A Clinical Prediction Model for Postoperative Pneumonia After Lung Cancer Surgery

Postoperative 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. Patients who underwent lung cancer surgery be...

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Bibliographic Details
Published in:The Journal of surgical research 2023-04, Vol.284, p.62-69
Main Authors: Zhou, Jian, Wu, Dongsheng, Zheng, Quan, Wang, Tengyong, Lin, Mingying, Lu, Tianyi, Lin, Feng
Format: Article
Language:English
Subjects:
Humans
Lung cancer surgery
Lung Neoplasms - surgery
Models, Statistical
Nomograms
Pneumonia - epidemiology
Pneumonia - etiology
Postoperative pneumonia
Prediction model
Prognosis
Retrospective Studies
Risk factor
Risk Factors
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Summary:Postoperative 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. Patients 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. A 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. Based 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.
ISSN:0022-4804
1095-8673
DOI:10.1016/j.jss.2022.11.021