STUDY DESIGN: A retrospective real-world study. OBJECTIVE: Using machine learning models to identify risk factors for residual pain after PLIF in patients with degenerative lumbar spine disease. SUMMARY OF BACKGROUND DATA: Residual pain after PLIF is a frequent phenomenon, and the specific risk factors for residual pain are not known. MATERIALS AND METHODS: Between June 2018 and March 2023, 936 patients with lumbar degenerative disease who underwent PLIF surgery were recruited. Group A (n=501) had <
7 days of VAS ≥3 pain within 1 month post-PLIF, while Group B (n=435) had ≥7 days. Imaging outcomes included PMI, MMI, MMD, lumbar lordosis (LL), and LL improvement rate. Functional outcomes were assessed by VAS. Univariate and multivariate logistic regression analyses were used to determine the potential risk of short-term postoperative pain. Risk factors were identified using machine learning models and predicted whether residual pain would occur. RESULTS: A total of 435 (46.5%) patients experienced residual postoperative pain. Independent risk factors included surgical segment, PMI, MMI, and depression level. The Random Forest Model model had an accuracy of 95.7%, a sensitivity of 96.4%, a specificity of 94.1%, and an F1 score of approximately 95.2% for predicting recurrent pain, indicating high reliability and generalizability. CONCLUSIONS: Our study reveals risk factors for the development of residual pain after PLIF. Compared to the group with residual pain, the group without pain had more robust paravertebral muscles, improved psychological characteristics and a greater LL improvement rate. These factors may represent targets for pre-operative and peri-operative optimization as a means to minimize the potential for residual pain following PLIF.