomes. Subgroup and sensitivity analyses demonstrated robust predictive stability without obvious overfitting. The nomogram integrating perioperative ΔNLR and ΔPNI provides a practical tool for early identification and risk stratification of PPCs after NSCLC surgery.