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Article Abstract
Background: Anastomotic leakage is one of the most severe complications after esophageal cancer surgery. The purpose of this study was to evaluate the impact of preoperative inflammatory status on anastomotic leakage after esophageal cancer surgery and to construct a model for predicting anastomotic leakage after esophageal cancer surgery.
Methods: A retrospective analysis was conducted on 1106 patients with esophageal cancer who underwent surgical treatment between September 2018 and December 2022. Patients were randomly divided into training and testing sets at a ratio of 7:3. Logistic regression analysis and LASSO regression analysis were performed on the training set. Independent influencing factors selected from the analysis were used for model construction. Internal validation was then performed.
Results: A total of 1106 patients with esophageal cancer, with a mean age of 64.05 years, were included in our study. Among them, there were 785 male patients (71.0%) and 321 female patients (29.0%). Multivariate analysis revealed that a history of smoking (OR = 2.121, P = 0.016; 95% CI, 1.151-3.938), history of diabetes mellitus (OR = 5.473, P < 0.001; 95% CI, 2.587-11.382), high NMR (OR = 3.423, P = 0.002; 95% CI, 1.628-7.489), high PLR (OR = 3.675, P < 0.002; 95% CI, 1.642-8.406), and low PLT (OR = 0.986, P = < 0.001; 95% CI, 0.980-0.993) were independent risk factors for anastomotic leakage after esophageal cancer surgery. A forest plot was constructed for the independent risk factors, and the ROC curve analysis results showed that the model had good predictive ability in both the training and testing sets. Additionally, calibration curve and DCA curve analyses showed that the model had good predictive ability and net benefit.
Conclusion: This study found that smoking history, diabetes history and preoperative inflammatory status (preoperative high NMR, high PLR, and low PLT) were risk factors for postoperative anastomotic leakage in patients with esophageal cancer. Based on these findings, we constructed a model for predicting anastomotic leakage after esophageal cancer surgery that demonstrated good predictive ability.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12364653 | PMC |
| http://dx.doi.org/10.3389/fonc.2025.1587586 | DOI Listing |
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