Toh Zheng An, Thambawita Vajira, Grotle Margreth, He Hong-Gu, Pikkarainen Minna, Hey Hwee Weng Dennis, Solberg Tore, Ingebrigtsen Tor, Berg Bjørnar
Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
National University Health System, Singapore, Singapore.
Eur Spine J. 2025 Sep 15. doi: 10.1007/s00586-025-09304-y.
Spinal surgery outcomes vary significantly among patients, underscoring the need for objective tools to guide clinical decision-making. While previous prediction models have focused on functional and pain-specific outcomes, global patient-reported measures such as the Global Perceived Effect (GPE) remain underexplored. This study aimed to develop and temporally validate machine learning models to predict patient's GPE 12 months after lumbar disc herniation and spinal stenosis surgery.
This registry-based study used data from the Norwegian Registry for Spine Surgery. The dataset was temporally split for model development (2007-2017) and validation (2018-2021). Six supervised machine learning models, including XGBoost, Gradient Boosting, Random Forest, Multilayer Perceptron (MLP), Decision Tree, and K-Nearest Neighbors, were used to predict dichotomized GPE outcomes (success vs. non-success). Model performance was evaluated through discrimination, calibration, and decision curve analysis.
Analyses included 13,029 patients operated for disc herniation and 18,058 for spinal stenosis. For disc herniation, the MLP model achieved the highest performance at temporal validation, with a C-statistic of 0.72 (95% CI 0.71-0.74) and good calibration. For spinal stenosis, XGBoost performed best, with C-statistic 0.67 (95% CI 0.66-0.68) and good calibration. Key predictors included pain duration, prior surgery, anxiety/depression, education level, and healthcare setting. The models demonstrated consistent clinical utility through decision curve analysis.
Using nationwide registry data, we developed and temporally validated machine learning models to predict patient-perceived benefit one year after lumbar spine surgery. The disc herniation model showed possibly useful discrimination and good calibration, supporting its potential to inform clinical decision-making.
脊柱手术的结果在患者之间差异显著，这突出表明需要客观工具来指导临床决策。虽然先前的预测模型侧重于功能和疼痛特定结果，但诸如整体感知效果（GPE）等患者报告的整体指标仍未得到充分探索。本研究旨在开发并在时间上验证机器学习模型，以预测腰椎间盘突出症和腰椎管狭窄症手术后12个月患者的GPE。
这项基于登记处的研究使用了挪威脊柱手术登记处的数据。数据集在时间上分为模型开发（2007 - 2017年）和验证（2018 - 2021年）两部分。使用六种监督机器学习模型，包括XGBoost、梯度提升、随机森林、多层感知器（MLP）、决策树和K近邻，来预测二分法的GPE结果（成功与未成功）。通过辨别力、校准和决策曲线分析来评估模型性能。
分析包括13029例接受椎间盘突出症手术的患者和18058例接受腰椎管狭窄症手术的患者。对于椎间盘突出症，MLP模型在时间验证中表现最佳，C统计量为0.72（95%CI 0.71 - 0.74），校准良好。对于腰椎管狭窄症，XGBoost表现最佳，C统计量为0.67（95%CI 0.66 - 0.68），校准良好。关键预测因素包括疼痛持续时间、既往手术、焦虑/抑郁、教育水平和医疗环境。通过决策曲线分析，模型显示出一致的临床实用性。
利用全国登记处数据，我们开发并在时间上验证了机器学习模型，以预测腰椎手术后一年患者感知的益处。椎间盘突出症模型显示出可能有用的辨别力和良好的校准，支持其为临床决策提供信息的潜力。
