Machine Learning Model for Recurrent Lumbar Disc Herniation after Lumbar Discectomy.

Abstract

Objective: Recurrent lumbar disc herniation (RLDH) is a significant challenge following lumbar discectomy, with recurrence rates of 5-15%. Established risk factors include male gender, diabetes mellitus, smoking, and obesity, but the role of paraspinal muscles in recurrence is unclear. This study was conducted to identify key risk factors for RLDH, including the volume of paraspinal muscles with machine learning.

Methods: We used data from 126 patients who underwent lumbar discectomy between January 2003 and September 2023 and had follow-up outpatient visits for more than 6 months at a single institution. Variables selected for the model, comprising demographic and clinical variables, medical history, LDH operation-related variables, and magnetic resonance imaging (MRI) measurements for RLDH. Based on clinical symptoms and radiologic results, the patients were classified into RLDH and non-RLDH groups, and RLDH was defined at the same surgical level on follow-up MRI.

Results: Totally, 38 patients were included in the RLDH group and 88 in the non-RLDH group. The volume of quadratus lumborum was identified as a risk factor for RLDH (odds ratio, 7.894; p=0.001). Among the five different machine learning algorithms, extreme gradient boosting achieved the best result with an accuracy of 0.794 and area under the curve of 0.811. In terms of Shapley Additive exPlanations value analysis, the weight, volume of quadratus lumborum, psoas major, and vertebra were key features for predicting RLDH.

Conclusion: The prediction model would be of great assistance for surgeons to make surgical decisions or establish observation intervals.