Risk prediction model of anastomotic fistula after radical resection of esophageal cancer: A systematic review and meta-analysis_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

LI Tao ¹ ,  JIANG Yunlan ² , KANG Jing ¹ , SONG Shuang ¹ , DU Qiufeng ¹ , YI Xiaodong ¹

1. Department of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, P. R. China;
2. Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, P. R. China;

Corresponding author：

JIANG Yunlan, Email: 18980880152@163.com

Keywords：

Esophageal carcinoma; anastomotic fistula; risk prediction model; systematic review/meta-analysis

DOI：

10.7507/1007-4848.202305014

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To systematically evaluate the risk prediction model of anastomotic fistula after radical resection of esophageal cancer, and to provide objective basis for selecting a suitable model. Methods A comprehensive search was conducted on Chinese and English databases including CNKI, Wanfang, VIP, CBM, PubMed, EMbase, Web of Science, The Cochrane Library for relevant studies on the risk prediction model of anastomotic fistula after radical resection of esophageal cancer from inception to April 30, 2023. Two researchers independently screened literatures and extracted data information. PROBAST tool was used to assess the risk of bias and applicability of included literatures. Meta-analysis was performed on the predictive value of common predictors in the model with RevMan 5.3 software. Results A total of 18 studies were included, including 11 Chinese literatures and 7 English literatures. The area under the curve (AUC) of the prediction models ranged from 0.68 to 0.954, and the AUC of 10 models was >0.8, indicating that the prediction performance was good, but the risk of bias in the included studies was high, mainly in the field of research design and data analysis. The results of the meta-analysis on common predictors showed that age, history of hypertension, history of diabetes, C-reactive protein, history of preoperative chemotherapy, hypoproteinemia, peripheral vascular disease, pulmonary infection, and calcification of gastric omental vascular branches are effective predictors for the occurrence of anastomotic leakage after radical surgery for esophageal cancer (P<0.05). Conclusion The study on the risk prediction model of anastomotic fistula after radical resection of esophageal cancer is still in the development stage. Future studies can refer to the common predictors summarized by this study, and select appropriate methods to develop and verify the anastomotic fistula prediction model in combination with clinical practice, so as to provide targeted preventive measures for patients with high-risk anastomotic fistula as soon as possible.

Citation： LI Tao, JIANG Yunlan, KANG Jing, SONG Shuang, DU Qiufeng, YI Xiaodong. Risk prediction model of anastomotic fistula after radical resection of esophageal cancer: A systematic review and meta-analysis. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2025, 32(3): 385-392. doi: 10.7507/1007-4848.202305014 Copy

1.	Guinan EM, Dowds J, Donohoe C, et al. The physiotherapist and the esophageal cancer patient: From prehabilitation to rehabilitation. Dis Esophagus, 2017, 30(1): 1-12.
2.	Zeng H, Zheng R, Guo Y, et al. Cancer survival in China, 2003-2005: A population-based study. Int J Cancer, 2015, 136(8): 1921-1930.
3.	Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2018, 68(6): 394-424.
4.	中国肿瘤学会指南工作委员会组织. 中国临床肿瘤学会(CSCO)食管癌诊疗指南-2019. 北京: 人民卫生出版社, 2019. 615-620.The Guidelines Working Committee of Chinese Society of Cancer. Chinese Society of Clinical Oncology (CSCO) guidelines for the diagnosis and treatment of esophageal cancer-2019. Beijing: People's Medical Publishing House, 2019. 615-620.
5.	Seesing MFJ, Gisbertz SS, Goense L, et al. A propensity score matched analysis of open versus minimally invasive transthoracic esophagectomy in the Netherlands. Ann Surg, 2017, 266(5): 839-846.
6.	Chen C, Yu Z, Jin Q, et al. Clinical features and risk factors of anastomotic leakage after radical esophagectomy. Zhonghua Wai Ke Za Zhi, 2015, 53(7): 518-521.
7.	Fransen LFC, Berkelmans GHK, Asti E, et al. The effect of postoperative complications after minimally invasive esophagectomy on long-term survival: An international multicenter cohort study. Ann Surg, 2021, 274(6): e1129-e1137.
8.	张蕊, 郑黎强, 潘国伟. 疾病发病风险预测模型的应用与建立. 中国卫生统计, 2015, 32(4): 724-726.Zhang R, Zheng LQ, Pan GW. Application and establishment of disease risk prediction model. Chin J Health Stat, 2015, 32(4): 724-726.
9.	Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: The CHARMS checklist. PLoS Med, 2014, 11(10): e1001744.
10.	陈香萍, 张奕, 庄一渝, 等. PROBAST: 诊断或预后多因素预测模型研究偏倚风险的评估工具. 中国循证医学杂志, 2020, 20(6): 737-744.Chen XP, Zhang Y, Zhuang YY, et al. PROBAST: A tool for assessing risk of bias in the study of diagnostic or prognostic multi-factorial predictive models. Chin J Evid-Based Med, 2020, 20(6): 737-744.
11.	代磊, 任自学, 张安庆, 等. McKeown食管癌术后吻合口瘘的危险因素分析及预测模型建立. 中国胸心血管外科临床杂志, 2020, 27(12): 1436-1440.Dai L, Ren ZX, Zhang AQ, et al. Risk factors and prediction model of anastomotic leakage after McKeown esophagectom. Chin J Clin Thorac Cardiovasc Surg, 2020, 27(12): 1436-1440.
12.	庞鹏, 王辉, 席启, 等. McKeown微创食管癌根治术后颈部吻合口瘘风险的列线图预测模型的构建与验证. 医学综述, 2021, 27(9): 1857-1862.Pang P, Wang H, Xi Q, et al. Construction and validation of histogram prediction model for neck anastomotic fistula risk after McKeown minimally invasive radical resection of esophageal cancer. Med Recapitul, 2021, 27(9): 1857-1862.
13.	赵湘, 顾园园, 滕亚莉. 老年食管癌VATS术后颈部吻合口瘘有关影响因素的logistic回归分析与预测模型构建. 河北医学, 2021, 27(12): 2065-2070.Zhao X, Gu YY, Teng YL. Logistic regression analysis and prediction model construction of related influencing factors of cervical anastomotic leakage after VATS operation in elderly patients with esophageal cancer. Hebei Med J, 2021, 27(12): 2065-2070.
14.	卢晨, 宁光耀, 司盼盼, 等. 食管癌根治性切除术后吻合口瘘发生危险因素分析及预测模型的构建. 川北医学院学报, 2022, 37(8): 983-987.Lu C, Ning GY, Si PP, et al. Analysis of risk factors and construction of prediction model of occurrence of anastomotic leakage after radical resection of esophageal cancer. J North Sichuan Med Coll, 2022, 37(8): 983-987.
15.	李殿波, 李金龙, 于海防, 等. 食管癌患者三切口食管切除术后发生颈部吻合口瘘的列线图预测模型构建. 河北医药, 2022, 44(20): 3050-3054.Li DB, Li JL, Yu HF, et al. Construction of nomogram prediction model for post-operative jugular anastomotic fistula of tri-incisional esophagectomy in patients with esophageal cancer. Hebei Med J, 2022, 44(20): 3050-3054.
16.	许峰. 食管癌切除术后吻合口瘘风险预测模型的构建. 交通医学, 2022, 36(6): 619-621, 623.Xu F. Construction of a risk prediction model for anastomotic leakage after esophageal cancer resection. Med J Commun, 2022, 36(6): 619-621, 623.
17.	周瑾, 马红霞. 食管癌术后颈部吻合口瘘危险因素分析及预测模型的建立. 南京医科大学学报(自然科学版), 2023, 43(2): 268-274, 296.Zhou J, Ma HX. Analysis of risk factors and establishment of a prediction model for the cervical anastomotic leakage after esophagectomy. J Nanjing Med Univ (Nat Sci), 2023, 43(2): 268-274, 296.
18.	赵茹. 食管癌术后吻合口瘘风险的列线图预测模型构建. 安徽医科大学, 2022.Zhao R. Construction of a nomograph prediction model for anastomotic fistula risk after esophageal cancer. Anhui Medical University, 2022.
19.	聂洪鑫, 杨思豪, 刘洪刚, 等. 围术期食管癌术后食管胃吻合口瘘的危险因素及预测模型的建立. 中国胸心血管外科临床杂志, 2023, 30(4): 586-592.Nie HX, Yang SH, Liu HG, et al. Risk factors and prediction model of perioperative esophagogastric anastomotic leakage after esophageal cancer surgery. Chin J Clin Thorac Cardiovasc Surg, 2023, 30(4): 586-592.
20.	Yu WQ, Gao HJ, Shi GD, et al. Development and validation of a nomogram to predict anastomotic leakage after esophagectomy for esophageal carcinoma. J Thorac Dis, 2021, 13(6): 3549-3565.
21.	Sun ZW, Du H, Li JR, et al. Constructing a risk prediction model for anastomotic leakage after esophageal cancer resection. J Int Med Res, 2020, 48(4): 300060519896726.
22.	隋泽森. 通过危险因素对食管癌术后吻合口瘘的预测: Logistic回归模型与人工神经网络模型的建立及比较. 南方医科大学, 2019.Sui ZS. Using risk factors to predict anastomosis leakage after esophagectomy: A comparative study of logistic regression analysis and artificial neural network. Southern Medical University, 2019.
23.	Huang C, Yao H, Huang Q, et al. A novel nomogram to predict the risk of anastomotic leakage in patients after oesophagectomy. BMC Surg, 2020, 20(1): 64.
24.	Lindenmann J, Fink-Neuboeck N, Porubsky C, et al. A nomogram illustrating the probability of anastomotic leakage following cervical esophagogastrostomy. Surg Endosc, 2021, 35(11): 6123-6131.
25.	Zhao Z, Cheng X, Sun X, et al. Prediction model of anastomotic leakage among esophageal cancer patients after receiving an esophagectomy: Machine learning approach. JMIR Med Inform, 2021, 9(7): e27110.
26.	Noble F, Curtis N, Harris S, et al. Risk assessment using a novel score to predict anastomotic leak and major complications after oesophageal resection. J Gastrointest Surg, 2012, 16(6): 1083-1095.
27.	van Kooten RT, Bahadoer RR, Ter Buurkes de Vries B, et al. Conventional regression analysis and machine learning in prediction of anastomotic leakage and pulmonary complications after esophagogastric cancer surgery. J Surg Oncol, 2022, 126(3): 490-501.
28.	张磊, 李辉, 侯生才, 等. 基于累积评分的食管癌术后吻合口瘘的风险分级系统. 中华胸部外科电子杂志, 2016, 3(1): 15-20.Zhang L, Li H, Hou SC, et al. An aggregate score system to stratify the risk of anastomotic leakage after esophageal carcinoma surgery. Chin J Thorac Surg (Electron Ed), 2016, 3(1): 15-20.
29.	何贤英, 赵志, 温兴煊, 等. Logistic回归中连续型自变量离散化为二分类变量时适宜分界点的确定. 中国卫生统计, 2015, 32(2): 275-277, 280.He XY, Zhao Z, Wen XX, et al. The determination of appropriate boundary points when continuous independent variables are discretized into bicategorical variables in logistic regression. Chin J Health Statist, 2015, 32(2): 275-277, 280.
30.	许汝福. Logistic回归变量筛选及回归方法选择实例分析. 中国循证医学杂志, 2016, 16(11): 1360-1364.Xu RF. Selection for independent variables and regression method in logistic regression: An example analysis. Chin J Evid-Based Med, 2016, 16(11): 1360-1364.
31.	Wolff RF, Moons KGM, Riley RD, et al. PROBAST: A tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
32.	Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement. BMJ, 2015, 350: g7594.
33.	Fjederholt KT, Okholm C, Svendsen LB, et al. Ketorolac and other NSAIDs increase the risk of anastomotic leakage after surgery for GEJ cancers: A cohort study of 557 patients. J Gastrointest Surg, 2018, 22(4): 587-594.
34.	Schweigert M, Solymosi N, Dubecz A, et al. Current outcome of esophagectomy in the very elderly: Experience of a German high-volume center. Ame Surg, 2013, 79(8): 754-763.
35.	Lin X, Li J, Chen W, et al. Diabetes and risk of anastomotic leakage after gastrointestinal surgery. J Surg Res, 2015, 196(2): 294-301.
36.	Natalini J, Palit A, Sankineni A, et al. Diabetes mellitus is an independent risk for gastroesophageal reflux disease among urban African Americans. Dis Esophagus, 2015, 28(5): 405-411.
37.	van Rossum PSN, Haverkamp L, Verkooijen HM, et al. Calcification of arteries supplying the gastric tube: A new risk factor for anastomotic leakage after esophageal surgery. Radiology, 2015, 274(1): 124-132.
38.	Borggreve AS, Goense L, van Rossum PSN, et al. Generalized cardiovascular disease on a preoperative CT scan is predictive for anastomotic leakage after esophagectomy. Eur J Surg Oncol, 2018, 44(5): 587-593.
39.	Li SJ, Wang ZQ, Li YJ, et al. Diabetes mellitus and risk of anastomotic leakage after esophagectomy: A systematic review and meta-analysis. Dis Esophagus, 2017, 30(6): 1-12.
40.	Roh S, Iannettoni MD, Keech J, et al. Timing of esophagectomy after neoadjuvant chemoradiation therapy affects the incidence of anastomotic leaks. Korean J Thorac Cardiovasc Surg, 2019, 52(1): 1-8.
41.	Haddad A, Bashir A, Nimeri A. Gastrogastric fistula: An unusual cause for severe bile reflux following conversion of sleeve gastrectomy to one anastomosis gastric bypass. Obes Surg, 2018, 28(7): 2151-2153.
42.	Kassis ES, Kosinski AS, Ross P, et al. Predictors of anastomotic leak after esophagectomy: An analysis of the society of thoracic surgeons general thoracic database. Ann Thorac Surg, 2013, 96(6): 1919-1926.

1. Guinan EM, Dowds J, Donohoe C, et al. The physiotherapist and the esophageal cancer patient: From prehabilitation to rehabilitation. Dis Esophagus, 2017, 30(1): 1-12.
2. Zeng H, Zheng R, Guo Y, et al. Cancer survival in China, 2003-2005: A population-based study. Int J Cancer, 2015, 136(8): 1921-1930.
3. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2018, 68(6): 394-424.
4. 中国肿瘤学会指南工作委员会组织. 中国临床肿瘤学会(CSCO)食管癌诊疗指南-2019. 北京: 人民卫生出版社, 2019. 615-620.The Guidelines Working Committee of Chinese Society of Cancer. Chinese Society of Clinical Oncology (CSCO) guidelines for the diagnosis and treatment of esophageal cancer-2019. Beijing: People's Medical Publishing House, 2019. 615-620.
5. Seesing MFJ, Gisbertz SS, Goense L, et al. A propensity score matched analysis of open versus minimally invasive transthoracic esophagectomy in the Netherlands. Ann Surg, 2017, 266(5): 839-846.
6. Chen C, Yu Z, Jin Q, et al. Clinical features and risk factors of anastomotic leakage after radical esophagectomy. Zhonghua Wai Ke Za Zhi, 2015, 53(7): 518-521.
7. Fransen LFC, Berkelmans GHK, Asti E, et al. The effect of postoperative complications after minimally invasive esophagectomy on long-term survival: An international multicenter cohort study. Ann Surg, 2021, 274(6): e1129-e1137.
8. 张蕊, 郑黎强, 潘国伟. 疾病发病风险预测模型的应用与建立. 中国卫生统计, 2015, 32(4): 724-726.Zhang R, Zheng LQ, Pan GW. Application and establishment of disease risk prediction model. Chin J Health Stat, 2015, 32(4): 724-726.
9. Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: The CHARMS checklist. PLoS Med, 2014, 11(10): e1001744.
10. 陈香萍, 张奕, 庄一渝, 等. PROBAST: 诊断或预后多因素预测模型研究偏倚风险的评估工具. 中国循证医学杂志, 2020, 20(6): 737-744.Chen XP, Zhang Y, Zhuang YY, et al. PROBAST: A tool for assessing risk of bias in the study of diagnostic or prognostic multi-factorial predictive models. Chin J Evid-Based Med, 2020, 20(6): 737-744.
11. 代磊, 任自学, 张安庆, 等. McKeown食管癌术后吻合口瘘的危险因素分析及预测模型建立. 中国胸心血管外科临床杂志, 2020, 27(12): 1436-1440.Dai L, Ren ZX, Zhang AQ, et al. Risk factors and prediction model of anastomotic leakage after McKeown esophagectom. Chin J Clin Thorac Cardiovasc Surg, 2020, 27(12): 1436-1440.
12. 庞鹏, 王辉, 席启, 等. McKeown微创食管癌根治术后颈部吻合口瘘风险的列线图预测模型的构建与验证. 医学综述, 2021, 27(9): 1857-1862.Pang P, Wang H, Xi Q, et al. Construction and validation of histogram prediction model for neck anastomotic fistula risk after McKeown minimally invasive radical resection of esophageal cancer. Med Recapitul, 2021, 27(9): 1857-1862.
13. 赵湘, 顾园园, 滕亚莉. 老年食管癌VATS术后颈部吻合口瘘有关影响因素的logistic回归分析与预测模型构建. 河北医学, 2021, 27(12): 2065-2070.Zhao X, Gu YY, Teng YL. Logistic regression analysis and prediction model construction of related influencing factors of cervical anastomotic leakage after VATS operation in elderly patients with esophageal cancer. Hebei Med J, 2021, 27(12): 2065-2070.
14. 卢晨, 宁光耀, 司盼盼, 等. 食管癌根治性切除术后吻合口瘘发生危险因素分析及预测模型的构建. 川北医学院学报, 2022, 37(8): 983-987.Lu C, Ning GY, Si PP, et al. Analysis of risk factors and construction of prediction model of occurrence of anastomotic leakage after radical resection of esophageal cancer. J North Sichuan Med Coll, 2022, 37(8): 983-987.
15. 李殿波, 李金龙, 于海防, 等. 食管癌患者三切口食管切除术后发生颈部吻合口瘘的列线图预测模型构建. 河北医药, 2022, 44(20): 3050-3054.Li DB, Li JL, Yu HF, et al. Construction of nomogram prediction model for post-operative jugular anastomotic fistula of tri-incisional esophagectomy in patients with esophageal cancer. Hebei Med J, 2022, 44(20): 3050-3054.
16. 许峰. 食管癌切除术后吻合口瘘风险预测模型的构建. 交通医学, 2022, 36(6): 619-621, 623.Xu F. Construction of a risk prediction model for anastomotic leakage after esophageal cancer resection. Med J Commun, 2022, 36(6): 619-621, 623.
17. 周瑾, 马红霞. 食管癌术后颈部吻合口瘘危险因素分析及预测模型的建立. 南京医科大学学报(自然科学版), 2023, 43(2): 268-274, 296.Zhou J, Ma HX. Analysis of risk factors and establishment of a prediction model for the cervical anastomotic leakage after esophagectomy. J Nanjing Med Univ (Nat Sci), 2023, 43(2): 268-274, 296.
18. 赵茹. 食管癌术后吻合口瘘风险的列线图预测模型构建. 安徽医科大学, 2022.Zhao R. Construction of a nomograph prediction model for anastomotic fistula risk after esophageal cancer. Anhui Medical University, 2022.
19. 聂洪鑫, 杨思豪, 刘洪刚, 等. 围术期食管癌术后食管胃吻合口瘘的危险因素及预测模型的建立. 中国胸心血管外科临床杂志, 2023, 30(4): 586-592.Nie HX, Yang SH, Liu HG, et al. Risk factors and prediction model of perioperative esophagogastric anastomotic leakage after esophageal cancer surgery. Chin J Clin Thorac Cardiovasc Surg, 2023, 30(4): 586-592.
20. Yu WQ, Gao HJ, Shi GD, et al. Development and validation of a nomogram to predict anastomotic leakage after esophagectomy for esophageal carcinoma. J Thorac Dis, 2021, 13(6): 3549-3565.
21. Sun ZW, Du H, Li JR, et al. Constructing a risk prediction model for anastomotic leakage after esophageal cancer resection. J Int Med Res, 2020, 48(4): 300060519896726.
22. 隋泽森. 通过危险因素对食管癌术后吻合口瘘的预测: Logistic回归模型与人工神经网络模型的建立及比较. 南方医科大学, 2019.Sui ZS. Using risk factors to predict anastomosis leakage after esophagectomy: A comparative study of logistic regression analysis and artificial neural network. Southern Medical University, 2019.
23. Huang C, Yao H, Huang Q, et al. A novel nomogram to predict the risk of anastomotic leakage in patients after oesophagectomy. BMC Surg, 2020, 20(1): 64.
24. Lindenmann J, Fink-Neuboeck N, Porubsky C, et al. A nomogram illustrating the probability of anastomotic leakage following cervical esophagogastrostomy. Surg Endosc, 2021, 35(11): 6123-6131.
25. Zhao Z, Cheng X, Sun X, et al. Prediction model of anastomotic leakage among esophageal cancer patients after receiving an esophagectomy: Machine learning approach. JMIR Med Inform, 2021, 9(7): e27110.
26. Noble F, Curtis N, Harris S, et al. Risk assessment using a novel score to predict anastomotic leak and major complications after oesophageal resection. J Gastrointest Surg, 2012, 16(6): 1083-1095.
27. van Kooten RT, Bahadoer RR, Ter Buurkes de Vries B, et al. Conventional regression analysis and machine learning in prediction of anastomotic leakage and pulmonary complications after esophagogastric cancer surgery. J Surg Oncol, 2022, 126(3): 490-501.
28. 张磊, 李辉, 侯生才, 等. 基于累积评分的食管癌术后吻合口瘘的风险分级系统. 中华胸部外科电子杂志, 2016, 3(1): 15-20.Zhang L, Li H, Hou SC, et al. An aggregate score system to stratify the risk of anastomotic leakage after esophageal carcinoma surgery. Chin J Thorac Surg (Electron Ed), 2016, 3(1): 15-20.
29. 何贤英, 赵志, 温兴煊, 等. Logistic回归中连续型自变量离散化为二分类变量时适宜分界点的确定. 中国卫生统计, 2015, 32(2): 275-277, 280.He XY, Zhao Z, Wen XX, et al. The determination of appropriate boundary points when continuous independent variables are discretized into bicategorical variables in logistic regression. Chin J Health Statist, 2015, 32(2): 275-277, 280.
30. 许汝福. Logistic回归变量筛选及回归方法选择实例分析. 中国循证医学杂志, 2016, 16(11): 1360-1364.Xu RF. Selection for independent variables and regression method in logistic regression: An example analysis. Chin J Evid-Based Med, 2016, 16(11): 1360-1364.
31. Wolff RF, Moons KGM, Riley RD, et al. PROBAST: A tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
32. Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement. BMJ, 2015, 350: g7594.
33. Fjederholt KT, Okholm C, Svendsen LB, et al. Ketorolac and other NSAIDs increase the risk of anastomotic leakage after surgery for GEJ cancers: A cohort study of 557 patients. J Gastrointest Surg, 2018, 22(4): 587-594.
34. Schweigert M, Solymosi N, Dubecz A, et al. Current outcome of esophagectomy in the very elderly: Experience of a German high-volume center. Ame Surg, 2013, 79(8): 754-763.
35. Lin X, Li J, Chen W, et al. Diabetes and risk of anastomotic leakage after gastrointestinal surgery. J Surg Res, 2015, 196(2): 294-301.
36. Natalini J, Palit A, Sankineni A, et al. Diabetes mellitus is an independent risk for gastroesophageal reflux disease among urban African Americans. Dis Esophagus, 2015, 28(5): 405-411.
37. van Rossum PSN, Haverkamp L, Verkooijen HM, et al. Calcification of arteries supplying the gastric tube: A new risk factor for anastomotic leakage after esophageal surgery. Radiology, 2015, 274(1): 124-132.
38. Borggreve AS, Goense L, van Rossum PSN, et al. Generalized cardiovascular disease on a preoperative CT scan is predictive for anastomotic leakage after esophagectomy. Eur J Surg Oncol, 2018, 44(5): 587-593.
39. Li SJ, Wang ZQ, Li YJ, et al. Diabetes mellitus and risk of anastomotic leakage after esophagectomy: A systematic review and meta-analysis. Dis Esophagus, 2017, 30(6): 1-12.
40. Roh S, Iannettoni MD, Keech J, et al. Timing of esophagectomy after neoadjuvant chemoradiation therapy affects the incidence of anastomotic leaks. Korean J Thorac Cardiovasc Surg, 2019, 52(1): 1-8.
41. Haddad A, Bashir A, Nimeri A. Gastrogastric fistula: An unusual cause for severe bile reflux following conversion of sleeve gastrectomy to one anastomosis gastric bypass. Obes Surg, 2018, 28(7): 2151-2153.
42. Kassis ES, Kosinski AS, Ross P, et al. Predictors of anastomotic leak after esophagectomy: An analysis of the society of thoracic surgeons general thoracic database. Ann Thorac Surg, 2013, 96(6): 1919-1926.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Risk prediction model of anastomotic fistula after radical resection of esophageal cancer: A systematic review and meta-analysis

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