Work

Nomogram for Predicting Postoperative Cardiopulmonary Complications in Non-Small Cell Lung Cancer Based on Systemic Inflammatory Markers: A Retrospective Study

Year: 2025

Type: article

Abstract: 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.

Source: Journal of Inflammation Research

DOI: 10.2147/jir.s519449

Authors: Zemin He, Keting Liu, Ling Wu, Qiang Wei

Institutions: Sichuan University, The First People's Hospital of Shuangliu District, Chengdu Seventh People's Hospital

Language: English

Topic: Inflammatory Biomarkers in Disease Prognosis

Subfield: Oncology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,836