Develop and validate a machine learning model to predict the risk of persistent pain after percutaneous transforaminal endoscopic discectomy
BackgroundPersistent pain is a common complication following percutaneous transforaminal endoscopic discectomy (PTED) for lumbar disc herniation. Identifying associated risk factors and developing a predictive model are crucial for guiding clinical decisions. This study aims to utilize machine learn...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Surgery |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fsurg.2025.1631651/full |
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| author | Jun Yuan Jun Fu |
| author_facet | Jun Yuan Jun Fu |
| author_sort | Jun Yuan |
| collection | DOAJ |
| description | BackgroundPersistent pain is a common complication following percutaneous transforaminal endoscopic discectomy (PTED) for lumbar disc herniation. Identifying associated risk factors and developing a predictive model are crucial for guiding clinical decisions. This study aims to utilize machine learning models to predict persistent pain, identify key influencing factors, and construct a risk model to assess the likelihood of persistent pain.MethodsWe first compared baseline characteristics and pathological indicators between patients who developed persistent pain and those who did not after PTED. Significant factors were used as input features in four machine learning models: Logistic Regression (LR), Support Vector Machine (SVM), XGBoost, and Multilayer Perceptron (MLP). Each model was optimized through grid search and 10-fold cross-validation. Performance was evaluated using ROC curves, F1 score, accuracy, recall, and precision. Models with AUC values exceeding 0.9, specifically XGBoost and MLP, were selected for SHAP visualization and risk prediction model construction.ResultsAmong the four machine learning models, XGBoost and MLP achieved the best performance, with AUC values of 0.907 and 0.916, respectively. SHAP analysis identified a history of lumbar spine trauma and herniation calcification as key features positively influencing persistent pain risk. Elevated inflammatory markers (e.g., CRP, ESR, and WBC) and older age also significantly impacted predictions. Using the most important features from XGBoost and MLP, a risk prediction model was constructed and externally validated, achieving an AUC of 0.798, indicating good predictive accuracy.ConclusionHistory of lumbar spine trauma, herniation calcification, and inflammatory markers are important predictors of persistent pain after PTED. The risk prediction model based on XGBoost and MLP shows high predictive accuracy and can serve as a valuable tool for clinical decision-making. |
| format | Article |
| id | doaj-art-168b8e48205f498dbf9dcc8969d7ccc3 |
| institution | Kabale University |
| issn | 2296-875X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Surgery |
| spelling | doaj-art-168b8e48205f498dbf9dcc8969d7ccc32025-08-20T03:25:29ZengFrontiers Media S.A.Frontiers in Surgery2296-875X2025-07-011210.3389/fsurg.2025.16316511631651Develop and validate a machine learning model to predict the risk of persistent pain after percutaneous transforaminal endoscopic discectomyJun Yuan0Jun Fu1Department of Orthopedics, Wuhan Hospital of Traditional Chinese Medicine, Wuhan, Hubei, ChinaDepartment of Pain, Hubei Maternal and Child Health Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaBackgroundPersistent pain is a common complication following percutaneous transforaminal endoscopic discectomy (PTED) for lumbar disc herniation. Identifying associated risk factors and developing a predictive model are crucial for guiding clinical decisions. This study aims to utilize machine learning models to predict persistent pain, identify key influencing factors, and construct a risk model to assess the likelihood of persistent pain.MethodsWe first compared baseline characteristics and pathological indicators between patients who developed persistent pain and those who did not after PTED. Significant factors were used as input features in four machine learning models: Logistic Regression (LR), Support Vector Machine (SVM), XGBoost, and Multilayer Perceptron (MLP). Each model was optimized through grid search and 10-fold cross-validation. Performance was evaluated using ROC curves, F1 score, accuracy, recall, and precision. Models with AUC values exceeding 0.9, specifically XGBoost and MLP, were selected for SHAP visualization and risk prediction model construction.ResultsAmong the four machine learning models, XGBoost and MLP achieved the best performance, with AUC values of 0.907 and 0.916, respectively. SHAP analysis identified a history of lumbar spine trauma and herniation calcification as key features positively influencing persistent pain risk. Elevated inflammatory markers (e.g., CRP, ESR, and WBC) and older age also significantly impacted predictions. Using the most important features from XGBoost and MLP, a risk prediction model was constructed and externally validated, achieving an AUC of 0.798, indicating good predictive accuracy.ConclusionHistory of lumbar spine trauma, herniation calcification, and inflammatory markers are important predictors of persistent pain after PTED. The risk prediction model based on XGBoost and MLP shows high predictive accuracy and can serve as a valuable tool for clinical decision-making.https://www.frontiersin.org/articles/10.3389/fsurg.2025.1631651/fulllumbar disc herniationmachine learningpercutaneous transforaminal endoscopic discectomypersistent painrisk prediction model |
| spellingShingle | Jun Yuan Jun Fu Develop and validate a machine learning model to predict the risk of persistent pain after percutaneous transforaminal endoscopic discectomy Frontiers in Surgery lumbar disc herniation machine learning percutaneous transforaminal endoscopic discectomy persistent pain risk prediction model |
| title | Develop and validate a machine learning model to predict the risk of persistent pain after percutaneous transforaminal endoscopic discectomy |
| title_full | Develop and validate a machine learning model to predict the risk of persistent pain after percutaneous transforaminal endoscopic discectomy |
| title_fullStr | Develop and validate a machine learning model to predict the risk of persistent pain after percutaneous transforaminal endoscopic discectomy |
| title_full_unstemmed | Develop and validate a machine learning model to predict the risk of persistent pain after percutaneous transforaminal endoscopic discectomy |
| title_short | Develop and validate a machine learning model to predict the risk of persistent pain after percutaneous transforaminal endoscopic discectomy |
| title_sort | develop and validate a machine learning model to predict the risk of persistent pain after percutaneous transforaminal endoscopic discectomy |
| topic | lumbar disc herniation machine learning percutaneous transforaminal endoscopic discectomy persistent pain risk prediction model |
| url | https://www.frontiersin.org/articles/10.3389/fsurg.2025.1631651/full |
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