Volume 24 Issue 1
Jan.  2026
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ZHOU Minhong, WANG Lijuan, WANG Linxia, JIN Haiming. A prediction model for postoperative residual low back and leg pain in patients with postmenopausal osteoporosis complicated with lumbar disc herniation was constructed based on machine learning algorithms[J]. Chinese Journal of General Practice, 2026, 24(1): 50-54. doi: 10.16766/j.cnki.issn.1674-4152.004327
Citation: ZHOU Minhong, WANG Lijuan, WANG Linxia, JIN Haiming. A prediction model for postoperative residual low back and leg pain in patients with postmenopausal osteoporosis complicated with lumbar disc herniation was constructed based on machine learning algorithms[J]. Chinese Journal of General Practice, 2026, 24(1): 50-54. doi: 10.16766/j.cnki.issn.1674-4152.004327

A prediction model for postoperative residual low back and leg pain in patients with postmenopausal osteoporosis complicated with lumbar disc herniation was constructed based on machine learning algorithms

doi: 10.16766/j.cnki.issn.1674-4152.004327
Funds:

 LQ21H060010

  • Received Date: 2025-07-06
    Available Online: 2026-04-01
  •   Objective  This study aims to provide a predictive tool for the identification of postoperative residual low back and leg pain in postmenopausal patients with osteoporosis (OP) combined with lumbar disc herniation (LDH) by constructing a predictive model based on machine learning algorithms.  Methods  A retrospective study was conducted on 200 postmenopausal patients with osteoporosis and lumbar disc herniation who were admitted to the Department of Pain Medicine and Department of Spinal Surgery at the Second Affiliated Hospital of Wenzhou Medical University from January 2023 to January 2025. Using stratified random sampling, the patients were divided into a training set (n=140) and a validation set (n=60) in a 7∶3 ratio. LASSO regression was used to screen the characteristic variables related to residual low back and leg pain after surgery in postmenopausal patients with OP combined with LDH. The random forest algorithm was used as the basic algorithm for machine learning analysis to construct the prediction model. The model performance was verified using the validation set data.  Results  The incidence of residual lumbago and leg pain after surgery in postmenopausal patients with osteoporosis and lumbar disc herniation was 48.50% (97 cases). Factors influencing postoperative residual pain in these patients include age, diabetes, lumbar bone mineral density, pain manifestations, Pfirrmann grade, and preoperative Oswestry disability index (ODI). Based on the variables selected via LASSO, a random forest model was constructed, achieving AUC values of 0.799 and 0.790 in the training and validation sets, respectively, for predicting postoperative residual lumbago and leg pain. Pfirrmann classification contributed positively to the residual low back and leg pain after surgery in postmenopausal patients with OP combined with LDH, while lumbar bone mineral density contributed negatively to the residual low back and leg pain after surgery in postmenopausal patients with OP combined with LDH.  Conclusion  The prediction model constructed based on the random forest algorithm can effectively predict the risk of postoperative residual low back and leg pain in postmenopausal patients with OP combined with LDH, which is conducive to the early identification of high-risk patients with postoperative residual low back and leg pain and provides guidance for clinical management.

     

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