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Nomogram for Predicting Postoperative Cardiopulmonary Complications in Non-Small Cell Lung Cancer Based on Systemic
Zemin He1, Keting Liu2, Ling Wu3
1Department of Thoracic Surgery, The First People's Hospital of Shuangliu District (West China Airport Hospital of Sichuan University), Chendu, Sichuan Province, 610200, People's Republic of China.
Objective:
The objective of this study is to investigate the association between systemic inflammatory markers and postoperative cardiopulmonary complications in patients with non-small cell lung cancer (NSCLC). Additionally, the study aims to develop a column chart tool to improve the accuracy of predicting the risk of postoperative cardiopulmonary complications in NSCLC patients.
Methods:
This study analyzed data on patients with lung cancer who underwent surgery in our department from July 2022 to December 2024.Patients were divided into training and validation sets.Logistic regression analysis was used to construct a column chart and identify predictive factors for cardiopulmonary complications.The chart's performance was evaluated using the C-index, the AUC, the calibration curve, and the decision curve analysis.The validation set was used for further model evaluation.
Results:
Multivariate logistic regression analysis demonstrated that smoking history, postoperative neutrophil count, postoperative systemic immunoinflammatory index (SII), ΔSII (change in SII), ΔPLR (change in platelet-lymphocyte ratio), and ΔAISI (change in neutrophil * platelet * monocyte/lymphocyte ratio) were predictive factors for postoperative cardiopulmonary complications. In the training set, the C-index of the model is 0.86 (95% confidence interval: 0.82-0.91), while in the validation set it is 0.81 (95% confidence interval: 0.73-0.89). The calibration curve demonstrates a strong correlation between the column chart model and the observed data. The decision curve analysis indicates that the net profit of this model is considerably superior to that of other models.
Conclusion:
The present study successfully developed and validated a predictive model based on systemic inflammatory markers to assess the risk of postoperative cardiopulmonary complications in patients with small cell lung cancer. This model assists clinicians in accurately assessing patients' risk of postoperative cardiovascular and pulmonary complications, thereby promoting personalized patient management.
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