A Clinical Prediction Model for Postoperative Pneumonia After Lung Cancer Surgery

Abstract

IntroductionPostoperative pneumonia (POP) is a common complication following lung cancer surgery and is associated with increased hospitalization costs and mortalities. We aimed to identify risk factors associated with POP and to develop a reliable predictive model. MethodsPatients who underwent lung cancer surgery between January 2015 and December 2021 in our hospital were enrolled. Least absolute shrinkage and selection operator regression analysis was used to select predictors of POP. Multivariable logistic regression was performed to construct the nomogram. Bootstrap resampling was conducted for internal validation. The performance of the model was evaluated by discrimination and calibration. ResultsA total of 5269 consecutive patients were enrolled. POP occurred in 1.7% of patients (92/5269). Five independent predictors were identified: age, predicted forced expiratory volume in 1 s, predicted diffusing capacity of the lungs for carbon monoxide, tuberculosis history, and surgery duration. The multivariable regression model showed good discrimination (C-index: 0.821, 95% confidence interval, 0.783-0.859), which was well validated by internal validation. The calibration curve illustrated good agreement between the predicted probability and observed probability of POP. ConclusionsBased on the easily available risk factors, our nomogram could predict the risk of POP with good discrimination and calibration. The model has good clinical practicability, enabling precise and targeted interventions to reduce the incidence of POP in high-risk patients.