Mohamed A.R. Soliman, Hendrick Francois, Alexander O. Aguirre, Asham Khan, Waeel Hamouda, Stipe Ćorluka, Zorica Buser, Samuel K. Cho, S. Tim Yoon, AO Spine Knowledge Forum Degenerative
Neurospine 2026;23(1):42-58. Published online January 31, 2026
Objective Lumbar discectomy is one of the most frequently undertaken procedures for the management of lumbar disc herniation. However, it may be complicated by recurrent disc herniation, with reported rates as high as 25%. To the authors’ knowledge, this study is the largest systematic review to date, analyzing the clinical and radiographic risk factors for recurrent disc herniation.
Methods A systematic literature search of Embase and PubMed/Medline, covering the period from inception to October 1, 2025, was conducted to identify case-control or cohort studies reporting risk factors for recurrent disc herniation. Risk factors were classified into baseline, clinical, and radiographic risk factors. Meta-analysis was performed for any reported risk factor with data from 3 or more studies. The assessment included an evaluation of publication bias and heterogeneity.
Results A total of 51 studies published during the search timeframe, comprising 52,479 patients, met the inclusion criteria. Recurrent disc herniation occurred in 6,794 patients (12.9%). Significant risk factors for disc herniation included high body mass index (BMI) (standard mean difference [SMD], 0.48; 95% confidence interval [CI], 0.26–0.70), diabetes (odds ratio [OR], 1.48; 95% CI, 1.23–1.77), increased sagittal range of motion (SMD, 2.15; 95% CI, 0.35–3.94), and Modic changes (OR, 2.97; 95% CI, 2.20–4.01). No other significant predictors for recurrent disc herniation were identified.
Conclusion In conclusion, patients with high BMI, diabetics, increased sagittal range of motion, and presence of Modic changes are at increased risk of recurrent disc herniation. Future prospective studies are needed to validate the risk factors identified in this study associated with recurrent disc herniation.
Sung Hyun Noh, Hye Sun Lee, Go Eun Park, Yoon Ha, Jeong Yoon Park, Sung Uk Kuh, Dong Kyu Chin, Keun Su Kim, Yong Eun Cho, Sang Hyun Kim, Kyung Hyun Kim
Neurospine 2023;20(1):265-274. Published online March 31, 2023
Objective This study aimed to create an ideal machine learning model to predict mechanical complications in adult spinal deformity (ASD) surgery based on GAPB (modified global alignment and proportion scoring with body mass index and bone mineral density) factors.
Methods Between January 2009 and December 2018, 238 consecutive patients with ASD, who received at least 4-level fusions and were followed-up for ≥ 2 years, were included in the study. The data were stratified into training (n = 167, 70%) and test (n = 71, 30%) sets and input to machine learning algorithms, including logistic regression, random forest gradient boosting system, and deep neural network.
Results Body mass index, bone mineral density, the relative pelvic version score, the relative lumbar lordosis score, and the relative sagittal alignment score of the global alignment and proportion score were significantly different in the training and test sets (p < 0.05) between the complication and no complication groups. In the training set, the area under receiver operating characteristics (AUROCs) for logistic regression, gradient boosting, random forest, and deep neural network were 0.871 (0.817–0.925), 0.942 (0.911–0.974), 1.000 (1.000–1.000), and 0.947 (0.915–0.980), respectively, and the accuracies were 0.784 (0.722–0.847), 0.868 (0.817–0.920), 1.000 (1.000–1.000), and 0.856 (0.803–0.909), respectively. In the test set, the AUROCs were 0.785 (0.678–0.893), 0.808 (0.702–0.914), 0.810 (0.710–0.910), and 0.730 (0.610–0.850), respectively, and the accuracies were 0.732 (0.629–0.835), 0.718 (0.614–0.823), 0.732 (0.629–0.835), and 0.620 (0.507–0.733), respectively. The random forest achieved the best predictive performance on the training and test dataset.
Conclusion This study created a comprehensive model to predict mechanical complications after ASD surgery. The best prediction accuracy was 73.2% for predicting mechanical complications after ASD surgery.
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Objective This study aimed to analyze the prediction rate of the modified Global Alignment and Proportion (GAP) scoring system with body mass index and bone mineral density (GAPB) in each GAP of the 3 categories.
Methods Between January 2009 and December 2016, 203 consecutive patients with adult spinal deformity (ASD) underwent corrective fusion of more than 4 levels and were followedup for more than 2 years. As a validation of the GAPB, the GAPB was divided into tertiles (Q1, Q2, Q3) for each section of the GAP score. Each patient’s GAP score and GAPB system complication rate were examined.
Results Of the 203 patients, 89 patients (44%) developed mechanical complications after ASD surgery. A GAP score analysis of the patients found that 42 patients were proportioned, 85 patients were moderately disproportioned, and 76 patients were severely disproportioned. Mechanical complications occurred with increasing GAPB in the proportioned group, but were not statistically significant (p = 0.0534). However, mechanical complications occurred in a statistically significant manner in the moderately disproportioned and severely disproportioned groups as GAPB increased (p < 0.001).
Conclusion The GAPB system showed improved predictability for mechanical complications after surgery for ASD in each category of the GAP score.
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