The risk prediction models for anastomotic leakage after esophagectomy: A systematic review and meta-analysis
- VernacularTitle:食管癌术后吻合口瘘风险预测模型的系统评价与Meta分析
- Author:
Yushuang SU
1
,
2
;
Yan LI
1
,
2
;
Hong GAO
1
,
2
;
Zaichun PU
3
;
Juan CHEN
1
,
2
;
Mengting LIU
1
,
2
;
Yaxie HE
4
;
Bin HE
1
,
2
;
Qin YANG
1
,
2
Author Information
1. Sichuan Academy of Medical Science·
2. Sichuan Provincial People s Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, P. R. China
3. Xindu District People s Hospital of Chengdu, Chengdu, 610500, P. R. China
4. School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Publication Type:Journal Article
- Keywords:
Esophageal cancer;
anastomotic leakage;
prediction model;
systematic review/meta-analysis
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2025;32(02):230-236
- CountryChina
- Language:Chinese
-
Abstract:
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.
