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ORIGINAL RESEARCH article

Front. Med.

Sec. Gastroenterology

Volume 12 - 2025 | doi: 10.3389/fmed.2025.1632214

Analysis of risk factors for esophagojejunal anastomotic leakage after total gastrectomy based on Bayesian Network model

Provisionally accepted
Yun-Feng  WangYun-Feng Wang1Zi-Qi  GuoZi-Qi Guo2Jing-Xiang  HanJing-Xiang Han2Lin-Na  GaoLin-Na Gao2Yu-Ming  LiuYu-Ming Liu2Kai  JiaKai Jia1Hao  ChenHao Chen1Tian  YaoTian Yao1*He  HuangHe Huang1*
  • 1First Hospital of Shanxi Medical University, Taiyuan, China
  • 2Shanxi Medical University, Taiyuan, Shanxi Province, China

The final, formatted version of the article will be published soon.

Objective: This 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. Material and Methods: The 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. Results: EJAL occurred in 13 of 238 patients (5.4%). End-to-side anastomosis, diabetes mellitus (DM), preoperative albumin (ALB) ≤33.6g/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. Conclusions: Surgeons 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.

Keywords: gastric cancer, Esophagojejunal anastomotic leakage, type of anastomosis, Prediction model, Bayesian network model

Received: 20 May 2025; Accepted: 17 Jul 2025.

Copyright: © 2025 Wang, Guo, Han, Gao, Liu, Jia, Chen, Yao and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Tian Yao, First Hospital of Shanxi Medical University, Taiyuan, China
He Huang, First Hospital of Shanxi Medical University, Taiyuan, China

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