Objective: Chronic post-surgical pain (CPSP) following thoracoscopic lung cancer surgery is a common and challenging complication. Identifying risk factors and predictive markers is essential for improving patient outcome.
Methods: In this retrospective case-control study, the clinical data from 106 patients with non-small cell lung cancer (NSCLC) who underwent thoracoscopic radical resection between January 2021 and December 2023 were comprehensively analyzed. Patients were divided into a CPSP group (n = 41) and a non-CPSP group (n = 65) based on CPSP status. An external validation cohort of 20 patients was also assessed. Demographic data, perioperative characteristics, psychological states, and pain scores were compared between the two groups. Logistic regression analysis was used to identify predictors of CPSP, and their predictive performance was validated using receiver operating characteristic (ROC) curve analysis.
Results: Age and TNM stage were significantly higher in the CPSP group (P < 0.001). Significant differences were observed in pain scores on postoperative days 1-3 and Fear of Pain Questionnaire-III (FPQ-III) scores (P = 0.003 and P < 0.001, respectively) between the two groups. Multivariate logistic regression identified age (OR, 1.230; P < 0.001), TNM staging (OR, 5.106; P < 0.001), early postoperative pain score (OR, 1.868; P = 0.012), and FPQ-III score (OR, 1.135; P < 0.001) as independent predictors of CPSP. A nomogram based on these predictors demonstrated excellent discrimination ability, with an area under the curve (AUC) of 0.891. External validation yielded an AUC of 0.956, confirming high sensitivity (1.00) and specificity (0.923).
Conclusion: Age, advanced TNM stage, early postoperative pain intensity, and higher fear of pain are significant predictors of chronic postoperative pain following thoracoscopic lung cancer surgery. Incorporating these factors into predictive models may improve postoperative management and reduce CPSP incidence.
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http://dx.doi.org/10.62347/NHTK3687 | DOI Listing |
J Breath Res
January 2025
School of Medicine and Pharmacy, Ocean University of China, 5 Yushan Rd, Qingdao, Shandong, 266003, CHINA.
Lung cancer is one of the most common malignancy in the world, and early detection of lung cancer remains a challenge. The exhaled breath condensate (EBC) from lung and trachea can be collected totally noninvasively. In this study, our aim is to identify differential metabolites between non-small cell lung cancer (NSCLC) and control EBC samples and discriminate NSCLC group from control group by orthogonal projections to latent structures-discriminant analysis (OPLS-DA) models.
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January 2025
School of Public Health, Capital Medical University, Beijing, 100069, P. R. China.
Substantial epidemiological evidence suggests a significant correlation between particulate matter 2.5 (PM) and lung cancer. However, the mechanism underlying this association needs to be further elucidated.
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January 2025
Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, 211166, P. R. China.
A previous study classifies solid tumors based on collagen deposition and immune infiltration abundance, identifying a refractory subtype termed armored & cold tumors, characterized by elevated collagen deposition and diminished immune infiltration. Beyond its impact on immune infiltration, collagen deposition also influences tumor angiogenesis. This study systematically analyzes the association between immuno-collagenic subtypes and angiogenesis across diverse cancer types.
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January 2025
Department of Electrical and Computer Engineering, University of Cyprus, Nicosia 2112 Cyprus.
Breath analysis is increasingly recognized as a powerful noninvasive diagnostic technique, and a plethora of exhaled volatile biomarkers have been associated with various diseases. However, traditional analytical methodologies are not amenable to high-throughput diagnostic applications at the point of need. An optical spectroscopic technique, surface-enhanced Raman spectroscopy (SERS), mostly used in the research setting for liquid sample analysis, has recently been applied to breath-based diagnostics.
View Article and Find Full Text PDFAm J Physiol Lung Cell Mol Physiol
January 2025
Research Institute of the, McGill University Health Centre, Montreal, QC, Canada.
The increasing shift from cannabis smoking to cannabis vaping is largely driven by the perception that vaping to form an aerosol represents a safer alternative to smoking and is a form of consumption appealing to youth. Herein, we compared the chemical composition and receptor-mediated activity of cannabis smoke extract (CaSE) to cannabis vaping extract (CaVE) along with the biological response in human bronchial epithelial cells. Chemical analysis using HPLC and GC/MS revealed that cannabis vaping aerosol contained fewer toxicants than smoke; CaSE and CaVE contained teratogens, carcinogens, and respiratory toxicants.
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