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已发表论文
老年人术后慢性疼痛的风险因素：一项单中心回顾性研究
Authors Li LH , Guo H , Su FZ, Chen J, Xie YB
Received 4 September 2025
Accepted for publication 25 November 2025
Published 3 December 2025 Volume 2025:20 Pages 2363—2376
DOI https://doi.org/10.2147/CIA.S562458
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 4
Editor who approved publication: Dr Zhi-Ying Wu
Li-Heng Li,1,* Hao Guo,2,* Feng-Zhi Su,3 Jie Chen,4 Yu-Bo Xie1,5
1Department of Anesthesiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China; 2Department of Anesthesiology, Renmin Hospital, Hubei University of Medicine, Shiyan, People’s Republic of China; 3Department of Anesthesiology, People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, People’s Republic of China; 4Department of Anesthesiology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China; 5Department of Anesthesiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Yu-Bo Xie, Department of Anesthesiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, People’s Republic of China, Email xybdoctor@163.com
Purpose: Chronic Post-Surgical Pain (CPSP) is a common surgical complication, but the association between perioperative complications, patients’ intrinsic mental status, and 3-month CPSP remains unclear in elderly surgical populations. This study thus aims to identify perioperative risk factors for 3-month CPSP in elderly patients after non-cardiac surgery, with CPSP here defined as pain intensity ≥ 3 on the Numerical Rating Scale at 3-month follow-up.
Patients and Methods: This retrospective study included 367 elderly patients. We first analyzed variables with descriptive statistics, then conducted all subsequent analyses separately for each of the three surgical subgroups, allowing for potential nuances in the contributory patterns of key factors across groups. To predict 3-month CPSP, we used 10 machine learning algorithms. Model performance was assessed via repeated 5-fold cross-validation, and top-performing models were interpreted using SHapley Additive exPlanations (SHAP) to clarify how key factors contribute.
Results: Of 367 patients, the overall prevalence of 3-month CPSP was 25.07%, with significant variation across surgical subgroups: 48.05% in orthopedic surgery, 10.34% in urinary tumor surgery, and 7.14% in abdominal tumor surgery. The Random Forest model showed strong, consistent predictive ability across the three subgroups. Frailty was a key shared risk factor for CPSP across all surgical types, and further analyses identified surgery-specific risk factors.
Conclusion: These findings demonstrate that data-driven models can reliably predict CPSP across studied surgical types, with frailty state as a universal risk factor and distinct surgery-specific profiles supporting tailored perioperative risk assessment and prevention strategies.
Keywords: elderly patients, chronic post-surgical pain, risk factors, machine learning algorithms
1Department of Anesthesiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China; 2Department of Anesthesiology, Renmin Hospital, Hubei University of Medicine, Shiyan, People’s Republic of China; 3Department of Anesthesiology, People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, People’s Republic of China; 4Department of Anesthesiology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China; 5Department of Anesthesiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Yu-Bo Xie, Department of Anesthesiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, People’s Republic of China, Email xybdoctor@163.com
Purpose: Chronic Post-Surgical Pain (CPSP) is a common surgical complication, but the association between perioperative complications, patients’ intrinsic mental status, and 3-month CPSP remains unclear in elderly surgical populations. This study thus aims to identify perioperative risk factors for 3-month CPSP in elderly patients after non-cardiac surgery, with CPSP here defined as pain intensity ≥ 3 on the Numerical Rating Scale at 3-month follow-up.
Patients and Methods: This retrospective study included 367 elderly patients. We first analyzed variables with descriptive statistics, then conducted all subsequent analyses separately for each of the three surgical subgroups, allowing for potential nuances in the contributory patterns of key factors across groups. To predict 3-month CPSP, we used 10 machine learning algorithms. Model performance was assessed via repeated 5-fold cross-validation, and top-performing models were interpreted using SHapley Additive exPlanations (SHAP) to clarify how key factors contribute.
Results: Of 367 patients, the overall prevalence of 3-month CPSP was 25.07%, with significant variation across surgical subgroups: 48.05% in orthopedic surgery, 10.34% in urinary tumor surgery, and 7.14% in abdominal tumor surgery. The Random Forest model showed strong, consistent predictive ability across the three subgroups. Frailty was a key shared risk factor for CPSP across all surgical types, and further analyses identified surgery-specific risk factors.
Conclusion: These findings demonstrate that data-driven models can reliably predict CPSP across studied surgical types, with frailty state as a universal risk factor and distinct surgery-specific profiles supporting tailored perioperative risk assessment and prevention strategies.
Keywords: elderly patients, chronic post-surgical pain, risk factors, machine learning algorithms