ArticleSpine2025
Development and Validation of Interpretable Machine Learning Models Incorporating Paraspinal Muscle Quality to Predict Cage Subsidence Risk Following Posterior Lumbar Interbody Fusion.
Article in Spine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Article
- Postoperative bone loss after posterior lumbar interbody fusion is a risk factor of cage subsidence independently of preoperative CT-derived attenuation: a retrospective study based on hounsfield unit.BMC musculoskeletal disorders · 2026Article
- Development of a predictive model for adverse outcomes after unilateral biportal endoscopic spine surgery in the treatment of lumbar disc herniation using six interpretable machine learning models.Frontiers in surgery · 2026Article
- A multidimensional in-depth analysis of postoperative pain after PLIF in patients with degenerative lumbar spine disease.Frontiers in medicine · 2025Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
designA real-world, multicenter retrospective study.
objectiveTo identify independent risk factors for cage subsidence after Posterior Lumbar Interbody Fusion (PLIF) and develop an interpretable machine learning model for risk prediction. MATERIALS AND
methodsPatients with degenerative lumbar disease who underwent single-level PLIF (January 2018-October 2023) were retrospectively included. A training set (n=620) came from the First Affiliated Hospital of Soochow University, and a validation set (n=100) from the Second Affiliated Hospital. Cage subsidence (≥2 mm intervertebral height loss) was assessed radiographically. Parameters included paraspinal muscle indices [psoas muscle index (PMI), multifidus muscle index (MMI), fat infiltration (FI)], bone density markers [Hounsfield Unit (HU) value, Vertebral Bone Quality (VBQ), Endplate Bone Quality (EBQ)], cage position, and postoperative alignment. Multivariate logistic regression identified risk factors; multiple machine learning models were developed and evaluated. A web-based tool was created for clinical deployment.
resultsMultivariate analysis identified PMI, FI, HU value, VBQ, cage position, cage height, postoperative Intervertebral Height (IH), corrected IH, and corrected SA as independent risk factors for cage subsidence. Light Gradient Boosting Machine (LightGBM) outperformed other models, achieving the highest AUC (0.9752), accuracy (0.92), and F1-score (0.9216), with the lowest Brier score (0.0660). After excluding indicators related to paravertebral muscle function from the prediction model, the predictive accuracy of the model decreased substantially. (SHapley Additive exPlanations) SHAP analysis confirmed VBQ, FI and PMI as the most influential predictors. The final model was deployed as a web-based tool for real-time clinical risk assessment.
conclusionsKey risk factors for PLIF cage subsidence were identified, and a validated machine learning model was developed. The high-performance LightGBM model, deployed in a user-friendly web application, enables spine surgeons to optimize surgical planning and reduce subsidence risk.
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