ArticleJournal of inflammation research2025
Nomogram for Predicting Postoperative Cardiopulmonary Complications in Non-Small Cell Lung Cancer Based on Systemic Inflammatory Markers: A Retrospective Study.
Article in Journal of inflammation research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Association between the prognostic nutritional index and prognosis in patients with non-small cell lung cancer undergoing curative lung resection: a systematic review and meta-analysis.Frontiers in oncology · 2026Pooled it
- Comparison of PNI and GNRI nomogram models for predicting postoperative complications in elderly patients with lung cancer: a retrospective study.Frontiers in nutrition · 2026Article
- Perioperative ΔNLR and ΔPNI independently predict postoperative pulmonary complications in non-small cell lung cancer: a difference analysis-based nomogram.American journal of cancer research · 2026Article
- Early Postoperative C-Reactive Protein Trajectories After Thoracic Surgery: A Retrospective Cohort Study.Biomedicines · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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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.
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