Journal of Shandong University (Health Sciences) ›› 2026, Vol. 64 ›› Issue (2): 78-88.doi: 10.6040/j.issn.1671-7554.0.2025.1184

• Clinical Medicine • Previous Articles    

Construction of a chronic post-surgical pain prediction model for posterior lumbar interbody fusion surgery based on interpretable machine learning

WANG Jianmin1,2, LI Xiaofeng1,2, YOU Zhitao1,2, DONG Shengjie2,3, ZHAO Yuchi2,3, LI Zhanju4, ZOU Dexin1,2, ZHANG Jianfeng1,2, SUN Tao2, DU Wei1,2   

  1. 1. Spinal Surgery;
    2. Yantai Key Laboratory for Repair and Reconstruction of Bone &
    Joint;
    3. Joint Surgery;
    4. Information Management Department, Yantaishan Hospital Affiliated to Binzhou Medical University, Yantai 264003, Shandong, China
  • Published:2026-02-10

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Abstract: Objective To construct a high-precision and well-interpretable risk prediction model for chronic post-surgical pain(CPSP)after posterior lumbar interbody fusion(PLIF), so as to provide a reliable tool for the early identification of high-risk populations and precise prevention in clinical practice. Methods A retrospective study was conducted on 759 patients who underwent PLIF at our hospital from January 2019 to December 2023, including 375 males and 384 females. The patients were 33-80(55.28±9.94)years old. All cases were stratified and randomly divided into a training set(n=531)and a testing set(n=228)at a ratio of 7∶3. A total of 40 characteristic variables were collected from all patients before, during, and after surgery. After data pre-processing and LASSO regression feature screening, seven machine learning models were constructed, with area under the curve(AUC), F1 score, and other indicators as the core indicator to select the optimal model, and the Shapley additive explanations(SHAP)tool was used for interpretability analysis. Results Ten core predictive features of CPSP were ultimately selected, among which pain catastrophizing scale(PCS)score, preoperative surgical site pain, and complications were the three core driving factors(cumulative contribution: 48.21%). The Naive Bayes(AUC=0.914)and logistic regression(AUC=0.913)models exhibited excellent performance, and logistic regression had a more balanced overall performance(F1 score=0.685). The Naive Bayes specificity was as high as 0.958. SHAP analysis clarified the direction and magnitude of the influence of each feature on the prediction results, revealing the threshold effect of significantly increasing CPSP risk when the PCS score exceeds 30 points. Conclusion The CPSP risk prediction model based on machine learning has good discriminative power and clinical interpretability. The core predictive factors provide clear targets for personalized prevention and control strategies in clinical practice, which helps to promote the transformation of spinal surgery from “empirical medicine” to “data-driven precision prevention”.

Key words: Posterior lumbar interbody fusion, Chronic post-surgical pain, Machine learning, Risk prediction model, Explainable artificial intelligence, Algorithm comparison

CLC Number: 

  • R687.3
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