Prediction of pulmonary infection during chemotherapy in non-small cell lung cancer: development and internal validation of a nomogram

Objective To develop and internally validate a nomogram for predicting pulmonary infection during chemotherapy in patients with non-small cell lung cancer (NSCLC). Methods This retrospective study included 246 NSCLC patients receiving cytotoxic chemotherapy. The primary outcome was pulmonary infection during chemotherapy. Patients were randomly divided into training (n=196) and validation (n=50) cohorts. Predictors were selected using AIC-guided forward stepwise logistic regression to construct a nomogram. Model performance was evaluated by discrimination, calibration, threshold-based classification metrics, and decision curve analysis, with internal validation performed using bootstrap resampling. Results Pulmonary infection occurred in 55 patients (22.4%). The final model included five predictors: absolute neutrophil count, serum albumin, age, combined immunotherapy, and chronic obstructive pulmonary disease. The apparent AUC was 0.786 (95% CI 0.703–0.869), with an optimism-corrected AUC of 0.735 and a validation AUC of 0.765 (95% CI 0.562–0.968). The nomogram classified patients into low-, intermediate-, and high-risk groups with infection rates of 5.4%, 18.2%, and 37.5%, respectively. Decision curve analysis suggested modest clinical net benefit within clinically relevant threshold probability ranges. Conclusion This internally validated nomogram, based on routinely available pre-chemotherapy variables, may provide a practical approach for preliminary pulmonary infection risk stratification in patients with NSCLC.