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Background <p>Percutaneous endoscopic interlaminar discectomy (PEID) is a common surgical technique for lumbar disc herniation (LDH), but the risk factors for adverse outcomes remain controversial. This study aims to develop and validate a predictive model based on machine learning algorithms to identify key clinical indicators influencing adverse outcomes after PEID.</p> Methods <p>This retrospective study included 414 LDH patients who underwent single-level PEID between October 2018 and June 2024. Data were divided into training (<i>n</i> = 290) and validation (<i>n</i> = 124) sets. Six machine learning algorithms were used for feature selection, identifying core indicators. Models were constructed based on these indicators and evaluated for predictive performance.</p> Result <p>Five core indicators were identified: Modic Changes (MC), Basal Width Of The Herniated Disc (BWHD), Body Mass Index (BMI), Ratio Of Disc Herniation (RDH), and Interspinous Ligament Injury (ILI). The XGB model performed best, with an AUC of 0.809 in the training set and 0.718 in the validation set. Risk thresholds for BWHD, BMI, and RDH were 1.7&#xa0;mm, 23.3&#xa0;kg/m², and 37.2%, respectively.</p> Conclusion <p>MC, ILI, BWHD, BMI, and RDH are risk factors for postoperative adverse outcomes in PEID patients. The model provides useful clinical guidance, with validated risk thresholds for key indicators.</p>

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Percutaneous endoscopic interlaminar discectomy in patients with lumbar disc herniation: risk factors and thresholds for adverse outcomes

  • 期刊论文

  • 发表日期：

    2026年5月11日

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  • Chengrui Peng,
  • Xuan Deng,
  • Xiuqian Wang,
  • Li He,
  • Jun Ao,
  • Hu Qian

摘要

Background

Percutaneous endoscopic interlaminar discectomy (PEID) is a common surgical technique for lumbar disc herniation (LDH), but the risk factors for adverse outcomes remain controversial. This study aims to develop and validate a predictive model based on machine learning algorithms to identify key clinical indicators influencing adverse outcomes after PEID.

Methods

This retrospective study included 414 LDH patients who underwent single-level PEID between October 2018 and June 2024. Data were divided into training (n = 290) and validation (n = 124) sets. Six machine learning algorithms were used for feature selection, identifying core indicators. Models were constructed based on these indicators and evaluated for predictive performance.

Result

Five core indicators were identified: Modic Changes (MC), Basal Width Of The Herniated Disc (BWHD), Body Mass Index (BMI), Ratio Of Disc Herniation (RDH), and Interspinous Ligament Injury (ILI). The XGB model performed best, with an AUC of 0.809 in the training set and 0.718 in the validation set. Risk thresholds for BWHD, BMI, and RDH were 1.7 mm, 23.3 kg/m², and 37.2%, respectively.

Conclusion

MC, ILI, BWHD, BMI, and RDH are risk factors for postoperative adverse outcomes in PEID patients. The model provides useful clinical guidance, with validated risk thresholds for key indicators.