ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2025
Prediction model of recurrence in patients with lumbar disc herniation after unilateral biportal endoscopy spinal surgery- XGBoost machine learning model can be interpreted based on SHAP.
Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundPatients with single-segment lumbar disc herniation(LDH) who have undergone unilateral biportal endoscopic (UBE) spinal surgery face the risk of postoperative recurrence. The potential factors for recurrence of lumbar disc herniation(rLDH) are not fully understood.
aimsThis study aimed to investigate the risk factors for rLDH after UBE spinal surgery and establish a risk factor prediction model using machine learning to predict the recurrence rate. STUDY
designThis study used LASSO regression for feature screening, XGBoost for machine learning model construction, ROC curve analysis, calibration curve analysis, clinical decision curve analysis, specificity, sensitivity, accuracy, and F1 score to evaluate the model’s performance.
resultsThe clinical data of 271 patients with LDH were retrospectively analyzed. All patients have undergone UBE spinal surgery. 47 patients relapsed, with a recurrence rate of 17.3%. The XGBoost model predicted a higher AUC for rLDH (training: 0.927, 95%CI: 0.882–0.958; Test: 0.866, 95%CI: 0.760–0.937). The importance of SHAP variables in the model in descending order is: “Width of Protrusion Base(WPB), “” Bone Removal Range(BRR), “"Modic Change,” " Type of LDH,” “Middle Vertebral space Height(MVH),” “Hyperlipidemia.”
conclusionThe large WPB、high WPB、 extensive BRR, and preoperative Modic Changes are more likely to develop rLDH. Hyperlipidemia and prolapsed disc herniation are negatively correlated with rLDH. RELEVANCE TO CLINICAL PRACTICE: Improve the ability to predict the rLDH after UBE spinal surgery. This will enable clinicians to intervene early and improve postoperative outcomes.
Indexed as
Identifiers
40624231What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.