The risk prediction models for anastomotic leakage after esophagectomy: A systematic review and meta-analysis_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

SU Yushuang ¹ , LI Yan ¹ , GAO Hong ¹ , PU Zaichun ² , CHEN Juan ¹ , LIU Mengting ¹ , HE Yaxie ³ ,  YANG Qin ¹ ,  HE Bin ¹

1. Sichuan Academy of Medical Science·Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, P. R. China;
2. Xindu District People’s Hospital of Chengdu, Chengdu, 610500, P. R. China;
3. School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China;

Corresponding author：

YANG Qin, Email: 20152160@qq.com; HE Bin, Email: sy99426@sina.com

Keywords：

Esophageal cancer; anastomotic leakage; prediction model; systematic review/meta-analysis

DOI：

10.7507/1007-4848.202310031

Video：

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Objective To systematically evaluate the risk prediction models for anastomotic leakage (AL) in patients with esophageal cancer after surgery. Methods A computer-based search of PubMed, EMbase, Web of Science, Cochrane Library, Chinese Medical Journal Full-text Database, VIP, Wanfang and CNKI was conducted to collect studies on postoperative AL risk prediction model for esophageal cancer from their inception to October 1st, 2023. PROBAST tool was employed to evaluate the bias risk and applicability of the model, and Stata 15 software was utilized for meta-analysis. Results A total of 19 literatures were included covering 25 AL risk prediction models and 7373 patients. The area under the receiver operating characteristic curve (AUC) was 0.67-0.960. Among them, 23 prediction models had a good prediction performance (AUC>0.7); 13 models were tested for calibration of the model; 1 model was externally validated, and 10 models were internally validated. Meta-analysis showed that hypoproteinemia (OR=9.362), postoperative pulmonary complications (OR=7.427), poor incision healing (OR=5.330), anastomosis type (OR=2.965), preoperative history of thoracoabdominal surgery (OR=3.181), preoperative diabetes mellitus (OR=2.445), preoperative cardiovascular disease (OR=3.260), preoperative neoadjuvant (OR=2.977), preoperative respiratory disease (OR=4.744), surgery method (OR=4.312), American Society of Anesthesiologists score (OR=2.424) were predictors for AL after esophageal cancer surgery. Conclusion At present, the prediction model of AL risk in patients with esophageal cancer after surgery is in the development stage, and the overall research quality needs to be improved.

1.	季刚, 卫江鹏, 卢强. 2022年V3版《NCCN食管和食管胃结合部癌临床实践指南》更新解读. 中国胸心血管外科临床杂志, 2022, 29(11): 1414-1423.
2.	Verstegen MHP, Bouwense SAW, van Workum F, et al. Management of intrathoracic and cervical anastomotic leakage after esophagectomy for esophageal cancer: A systematic review. World J Emerg Surg, 2019, 14: 17.
3.	Tachezy M, Chon SH, Rieck I, et al. Endoscopic vacuum therapy versus stent treatment of esophageal anastomotic leaks (ESOLEAK): Study protocol for a prospective randomized phase 2 trial. Trials, 2021, 22(1): 377.
4.	Paul G, Bohle W, Zoller W. Risk factors for the development of esophagorespiratory fistula in esophageal cancer. J Gastrointestin Liver Dis, 2019, 28(3): 265-270.
5.	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.
6.	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.
7.	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.
8.	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.
9.	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.
10.	陈子瞻. 通过对logistic回归模型和人工神经网络的比较预测食管胃交界部肿瘤术后吻合口瘘的发生. 南方医科大学, 2021.
11.	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.
12.	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.
13.	于文泉. 预测食管癌切除术后吻合口瘘列线图的建立与验证. 青岛大学, 2021.
14.	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.
15.	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.
16.	Xu Y, Cui H, Dong T, et al. Integrating clinical data and attentional CT imaging features for esophageal fistula prediction in esophageal cancer. Front Oncol, 2021, 11: 688706.
17.	Xu Y, Cui H, Fan B, et al. Integrative model of CT imaging and clinical features using attentional multi-view convolutional neural network (AM-CNN) for prediction of esophageal fistula in esophageal cancer. Int J Radiat Oncol Biol Phys, 2020, 108(3): E637-E638.
18.	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.
19.	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.
20.	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.
21.	Moon SW, Kim JJ, Cho DG, et al. Early detection of complications: Anastomotic leakage. J Thorac Dis, 2019, 11(Suppl 5): S805-S811.
22.	Low DE, Alderson D, Cecconello I, et al. International consensus on standardization of data collection for complications associated with esophagectomy: Esophagectomy Complications Consensus Group (ECCG). Ann Surg, 2015, 262(2): 286-294.
23.	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.
24.	Moons KGM, Wolff RF, Riley RD, et al. PROBAST: A tool to assess risk of bias and applicability of prediction model studies: Explanation and elaboration. Ann Intern Med, 2019, 170(1): W1-W33.
25.	白成云, 李忠诚, 李文军, 等. 食管癌切除术后发生吻合口瘘风险的列线图建立和验证. 肿瘤代谢与营养电子杂志, 2023, 10(3): 401-407.
26.	陈建清. 微创McKeown食管癌根治术前患者吻合口瘘临床预测模型的构建. 福建医科大学, 2021.
27.	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.
28.	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.
29.	Su P, Huang C, Lv H, et al. Prediction model using risk factors associated with anastomotic leakage after minimally invasive esophagectomy. Pak J Med Sci, 2023, 39(5): 1345-1349.
30.	Chiu CY, Wong KS, Tsai MH. Massive aspiration of barium sulfate during an upper gastrointestinal examination in a child with dysphagia. Int J Pediatr Otorhinolaryngol, 2005, 69(4): 541-544.
31.	谷鸿秋, 王俊峰, 章仲恒, 等. 临床预测模型: 模型的建立. 中国循证心血管医学杂志, 2019, 11(1): 14-16, 23.
32.	Ziegler A. Clinical prediction models: A practical approach to development, validation, and updating. Biometric J, 2020, 62(4): 1122-1123.
33.	Ranalli MG, Salvati N, Petrella L, et al. M-quantile regression shrinkage and selection via the lasso and elastic net to assess the effect of meteorology and traffic on air quality. Biom J, 2023, 65(8): e2100355.
34.	Zhang Q, Yuan KH, Wang L. Asymptotic bias of normal-distribution-based maximum likelihood estimates of moderation effects with data missing at random. Br J Math Stat Psychol, 2019, 72(2): 334-354.
35.	Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: Seven steps for development and an ABCD for validation. Eur Heart J, 2014, 35(29): 1925-1931.
36.	Steyerberg EW, Harrell FE. Prediction models need appropriate internal, internal-external, and external validation. J Clin Epidemiol, 2016, 69: 245-247.
37.	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.
38.	钟东晨. 食管癌切除术后吻合口瘘的危险因素分析. 汕头大学, 2022.
39.	Friedrich JO, Adhikari NK, Beyene J. Ratio of means for analyzing continuous outcomes in meta-analysis performed as well as mean difference methods. J Clin Epidemiol, 2011, 64(5): 556-564.
40.	Riley RD, Ensor J, Snell KIE, et al. Calculating the sample size required for developing a clinical prediction model. BMJ, 2020, 368: m441.

1. 季刚, 卫江鹏, 卢强. 2022年V3版《NCCN食管和食管胃结合部癌临床实践指南》更新解读. 中国胸心血管外科临床杂志, 2022, 29(11): 1414-1423.
2. Verstegen MHP, Bouwense SAW, van Workum F, et al. Management of intrathoracic and cervical anastomotic leakage after esophagectomy for esophageal cancer: A systematic review. World J Emerg Surg, 2019, 14: 17.
3. Tachezy M, Chon SH, Rieck I, et al. Endoscopic vacuum therapy versus stent treatment of esophageal anastomotic leaks (ESOLEAK): Study protocol for a prospective randomized phase 2 trial. Trials, 2021, 22(1): 377.
4. Paul G, Bohle W, Zoller W. Risk factors for the development of esophagorespiratory fistula in esophageal cancer. J Gastrointestin Liver Dis, 2019, 28(3): 265-270.
5. 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.
6. 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.
7. 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.
8. 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.
9. 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.
10. 陈子瞻. 通过对logistic回归模型和人工神经网络的比较预测食管胃交界部肿瘤术后吻合口瘘的发生. 南方医科大学, 2021.
11. 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.
12. 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.
13. 于文泉. 预测食管癌切除术后吻合口瘘列线图的建立与验证. 青岛大学, 2021.
14. 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.
15. 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.
16. Xu Y, Cui H, Dong T, et al. Integrating clinical data and attentional CT imaging features for esophageal fistula prediction in esophageal cancer. Front Oncol, 2021, 11: 688706.
17. Xu Y, Cui H, Fan B, et al. Integrative model of CT imaging and clinical features using attentional multi-view convolutional neural network (AM-CNN) for prediction of esophageal fistula in esophageal cancer. Int J Radiat Oncol Biol Phys, 2020, 108(3): E637-E638.
18. 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.
19. 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.
20. 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.
21. Moon SW, Kim JJ, Cho DG, et al. Early detection of complications: Anastomotic leakage. J Thorac Dis, 2019, 11(Suppl 5): S805-S811.
22. Low DE, Alderson D, Cecconello I, et al. International consensus on standardization of data collection for complications associated with esophagectomy: Esophagectomy Complications Consensus Group (ECCG). Ann Surg, 2015, 262(2): 286-294.
23. 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.
24. Moons KGM, Wolff RF, Riley RD, et al. PROBAST: A tool to assess risk of bias and applicability of prediction model studies: Explanation and elaboration. Ann Intern Med, 2019, 170(1): W1-W33.
25. 白成云, 李忠诚, 李文军, 等. 食管癌切除术后发生吻合口瘘风险的列线图建立和验证. 肿瘤代谢与营养电子杂志, 2023, 10(3): 401-407.
26. 陈建清. 微创McKeown食管癌根治术前患者吻合口瘘临床预测模型的构建. 福建医科大学, 2021.
27. 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.
28. 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.
29. Su P, Huang C, Lv H, et al. Prediction model using risk factors associated with anastomotic leakage after minimally invasive esophagectomy. Pak J Med Sci, 2023, 39(5): 1345-1349.
30. Chiu CY, Wong KS, Tsai MH. Massive aspiration of barium sulfate during an upper gastrointestinal examination in a child with dysphagia. Int J Pediatr Otorhinolaryngol, 2005, 69(4): 541-544.
31. 谷鸿秋, 王俊峰, 章仲恒, 等. 临床预测模型: 模型的建立. 中国循证心血管医学杂志, 2019, 11(1): 14-16, 23.
32. Ziegler A. Clinical prediction models: A practical approach to development, validation, and updating. Biometric J, 2020, 62(4): 1122-1123.
33. Ranalli MG, Salvati N, Petrella L, et al. M-quantile regression shrinkage and selection via the lasso and elastic net to assess the effect of meteorology and traffic on air quality. Biom J, 2023, 65(8): e2100355.
34. Zhang Q, Yuan KH, Wang L. Asymptotic bias of normal-distribution-based maximum likelihood estimates of moderation effects with data missing at random. Br J Math Stat Psychol, 2019, 72(2): 334-354.
35. Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: Seven steps for development and an ABCD for validation. Eur Heart J, 2014, 35(29): 1925-1931.
36. Steyerberg EW, Harrell FE. Prediction models need appropriate internal, internal-external, and external validation. J Clin Epidemiol, 2016, 69: 245-247.
37. 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.
38. 钟东晨. 食管癌切除术后吻合口瘘的危险因素分析. 汕头大学, 2022.
39. Friedrich JO, Adhikari NK, Beyene J. Ratio of means for analyzing continuous outcomes in meta-analysis performed as well as mean difference methods. J Clin Epidemiol, 2011, 64(5): 556-564.
40. Riley RD, Ensor J, Snell KIE, et al. Calculating the sample size required for developing a clinical prediction model. BMJ, 2020, 368: m441.

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