Predictive model for the risk of postoperative lung infection in esophageal cancer patients: A systematic review and meta-analysis_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

LIU Yuqi ^1,2 ,  ZHOU Ying ¹ , SONG Ziyue ^1,2 , FENG Linmei ¹ , TU Wenjing ^1,2 , WANG Qian ^1,2

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;

Corresponding?author：

ZHOU Ying, Email: 1322915681@qq.com

Keywords：

Esophageal cancer; postoperative lung infection; prediction model; systematic evaluation/meta-analysis

DOI：

10.7507/1007-4848.202503021

Video：

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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.

Citation： LIU Yuqi, ZHOU Ying, SONG Ziyue, FENG Linmei, TU Wenjing, WANG Qian. Predictive model for the risk of postoperative lung infection in esophageal cancer patients: A systematic review and meta-analysis. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2026, 33(8): 1290-1298. doi: 10.7507/1007-4848.202503021 Copy

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2.	Zhu H, Ma X, Ye T, et al. Esophageal cancer in China: practice and research in the new era[J]. Int J Cancer, 2023, 152(9): 1741-1751.
3.	Tukanova KH, Chidambaram S, Guidozzi N, et al. Physiotherapy regimens in esophagectomy and gastrectomy: a systematic review and meta-analysis[J]. Ann Surg Oncol, 2022, 29(5): 3148-3167.
4.	Xie SH, Santoni G, M?lberg K, et al. Prediction model of long-term survival after esophageal cancer surgery[J]. Ann Surg, 2021, 273(5): 933-939.
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7.	楊惠玲, 戴美云, 袁園. 食管癌放化療患者并發下呼吸道感染的多因素Logistic回歸分析及與臨床預后的關系探究[J]. 中國急救復蘇與災害醫學雜志, 2023, 18(8): 1078-1082.Yang HL, Dai MY, Yuan Y. Multivariate Logistic regression analysis of the factors and clinical prognosis in patients with esophageal cancer undergoing radiotherapy and chemotherapy with lower respiratory tract infection[J]. China J Emerg Resusc Disaster Med, 2023, 18(8): 1078-1082.
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14.	呂明闖, 曹睿, 丁旭青, 等. 食管癌術后并發肺部感染危險因素和預測模型構建[J]. 中華醫院感染學雜志, 2024, 34(10): 1507-1511.Lyu MC, Cao R, Ding XQ, et al. Risk factors for pulmonary infection in patients esophageal cancer and establishment of prediction model[J]. Chin J Nosocomiol, 2024, 34(10): 1507-1511.
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17.	盧偉, 馬西淼, 李禮, 等. 食管癌術后輔助化療期間肺部感染的病原學特征及風險預測模型構建[J]. 熱帶醫學雜志, 2022, 22(12): 1701-1705.Lu W, Ma XM, Li L, et al. Etiological characteristics of lung infection during adjuvant chemotherapy after esophageal cancer surgery and the construction of risk prediction model[J]. J Trop Med, 2022, 22(12): 1701-1705.
18.	時佩君, 延佩, 菅利華. 基于Lasso-Nomogram構建食管癌術后感染的風險預測模型[J]. 廣西醫科大學學報, 2023, 40(11): 1850-1856.Shi PJ, Yan P, Jian LH. Construction of a Lasso-Nomogram risk prediction model for postoperative infection of esophageal cancer[J]. J Guangxi Med Univ, 2023, 40(11): 1850-1856.
19.	牛玲娟, 王玲玲, 呂東霞, 等. 食管癌術后肺部感染病原學及風險預測模型的構建[J]. 中華醫院感染學雜志, 2023, 33(10): 1504-1507.Niu LJ, Wang LL, Lyu DX, et al. Etiological characteristics of postoperative pulmonary infection in esophageal cancer patients and establishment of risk prediction model[J]. Chin J Nosocomiol, 2023, 33(10): 1504-1507.
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21.	Jin D, Yuan L, Li F, et al. A novel nomogram predicting the risk of postoperative pneumonia for esophageal cancer patients after minimally invasive esophagectomy[J]. Surg Endosc, 2022, 36(11): 8144-8153.
22.	Moons Kg, De Groot Ja, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist[J/OL]. PLoS Med, 2014, 11(10): e1001744. DOI: 10.1371/journal.pmed.1001744.
23.	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[J]. Ann Intern Med, 2019, 170(1): W1-W33.
24.	Gardner Al, Charlesworth M. How to write a retrospective observational study[J]. Anaesthesia, 2023, 78(4): 521-525.
25.	He A, Wang Z, Wu X, et al. Incidence of post-stroke cognitive impairment in patients with first-ever ischemic stroke: a multicenter cross-sectional study in China[J]. Lancet Reg Health West Pac, 2023, 33: 100687.
26.	許汝福. Logistic回歸變量篩選及回歸方法選擇實例分析[J]. 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[J]. Chin J Evid-Based Med, 2016, 16(11): 1360-1364.
27.	Ranalli MG, Salvat 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[J/OL]. Biom J, 2023, 65(8): e2100355. DOI: 10.1002/bimj.202100355.
28.	周航宇, 馮國雙. 醫學論文中logistic回歸分析常見邏輯錯誤與案例分析[J]. 中華全科醫師雜志, 2020, 19(12): 1194-1196.Zhou HY, Feng GS. Analysis of common mistakes in logistic regression analysis in medical papers[J]. Chin J Gen Pract, 2020, 19(12): 1194-1196.
29.	黎劍鋒, 張靜怡, 李立康, 等. 臨床試驗中缺失值的處理方法探討[J]. 協和醫學雜志, 2024, 15(5): 1165-1172.Li JF, Zhang JY, Li LK, et al. Approach to handling missing data in clinical trials[J]. Med J Peking Union Med Coll Hosp, 2024, 15(5): 1165-1172.
30.	Collins GS, Dhiman P, Ma J, et al. Evaluation of clinical prediction models (part 1): from development to external validation[J/OL]. BMJ, 2024, 384: e074819. DOI: 10.1136/bmj-2023-074819.
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34.	金麗媛, 傅方潔, 程思珺, 等. 糖尿病患者免疫功能狀態對肺部感染的影響[J]. 醫學綜述, 2020, 26(18): 3674-3678.Jin LY, Fu FJ, Cheng SJ, et al. Effect of immune function status on lung infection in patients with diabetes[J]. Med Recapitul, 2020, 26(18): 3674-3678.
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1. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2024, 74(3): 229-263.
2. Zhu H, Ma X, Ye T, et al. Esophageal cancer in China: practice and research in the new era[J]. Int J Cancer, 2023, 152(9): 1741-1751.
3. Tukanova KH, Chidambaram S, Guidozzi N, et al. Physiotherapy regimens in esophagectomy and gastrectomy: a systematic review and meta-analysis[J]. Ann Surg Oncol, 2022, 29(5): 3148-3167.
4. Xie SH, Santoni G, M?lberg K, et al. Prediction model of long-term survival after esophageal cancer surgery[J]. Ann Surg, 2021, 273(5): 933-939.
5. 劉靜, 劉芹, 黃梅. 基于SMOTE算法的食管癌放化療患者肺部感染的預后模型構建[J]. 國際腫瘤學雜志, 2024, 51(5): 267-273.Liu J, Liu Q, Huang M. Prognostic model construction of lung infection in patients with chemoradiotherapy for esophageal cancer based on SMOTE algorithm[J]. J Int Oncol, 2024, 51(5): 267-273.
6. 徐磊. 食管癌術后肺炎危險因素分析和風險預測模型的建立[D]. 鄭州大學, 2018.Xu L. Analysis of risk factors for pneumonia after esophagectomy and establishment of a risk predictive model[D]. Zhengzhou University, 2018.
7. 楊惠玲, 戴美云, 袁園. 食管癌放化療患者并發下呼吸道感染的多因素Logistic回歸分析及與臨床預后的關系探究[J]. 中國急救復蘇與災害醫學雜志, 2023, 18(8): 1078-1082.Yang HL, Dai MY, Yuan Y. Multivariate Logistic regression analysis of the factors and clinical prognosis in patients with esophageal cancer undergoing radiotherapy and chemotherapy with lower respiratory tract infection[J]. China J Emerg Resusc Disaster Med, 2023, 18(8): 1078-1082.
8. 張業凡. 食管癌術后肺部感染相關危險因素的分析及風險預測模型的建立[D]. 大連醫科大學, 2023.Zhang YF. Analysis of risk factors for postoperative pulmonary infection in patients with esophageal cancer and construction of nomogram[D]. Dalian Medical University, 2023.
9. Li S, Su J, Sui Q, et al. A nomogram for predicting postoperative pulmonary infection in esophageal cancer patients[J]. BMC Pulm Med, 2021, 21(1): 283.
10. Wang W, Yu Y, Sun H, et al. Predictive model of postoperative pneumonia after neoadjuvant immunochemotherapy for esophageal cancer[J]. J Gastrointest Oncol, 2022, 13(2): 488-498.
11. Ohkura Y, Miyata H, Konno H, et al. Development of a model predicting the risk of eight major postoperative complications after esophagectomy based on 10 826 cases in the Japan National Clinical Database[J]. J Surg Oncol, 2020, 121(2): 313-321.
12. 陳瓊儀. 基于機器學習老年人微創食管癌術后肺炎預測模型研究[D]. 汕頭大學, 2023.Chen QY. A machine learning-based prediction model for minimally invasive postoperative pneumonia in elderly people with esophageal cancer[D]. Shantou University, 2023.
13. 黨新臣. 食管癌根治術后患者并發肺部感染的危險因素分析及列線圖風險預測模型建立[D]. 新鄉醫學院, 2021.Dang XC. Analysis of risk factors for patients with pulmonary infection after radical resection of esophageal cancer and establishment of nomogram prediction risk model[D]. Xinxiang Medical University, 2021.
14. 呂明闖, 曹睿, 丁旭青, 等. 食管癌術后并發肺部感染危險因素和預測模型構建[J]. 中華醫院感染學雜志, 2024, 34(10): 1507-1511.Lyu MC, Cao R, Ding XQ, et al. Risk factors for pulmonary infection in patients esophageal cancer and establishment of prediction model[J]. Chin J Nosocomiol, 2024, 34(10): 1507-1511.
15. 畢岑. 食管癌術后肺部感染的風險預測模型的構建與驗證[D]. 江蘇大學, 2022.Bi C. Construction and validation of risk prediction model for postoperative pulmonary infection of esophageal cancer[D]. Jiangsu University, 2022.
16. 馬健健, 李萍. 食管癌術后肺部感染的危險因素模型構建及預防措施[J]. 循證護理, 2024, 10(7): 1266-1271.Ma JJ, Li P. Construction of risk factors model and preventive measures for pulmonary infection after esophageal cancer surgery[J]. Chin Evid-Based Nurs, 2024, 10(7): 1266-1271.
17. 盧偉, 馬西淼, 李禮, 等. 食管癌術后輔助化療期間肺部感染的病原學特征及風險預測模型構建[J]. 熱帶醫學雜志, 2022, 22(12): 1701-1705.Lu W, Ma XM, Li L, et al. Etiological characteristics of lung infection during adjuvant chemotherapy after esophageal cancer surgery and the construction of risk prediction model[J]. J Trop Med, 2022, 22(12): 1701-1705.
18. 時佩君, 延佩, 菅利華. 基于Lasso-Nomogram構建食管癌術后感染的風險預測模型[J]. 廣西醫科大學學報, 2023, 40(11): 1850-1856.Shi PJ, Yan P, Jian LH. Construction of a Lasso-Nomogram risk prediction model for postoperative infection of esophageal cancer[J]. J Guangxi Med Univ, 2023, 40(11): 1850-1856.
19. 牛玲娟, 王玲玲, 呂東霞, 等. 食管癌術后肺部感染病原學及風險預測模型的構建[J]. 中華醫院感染學雜志, 2023, 33(10): 1504-1507.Niu LJ, Wang LL, Lyu DX, et al. Etiological characteristics of postoperative pulmonary infection in esophageal cancer patients and establishment of risk prediction model[J]. Chin J Nosocomiol, 2023, 33(10): 1504-1507.
20. 樊一昕, 徐碩男, 劉悅, 等. 老年食管癌術后發生肺部感染的危險因素分析[J]. 老年醫學與保健, 2023, 29(1): 49-53.Fan YX, Xu SN, Liu Y, et al. Analysis of risk factors of postoperative pulmonary infection in elderly patients with esophageal cancer[J]. Geriatr Health Care, 2023, 29(1): 49-53.
21. Jin D, Yuan L, Li F, et al. A novel nomogram predicting the risk of postoperative pneumonia for esophageal cancer patients after minimally invasive esophagectomy[J]. Surg Endosc, 2022, 36(11): 8144-8153.
22. Moons Kg, De Groot Ja, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist[J/OL]. PLoS Med, 2014, 11(10): e1001744. DOI: 10.1371/journal.pmed.1001744.
23. 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[J]. Ann Intern Med, 2019, 170(1): W1-W33.
24. Gardner Al, Charlesworth M. How to write a retrospective observational study[J]. Anaesthesia, 2023, 78(4): 521-525.
25. He A, Wang Z, Wu X, et al. Incidence of post-stroke cognitive impairment in patients with first-ever ischemic stroke: a multicenter cross-sectional study in China[J]. Lancet Reg Health West Pac, 2023, 33: 100687.
26. 許汝福. Logistic回歸變量篩選及回歸方法選擇實例分析[J]. 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[J]. Chin J Evid-Based Med, 2016, 16(11): 1360-1364.
27. Ranalli MG, Salvat 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[J/OL]. Biom J, 2023, 65(8): e2100355. DOI: 10.1002/bimj.202100355.
28. 周航宇, 馮國雙. 醫學論文中logistic回歸分析常見邏輯錯誤與案例分析[J]. 中華全科醫師雜志, 2020, 19(12): 1194-1196.Zhou HY, Feng GS. Analysis of common mistakes in logistic regression analysis in medical papers[J]. Chin J Gen Pract, 2020, 19(12): 1194-1196.
29. 黎劍鋒, 張靜怡, 李立康, 等. 臨床試驗中缺失值的處理方法探討[J]. 協和醫學雜志, 2024, 15(5): 1165-1172.Li JF, Zhang JY, Li LK, et al. Approach to handling missing data in clinical trials[J]. Med J Peking Union Med Coll Hosp, 2024, 15(5): 1165-1172.
30. Collins GS, Dhiman P, Ma J, et al. Evaluation of clinical prediction models (part 1): from development to external validation[J/OL]. BMJ, 2024, 384: e074819. DOI: 10.1136/bmj-2023-074819.
31. Abou Chaar MK, Godin A, Harmsen WS, et al. Determinants of long-term survival decades after esophagectomy for esophageal cancer[J]. Ann Thorac Surg, 2023, 116(5): 1036-1044.
32. Fu YS, Kang N, Yu Y, et al. Polyphenols, flavonoids and inflammasomes: the role of cigarette smoke in COPD[J]. Eur Respir Rev, 2022, 31(164): 220028.
33. 郭嘉琪, 吳鏡湘, 姚海霞, 等. 術前衰弱對老年食管癌患者術后并發癥影響的回顧性隊列研究[J]. 中國胸心血管外科臨床雜志, 2023, 30(8): 1169-1174.Guo JQ, Wu JX, Yao HX, et al. Effect of preoperative frailty on postoperative complications in elderly patients with esophageal cancer: a retrospective cohort study[J]. Chin J Clin Thorac Cardiovasc Surg, 2023, 30(8): 1169-1174.
34. 金麗媛, 傅方潔, 程思珺, 等. 糖尿病患者免疫功能狀態對肺部感染的影響[J]. 醫學綜述, 2020, 26(18): 3674-3678.Jin LY, Fu FJ, Cheng SJ, et al. Effect of immune function status on lung infection in patients with diabetes[J]. Med Recapitul, 2020, 26(18): 3674-3678.
35. Herder C, Roden M, Venteclef N. Diabetes and pulmonary infection: how hyperglycaemia shapes the immune system[J]. Signal Transduct Target Ther, 2024, 9(1): 67.
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Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Predictive model for the risk of postoperative lung infection in esophageal cancer patients: A systematic review and meta-analysis

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