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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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.	中國腫瘤學會指南工作委員會組織. 中國臨床腫瘤學會(CSCO)食管癌診療指南-2019. 北京: 人民衛生出版社, 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.	張蕊, 鄭黎強, 潘國偉. 疾病發病風險預測模型的應用與建立. 中國衛生統計, 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.	陳香萍, 張奕, 莊一渝, 等. PROBAST: 診斷或預后多因素預測模型研究偏倚風險的評估工具. 中國循證醫學雜志, 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, 任自學, 張安慶, 等. McKeown食管癌術后吻合口瘺的危險因素分析及預測模型建立. 中國胸心血管外科臨床雜志, 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.	龐鵬, 王輝, 席啟, 等. McKeown微創食管癌根治術后頸部吻合口瘺風險的列線圖預測模型的構建與驗證. 醫學綜述, 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.	趙湘, 顧園園, 滕亞莉. 老年食管癌VATS術后頸部吻合口瘺有關影響因素的logistic回歸分析與預測模型構建. 河北醫學, 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.	盧晨, 寧光耀, 司盼盼, 等. 食管癌根治性切除術后吻合口瘺發生危險因素分析及預測模型的構建. 川北醫學院學報, 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, 李金龍, 于海防, 等. 食管癌患者三切口食管切除術后發生頸部吻合口瘺的列線圖預測模型構建. 河北醫藥, 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.	許峰. 食管癌切除術后吻合口瘺風險預測模型的構建. 交通醫學, 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, 馬紅霞. 食管癌術后頸部吻合口瘺危險因素分析及預測模型的建立. 南京醫科大學學報(自然科學版), 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.	趙茹. 食管癌術后吻合口瘺風險的列線圖預測模型構建. 安徽醫科大學, 2022.Zhao R. Construction of a nomograph prediction model for anastomotic fistula risk after esophageal cancer. Anhui Medical University, 2022.
19.	聶洪鑫, 楊思豪, 劉洪剛, 等. 圍術期食管癌術后食管胃吻合口瘺的危險因素及預測模型的建立. 中國胸心血管外科臨床雜志, 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.	隋澤森. 通過危險因素對食管癌術后吻合口瘺的預測: Logistic回歸模型與人工神經網絡模型的建立及比較. 南方醫科大學, 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.	張磊, 李輝, 侯生才, 等. 基于累積評分的食管癌術后吻合口瘺的風險分級系統. 中華胸部外科電子雜志, 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.	何賢英, 趙志, 溫興煊, 等. Logistic回歸中連續型自變量離散化為二分類變量時適宜分界點的確定. 中國衛生統計, 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.	許汝福. Logistic回歸變量篩選及回歸方法選擇實例分析. 中國循證醫學雜志, 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.
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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. 中國腫瘤學會指南工作委員會組織. 中國臨床腫瘤學會(CSCO)食管癌診療指南-2019. 北京: 人民衛生出版社, 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. 張蕊, 鄭黎強, 潘國偉. 疾病發病風險預測模型的應用與建立. 中國衛生統計, 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. 陳香萍, 張奕, 莊一渝, 等. PROBAST: 診斷或預后多因素預測模型研究偏倚風險的評估工具. 中國循證醫學雜志, 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, 任自學, 張安慶, 等. McKeown食管癌術后吻合口瘺的危險因素分析及預測模型建立. 中國胸心血管外科臨床雜志, 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. 龐鵬, 王輝, 席啟, 等. McKeown微創食管癌根治術后頸部吻合口瘺風險的列線圖預測模型的構建與驗證. 醫學綜述, 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. 趙湘, 顧園園, 滕亞莉. 老年食管癌VATS術后頸部吻合口瘺有關影響因素的logistic回歸分析與預測模型構建. 河北醫學, 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. 盧晨, 寧光耀, 司盼盼, 等. 食管癌根治性切除術后吻合口瘺發生危險因素分析及預測模型的構建. 川北醫學院學報, 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, 李金龍, 于海防, 等. 食管癌患者三切口食管切除術后發生頸部吻合口瘺的列線圖預測模型構建. 河北醫藥, 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. 許峰. 食管癌切除術后吻合口瘺風險預測模型的構建. 交通醫學, 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, 馬紅霞. 食管癌術后頸部吻合口瘺危險因素分析及預測模型的建立. 南京醫科大學學報(自然科學版), 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. 趙茹. 食管癌術后吻合口瘺風險的列線圖預測模型構建. 安徽醫科大學, 2022.Zhao R. Construction of a nomograph prediction model for anastomotic fistula risk after esophageal cancer. Anhui Medical University, 2022.
19. 聶洪鑫, 楊思豪, 劉洪剛, 等. 圍術期食管癌術后食管胃吻合口瘺的危險因素及預測模型的建立. 中國胸心血管外科臨床雜志, 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. 隋澤森. 通過危險因素對食管癌術后吻合口瘺的預測: Logistic回歸模型與人工神經網絡模型的建立及比較. 南方醫科大學, 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. 張磊, 李輝, 侯生才, 等. 基于累積評分的食管癌術后吻合口瘺的風險分級系統. 中華胸部外科電子雜志, 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. 何賢英, 趙志, 溫興煊, 等. Logistic回歸中連續型自變量離散化為二分類變量時適宜分界點的確定. 中國衛生統計, 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. 許汝福. Logistic回歸變量篩選及回歸方法選擇實例分析. 中國循證醫學雜志, 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.
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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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