Predictive model for the risk of postoperative lung infection in esophageal cancer patients: A systematic review and meta-analysis
- VernacularTitle:食管癌术后肺部感染风险预测模型的系统评价与Meta分析
- Author:
Yuqi LIU
1
,
2
;
Ying ZHOU
1
;
Ziyue SONG
1
,
2
;
Linmei FENG
1
;
Wenjing TU
1
,
2
;
Qian WANG
1
,
2
Author Information
1. Operating Room, The Second Affiliated Hospital of Nanchang University, Nanchang, 330008, P. R. China
2. School of Nursing, Nanchang University, Nanchang, 330006, P. R. China
- Publication Type:Journal Article
- Keywords:
Esophageal cancer;
postoperative lung infection;
prediction model;
systematic evaluation/meta-analysis
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2026;33(08):1290-1298
- CountryChina
- Language:Chinese
-
Abstract:
Objective To systematically evaluate the risk prediction models for postoperative pulmonary infection in patients with esophageal cancer, providing an objective basis for clinical selection and optimization of models. Methods A systematic search was conducted in Chinese and English databases such as VIP, Wanfang, CNKI, CBM, PubMed, Cochrane Library, Embase, and Web of Science for studies related to the risk prediction models of postoperative pulmonary infection in patients with esophageal cancer from the inception to September 30, 2024. The PROBAST tool was used to assess the quality of prognostic model research, and the RevMan 5.4 software was used for meta-analysis of predictive factors. Results A total of 17 articles were included, containing 26 pulmonary infection risk prediction models. The area under the receiver operating characteristic curve (AUC) ranged from 0.627 to 0.942, among which 21 models had good predictive performance (AUC>0.7). Quality assessment through the PROBAST tool revealed that all 17 articles had a high risk of bias. Meta-analysis results showed that common predictive factors for postoperative pulmonary infection in esophageal cancer included smoking history (OR=1.97), smoking index ≥200 (cigarettes-years) (OR=4.38), smoking index ≥400 (cigarettes-years) (OR=2.00), age (OR=1.27), comorbid diabetes (OR=2.13), comorbid emphysema or chronic obstructive pulmonary disease (OR=1.55), low plasma albumin levels (OR=1.17), prognostic nutritional index (OR=4.45), history of related lung diseases (OR=2.10), tumor location (OR=2.32), surgical approach (OR=2.21), operation time (OR=1.73), preoperative serum procalcitonin levels (OR=3.06), anastomotic leakage (OR=3.39), reduced forced expiratory volume in the first second/forced vital capacity ratio (OR=0.86), and hoarseness (OR=2.23). Conclusion At present, the risk prediction models for postoperative pulmonary infection in esophageal cancer are still in the stage of continuous development and optimization, and their research quality needs to be further improved. Future research can refer to the predictive factors summarized in this study based on meta-analysis, combined with clinical practice, to select appropriate methods to construct and validate the risk prediction models for postoperative pulmonary infection in esophageal cancer, thus providing early targeted preventive strategies for high-risk patients.
