Systematic review of predictive models for postoperative chronic pain risk in patients undergoing knee arthroplasty_West China Medical Journal

Authors：

 ZHOU Yang ^1,2 , ZENG Biyun ³ , GUO Yabin ^1,2 , WANG Boya ^1,2 , LIU Xiaotong ^1,2 , ZHOU Yang ¹

1. Teaching and Research Section of Clinical Nursing, Xiangya Hospital of Central South University, Changsha, Hunan 410005, P. R. China;
2. Xiangya School of Nursing, Central South University, Changsha, Hunan 410013, P. R. China;
3. Orthopedic Ward 6, Xiangya Hospital of Central South University, Changsha, Hunan 410005, P. R. China;

Corresponding author：

ZHOU Yang, Email: zhouyang1030@csu.edu.cn

Keywords：

Knee arthroplasty; postoperative chronic pain; prediction model; systematic review

DOI：

10.7507/1002-0179.202507211

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Objective To conduct a systematic review of the construction methods, predictive factors, and model quality of risk prediction models for postoperative chronic pain in knee replacement surgery patients, providing evidence for the development of nursing-sensitive dynamic prediction models. Methods A systematic review of risk prediction models for postoperative chronic pain in knee replacement surgery patients was conducted by searching PubMed, Web of Science, Cochrane Library, CINAHL, SinoMed, CNKI, Wanfang Database, and VIP Database. The search period was from the establishment of the databases to February 28, 2025. Two researchers independently screened the literature, extracted data, and assessed the risk of bias and applicability of the included studies. Results A total of 10 studies involving 10 predictive models were included in this review. Among these, three models underwent internal validation, and one model underwent external validation. Commonly reported predictive factors included postoperative 24-hour Numerical Rating Scale scores, postoperative knee function scores, sleep disorders, preoperative depression, postoperative functional exercises, postoperative complications, preoperative pain, and postoperative C-reactive protein levels. All 10 studies had a high risk of bias and were generally applicable. Conclusions Existing risk prediction models generally rely on static indicators and lack dynamic monitoring of postoperative rehabilitation behaviors and psychosocial factors, with severe deficiencies in model validation. Future research should focus on developing nursing-led multidimensional dynamic models that incorporate functional exercise adherence data collected via wearable devices, standardize external model validation, and enhance clinical translation value.

Citation： ZHOU Yang, ZENG Biyun, GUO Yabin, WANG Boya, LIU Xiaotong, ZHOU Yang. Systematic review of predictive models for postoperative chronic pain risk in patients undergoing knee arthroplasty. West China Medical Journal, 2025, 40(9): 1437-1442. doi: 10.7507/1002-0179.202507211 Copy

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West China Medical Journal

Systematic review of predictive models for postoperative chronic pain risk in patients undergoing knee arthroplasty

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