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已发表论文
根治性膀胱切除术后急性肾损伤预测中整合术前和术后全身炎症标志物：一项多中心回顾性研究
Authors Liu Z, Fan P, Lu Y, Cao M, Yao W , Chen D, Ji F
Received 10 April 2025
Accepted for publication 21 August 2025
Published 25 September 2025 Volume 2025:18 Pages 13335—13345
DOI https://doi.org/10.2147/JIR.S529335
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Wenjian Li
Zhongqi Liu,1,* Peng Fan,1,* Yanan Lu,1,* Minghui Cao,1 Weifeng Yao,2 Dongtai Chen,3 Fengtao Ji1
1Department of Anesthesiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People’s Republic of China; 2Department of Anesthesiology, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China; 3Department of Anesthesiology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Dongtai Chen, Department of Anesthesiology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, People’s Republic of China, Email chendt@sysucc.org.cn Fengtao Ji, Department of Anesthesiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China, Email jift@mail.sysu.edu.cn
Purpose: The present study aimed to investigate the association of perioperative dynamic changes of systemic inflammation markers with AKI after radical cystectomy and their predictive value through machine learning algorithms.
Patients and Methods: Patients undergoing radical cystectomy with urinary diversion for bladder cancer from 2013 to 2022 at three university-affiliated tertiary hospitals were gathered. Perioperative dynamic changes of systemic inflammatory markers were calculated based on peripheral blood cell counts from pre- and post-operative values and categorized using restricted cubic splines (RCS). The number of positive changes in these markers was recorded as the perioperative inflammation index. Multivariable logistic regression was utilized to identify risk factors for AKI after radical cystectomy. AKI prediction models were constructed through various supervised machine learning algorithms and evaluated by the area under the receiver operating characteristic curve (AUROC).
Results: 727 patients were finally enrolled in the study, with 151 (20.8%) patients experiencing AKI following radical cystectomy. Postoperative hemoglobin (p = 0.003; OR, 0.977; 95% CI, 0.962– 0.992), albumin level (p = 0.007; OR, 0.906; 95% CI, 0.843– 0.974), intraoperative fluid infusion rate (p < 0.001; OR, 0.769; 95% CI, 0.665– 0.890) and the perioperative inflammation index (p < 0.001; OR, 1.507; 95% CI, 1.209– 1.877) were identified as independent risk factors with predictive value for AKI following radical cystectomy with urinary diversion. Among various machine learning models, XGBoost performed best (AUROC: 0.801; 95% CI: 0.735– 0.867) in AKI prediction.
Conclusion: The association between perioperative dynamic changes of inflammatory markers and AKI after radical cystectomy reinforced the necessity of perioperative inflammatory evaluation. AKI predictive models, integrating perioperative metrics, enable early identification and optimize perioperative management for AKI prevention.
Keywords: radical cystectomy, urinary diversion, perioperative systemic inflammation, acute kidney injury, machine learning algorithms
1Department of Anesthesiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People’s Republic of China; 2Department of Anesthesiology, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People’s Republic of China; 3Department of Anesthesiology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Dongtai Chen, Department of Anesthesiology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, People’s Republic of China, Email chendt@sysucc.org.cn Fengtao Ji, Department of Anesthesiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China, Email jift@mail.sysu.edu.cn
Purpose: The present study aimed to investigate the association of perioperative dynamic changes of systemic inflammation markers with AKI after radical cystectomy and their predictive value through machine learning algorithms.
Patients and Methods: Patients undergoing radical cystectomy with urinary diversion for bladder cancer from 2013 to 2022 at three university-affiliated tertiary hospitals were gathered. Perioperative dynamic changes of systemic inflammatory markers were calculated based on peripheral blood cell counts from pre- and post-operative values and categorized using restricted cubic splines (RCS). The number of positive changes in these markers was recorded as the perioperative inflammation index. Multivariable logistic regression was utilized to identify risk factors for AKI after radical cystectomy. AKI prediction models were constructed through various supervised machine learning algorithms and evaluated by the area under the receiver operating characteristic curve (AUROC).
Results: 727 patients were finally enrolled in the study, with 151 (20.8%) patients experiencing AKI following radical cystectomy. Postoperative hemoglobin (p = 0.003; OR, 0.977; 95% CI, 0.962– 0.992), albumin level (p = 0.007; OR, 0.906; 95% CI, 0.843– 0.974), intraoperative fluid infusion rate (p < 0.001; OR, 0.769; 95% CI, 0.665– 0.890) and the perioperative inflammation index (p < 0.001; OR, 1.507; 95% CI, 1.209– 1.877) were identified as independent risk factors with predictive value for AKI following radical cystectomy with urinary diversion. Among various machine learning models, XGBoost performed best (AUROC: 0.801; 95% CI: 0.735– 0.867) in AKI prediction.
Conclusion: The association between perioperative dynamic changes of inflammatory markers and AKI after radical cystectomy reinforced the necessity of perioperative inflammatory evaluation. AKI predictive models, integrating perioperative metrics, enable early identification and optimize perioperative management for AKI prevention.
Keywords: radical cystectomy, urinary diversion, perioperative systemic inflammation, acute kidney injury, machine learning algorithms