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Updated: Jul 30, 2026
An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
Published on: January 27, 2010
Using Artificial Intelligence to Predict Residual Distal Lumbosacral Pain Post Percutaneous Kyphoplasty for
Yuye Zhang1, Yingzi Zhang1, Xingyu You2
1Department of Orthopedics, The Second Affiliated Hospital of Soochow University, Suzhou, People's Republic of China.
Background:
Percutaneous kyphoplasty (PKP) can restore spinal stability and relieve pain in patients with osteoporotic vertebral compression fractures (OVCF). However, in some cases, distal lumbosacral pain (DLP) persists postoperatively, affecting patients' expectations of the surgery and their recovery to activities of daily life.
Objective:
To use artificial intelligence to predict DLP post-PKP for OVCF, thereby providing personalized treatment plans for patients with OVCF.
Study Design:
Retrospective study.
Setting:
The study was carried out at a university hospital.
Methods:
A univariate analysis was performed to identify the risk factors for DLP post-PKP. A heatmap analysis was conducted to examine the relationships between variables in the dataset. A random forest model was established, and its performance was evaluated using a confusion matrix. After validating and tuning the model, features were ranked based on their contribution to prediction accuracy.
Results:
A total of 179 patients completed this study. Patients were divided into 2 groups (Group 0 without DLP; Group 1 with DLP). The univariate analysis indicated statistically significant differences in terms of bone density, intravertebral vacuum cleft, sarcopenia, bone cement distribution, interspinous ligament degeneration, and Hounsfield unit (P < 0.05). The heatmap analysis revealed a moderate correlation between DLP and both sarcopenia and interspinous ligament degeneration. A random forest model was built. The confusion matrix showed that the model exhibited strong performance across all metrics. The random forest model showed that the preoperative Cobb angle and sarcopenia were the most critical features.
Limitations:
This was a retrospective study, which may be prone to selection and recall bias. Single-center noncontrolled studies may also introduce bias.
Conclusion:
Our random forest model can effectively predict DLP post-PKP for OVCF, assisting in the selection of treatment plans.
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