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 ,  HE Bin ¹ ,  YANG Qin 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：

HE Bin, Email: sy99426@sina.com; YANG Qin, Email: 20152160@qq.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, SinoMed 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 0.670-0.960. 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 therapy (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.

Citation： SU Yushuang, LI Yan, GAO Hong, PU Zaichun, CHEN Juan, LIU Mengting, HE Yaxie, HE Bin, YANG Qin. The risk prediction models for anastomotic leakage after esophagectomy: A systematic review and meta-analysis. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2025, 32(2): 230-236. doi: 10.7507/1007-4848.202310031 Copy

1.	季剛, 衛江鵬, 盧強. 2022年V3版《NCCN食管和食管胃結合部癌臨床實踐指南》更新解讀. 中國胸心血管外科臨床雜志, 2022, 29(11): 1414-1423.Ji G, Wei JP, Lu Q. Interpretation of updates in the 2022 V3 edition of the NCCN clinical practice guidelines for esophageal and esophagogastric junction cancers. Chin J Clin Thorac Cardiovasc Surg, 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.	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 factors influencing cervical anastomotic leak after VATS in elderly patients with esophageal cancer. Hebei Med J, 2021, 27(12): 2065-2070.
6.	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 a predictive model for anastomotic leak after radical esophagectomy. J North Sichuan Med Coll, 2022, 37(8): 983-987.
7.	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 a nomogram prediction model for cervical anastomotic leak after three-field esophagectomy in patients with esophageal cancer. Hebei Med, 2022, 44(20): 3050-3054.
8.	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 leak after esophagectomy. Traff Med, 2022, 36(6): 619-621, 623.
9.	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 development of a predictive model for cervical anastomotic leak after esophageal cancer surgery. J Nanjing Med Univ (Nat Sci Ed), 2023, 43(2): 268-274, 296.
10.	陳子瞻. 通過對logistic回歸模型和人工神經網絡的比較預測食管胃交界部腫瘤術后吻合口瘺的發生. 南方醫科大學, 2021.Chen ZZ. Prediction of anastomotic leak after surgery for tumors of the esophagogastric junction: A comparative study of logistic regression models and artificial neural networks. Southern Medical University, 2021.
11.	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. Prediction of anastomotic leak after esophageal cancer surgery based on risk factors: Development and comparison of logistic regression and artificial neural network models. Southern Medical University, 2019.
12.	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 development of a predictive model for esophagogastric anastomotic leak after esophageal cancer surgery during the perioperative period. Chin J Clin Thorac Cardiovasc Surg, 2023, 30(4): 586-592.
13.	于文泉. 預測食管癌切除術后吻合口瘺列線圖的建立與驗證. 青島大學, 2021.Yu WQ. Development and validation of a nomogram for predicting anastomotic leak after esophagectomy. Qingdao University, 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. Analysis of risk factors and development of a predictive model for anastomotic leak after mckeown esophagectomy. Chin J Clin Thorac Cardiovasc Surg, 2020, 27(12): 1436-1440.
19.	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 a nomogram prediction model for the risk of cervical anastomotic leak after mckeown minimally invasive radical esophagectomy. Med Rev, 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, 李忠誠, 李文軍, 等. 食管癌切除術后發生吻合口瘺風險的列線圖建立和驗證. 腫瘤代謝與營養電子雜志, 2023, 10(3): 401-407.Bai CY, Li ZC, Li WJ, et al. Development and validation of a nomogram for predicting the risk of anastomotic leak after esophagectomy. Electron J Metab Nutr Oncol, 2023, 10(3): 401-407.
26.	陳建清. 微創McKeown食管癌根治術前患者吻合口瘺臨床預測模型的構建. 福建醫科大學, 2021.Chen JQ. Development of a clinical prediction model for anastomotic leak in patients undergoing minimally invasive mckeown esophagectomy. Fujian Medical University, 2021.
27.	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 nomogram prediction model for the risk of anastomotic leak after esophageal cancer surgery. Anhui Medical University, 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.	谷鴻秋, 王俊峰, 章仲恒, 等. 臨床預測模型: 模型的建立. 中國循證心血管醫學雜志, 2019, 11(1): 14-16, 23.Gu HQ, Wang JF, Zhang ZH, et al. Clinical prediction models: Development of models. Chin J Evid-Based Cardiovasc Med, 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.	鐘東晨. 食管癌切除術后吻合口瘺的危險因素分析. 汕頭大學, 2022.Zhong DC. Analysis of risk factors for anastomotic leak after esophagectomy. Shantou University, 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. 季剛, 衛江鵬, 盧強. 2022年V3版《NCCN食管和食管胃結合部癌臨床實踐指南》更新解讀. 中國胸心血管外科臨床雜志, 2022, 29(11): 1414-1423.Ji G, Wei JP, Lu Q. Interpretation of updates in the 2022 V3 edition of the NCCN clinical practice guidelines for esophageal and esophagogastric junction cancers. Chin J Clin Thorac Cardiovasc Surg, 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. 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 factors influencing cervical anastomotic leak after VATS in elderly patients with esophageal cancer. Hebei Med J, 2021, 27(12): 2065-2070.
6. 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 a predictive model for anastomotic leak after radical esophagectomy. J North Sichuan Med Coll, 2022, 37(8): 983-987.
7. 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 a nomogram prediction model for cervical anastomotic leak after three-field esophagectomy in patients with esophageal cancer. Hebei Med, 2022, 44(20): 3050-3054.
8. 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 leak after esophagectomy. Traff Med, 2022, 36(6): 619-621, 623.
9. 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 development of a predictive model for cervical anastomotic leak after esophageal cancer surgery. J Nanjing Med Univ (Nat Sci Ed), 2023, 43(2): 268-274, 296.
10. 陳子瞻. 通過對logistic回歸模型和人工神經網絡的比較預測食管胃交界部腫瘤術后吻合口瘺的發生. 南方醫科大學, 2021.Chen ZZ. Prediction of anastomotic leak after surgery for tumors of the esophagogastric junction: A comparative study of logistic regression models and artificial neural networks. Southern Medical University, 2021.
11. 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. Prediction of anastomotic leak after esophageal cancer surgery based on risk factors: Development and comparison of logistic regression and artificial neural network models. Southern Medical University, 2019.
12. 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 development of a predictive model for esophagogastric anastomotic leak after esophageal cancer surgery during the perioperative period. Chin J Clin Thorac Cardiovasc Surg, 2023, 30(4): 586-592.
13. 于文泉. 預測食管癌切除術后吻合口瘺列線圖的建立與驗證. 青島大學, 2021.Yu WQ. Development and validation of a nomogram for predicting anastomotic leak after esophagectomy. Qingdao University, 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. Analysis of risk factors and development of a predictive model for anastomotic leak after mckeown esophagectomy. Chin J Clin Thorac Cardiovasc Surg, 2020, 27(12): 1436-1440.
19. 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 a nomogram prediction model for the risk of cervical anastomotic leak after mckeown minimally invasive radical esophagectomy. Med Rev, 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, 李忠誠, 李文軍, 等. 食管癌切除術后發生吻合口瘺風險的列線圖建立和驗證. 腫瘤代謝與營養電子雜志, 2023, 10(3): 401-407.Bai CY, Li ZC, Li WJ, et al. Development and validation of a nomogram for predicting the risk of anastomotic leak after esophagectomy. Electron J Metab Nutr Oncol, 2023, 10(3): 401-407.
26. 陳建清. 微創McKeown食管癌根治術前患者吻合口瘺臨床預測模型的構建. 福建醫科大學, 2021.Chen JQ. Development of a clinical prediction model for anastomotic leak in patients undergoing minimally invasive mckeown esophagectomy. Fujian Medical University, 2021.
27. 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 nomogram prediction model for the risk of anastomotic leak after esophageal cancer surgery. Anhui Medical University, 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. 谷鴻秋, 王俊峰, 章仲恒, 等. 臨床預測模型: 模型的建立. 中國循證心血管醫學雜志, 2019, 11(1): 14-16, 23.Gu HQ, Wang JF, Zhang ZH, et al. Clinical prediction models: Development of models. Chin J Evid-Based Cardiovasc Med, 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. 鐘東晨. 食管癌切除術后吻合口瘺的危險因素分析. 汕頭大學, 2022.Zhong DC. Analysis of risk factors for anastomotic leak after esophagectomy. Shantou University, 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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The risk prediction models for anastomotic leakage after esophagectomy: A systematic review and meta-analysis

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