Analysis of risk factors for esophagojejunal anastomotic leakage after total gastrectomy based on Bayesian network model
ObjectivesThis research aims to develop a nomogram for predicting esophagojejunal anastomotic leakage (EJAL) after total gastrectomy and analyze the relationship between individual risk factors through the Bayesian network model.Materials and methodsThe research enrolled 238 patients who underwent t...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1632214/full |
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| author | Yun-Feng Wang Yun-Feng Wang Zi-Qi Guo Zi-Qi Guo Jing-Xiang Han Jing-Xiang Han Lin-Na Gao Lin-Na Gao Yu-Ming Liu Yu-Ming Liu Kai Jia Hao Chen Tian Yao He Huang He Huang He Huang |
| author_facet | Yun-Feng Wang Yun-Feng Wang Zi-Qi Guo Zi-Qi Guo Jing-Xiang Han Jing-Xiang Han Lin-Na Gao Lin-Na Gao Yu-Ming Liu Yu-Ming Liu Kai Jia Hao Chen Tian Yao He Huang He Huang He Huang |
| author_sort | Yun-Feng Wang |
| collection | DOAJ |
| description | ObjectivesThis research aims to develop a nomogram for predicting esophagojejunal anastomotic leakage (EJAL) after total gastrectomy and analyze the relationship between individual risk factors through the Bayesian network model.Materials and methodsThe research enrolled 238 patients who underwent total gastrectomy and esophagojejunal Roux-en-Y anastomosis for gastric cancer between January 2017 and June 2022 in the Department of Gastrointestinal Surgery of the First Hospital of Shanxi Medical University and retrospectively collected clinical data of the patients. Multivariable logistic regression was used to explore the risk factors of EJAL and a nomogram based on the results was constructed. The predictive ability of the model was assessed by receiver operating characteristic (ROC) curve and calibration curve. In addition, the clinical benefit was indicated by decision curve analysis (DCA). Ultimately, a Bayesian network model was developed to analyze the interrelationship between the risk factors.ResultsEsophagojejunal anastomotic leakage occurred in 13 of 238 patients (5.4%). End-to-side anastomosis, diabetes mellitus (DM), preoperative albumin (ALB) ≤ 33.6 g/L, drinking history and systemic inflammation response index (SIRI) > 1.18 were identified as independent risk factors for EJAL based on multivariable logistic regression. A nomogram containing the aforementioned factors was constructed, with an area under the receiver operating characteristic curve (AUROC) of 0.880. Likewise, the model showed good predictive ability and clinical application in the calibration curve and DCA. Ultimately, the Bayesian network model demonstrates that type of anastomosis (ToA), DM, and ALB were directly associated with EJAL development, while gender, age, drinking history, smoking history, hypertension, and SIRI were conditionally dependent on EJAL given the presence of mediator variables.ConclusionSurgeons should be alert to the occurrence of EJAL, especially in patients with end-to-side anastomosis, DM, drinking history, preoperative lower ALB, and higher SIRI. Also, males, advanced age, smoking history, and hypertension can affect the development of EJAL. |
| format | Article |
| id | doaj-art-80e5abeb9d41495d97d62dcb1fda0126 |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-80e5abeb9d41495d97d62dcb1fda01262025-08-20T03:43:55ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-08-011210.3389/fmed.2025.16322141632214Analysis of risk factors for esophagojejunal anastomotic leakage after total gastrectomy based on Bayesian network modelYun-Feng Wang0Yun-Feng Wang1Zi-Qi Guo2Zi-Qi Guo3Jing-Xiang Han4Jing-Xiang Han5Lin-Na Gao6Lin-Na Gao7Yu-Ming Liu8Yu-Ming Liu9Kai Jia10Hao Chen11Tian Yao12He Huang13He Huang14He Huang15The First Clinical Medical College, Shanxi Medical University, Taiyuan, Shanxi, ChinaDepartment of Gastrointestinal Surgery, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, ChinaThe First Clinical Medical College, Shanxi Medical University, Taiyuan, Shanxi, ChinaDepartment of Gastrointestinal Surgery, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, ChinaThe First Clinical Medical College, Shanxi Medical University, Taiyuan, Shanxi, ChinaDepartment of Nutrition and Food Hygiene, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, ChinaThe First Clinical Medical College, Shanxi Medical University, Taiyuan, Shanxi, ChinaDepartment of Gastrointestinal Surgery, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, ChinaThe First Clinical Medical College, Shanxi Medical University, Taiyuan, Shanxi, ChinaDepartment of Gastrointestinal Surgery, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, ChinaDepartment of Gastrointestinal Surgery, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, ChinaDepartment of Gastrointestinal Surgery, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, ChinaDepartment of Gastrointestinal Surgery, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, ChinaThe First Clinical Medical College, Shanxi Medical University, Taiyuan, Shanxi, ChinaDepartment of Gastrointestinal Surgery, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, ChinaDepartment of Nutrition and Food Hygiene, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, ChinaObjectivesThis research aims to develop a nomogram for predicting esophagojejunal anastomotic leakage (EJAL) after total gastrectomy and analyze the relationship between individual risk factors through the Bayesian network model.Materials and methodsThe research enrolled 238 patients who underwent total gastrectomy and esophagojejunal Roux-en-Y anastomosis for gastric cancer between January 2017 and June 2022 in the Department of Gastrointestinal Surgery of the First Hospital of Shanxi Medical University and retrospectively collected clinical data of the patients. Multivariable logistic regression was used to explore the risk factors of EJAL and a nomogram based on the results was constructed. The predictive ability of the model was assessed by receiver operating characteristic (ROC) curve and calibration curve. In addition, the clinical benefit was indicated by decision curve analysis (DCA). Ultimately, a Bayesian network model was developed to analyze the interrelationship between the risk factors.ResultsEsophagojejunal anastomotic leakage occurred in 13 of 238 patients (5.4%). End-to-side anastomosis, diabetes mellitus (DM), preoperative albumin (ALB) ≤ 33.6 g/L, drinking history and systemic inflammation response index (SIRI) > 1.18 were identified as independent risk factors for EJAL based on multivariable logistic regression. A nomogram containing the aforementioned factors was constructed, with an area under the receiver operating characteristic curve (AUROC) of 0.880. Likewise, the model showed good predictive ability and clinical application in the calibration curve and DCA. Ultimately, the Bayesian network model demonstrates that type of anastomosis (ToA), DM, and ALB were directly associated with EJAL development, while gender, age, drinking history, smoking history, hypertension, and SIRI were conditionally dependent on EJAL given the presence of mediator variables.ConclusionSurgeons should be alert to the occurrence of EJAL, especially in patients with end-to-side anastomosis, DM, drinking history, preoperative lower ALB, and higher SIRI. Also, males, advanced age, smoking history, and hypertension can affect the development of EJAL.https://www.frontiersin.org/articles/10.3389/fmed.2025.1632214/fullgastric canceresophagojejunal anastomotic leakagetype of anastomosisprediction modelBayesian network model |
| spellingShingle | Yun-Feng Wang Yun-Feng Wang Zi-Qi Guo Zi-Qi Guo Jing-Xiang Han Jing-Xiang Han Lin-Na Gao Lin-Na Gao Yu-Ming Liu Yu-Ming Liu Kai Jia Hao Chen Tian Yao He Huang He Huang He Huang Analysis of risk factors for esophagojejunal anastomotic leakage after total gastrectomy based on Bayesian network model Frontiers in Medicine gastric cancer esophagojejunal anastomotic leakage type of anastomosis prediction model Bayesian network model |
| title | Analysis of risk factors for esophagojejunal anastomotic leakage after total gastrectomy based on Bayesian network model |
| title_full | Analysis of risk factors for esophagojejunal anastomotic leakage after total gastrectomy based on Bayesian network model |
| title_fullStr | Analysis of risk factors for esophagojejunal anastomotic leakage after total gastrectomy based on Bayesian network model |
| title_full_unstemmed | Analysis of risk factors for esophagojejunal anastomotic leakage after total gastrectomy based on Bayesian network model |
| title_short | Analysis of risk factors for esophagojejunal anastomotic leakage after total gastrectomy based on Bayesian network model |
| title_sort | analysis of risk factors for esophagojejunal anastomotic leakage after total gastrectomy based on bayesian network model |
| topic | gastric cancer esophagojejunal anastomotic leakage type of anastomosis prediction model Bayesian network model |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1632214/full |
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