Shen Ning, Zhang Yusi, Ding Zhendong, Li Runmin, Quan Xubin, Liu Xiaozhu, Zhang Yang, Xiang Tianyu, Zhang Yingang, Yin Chengliang, Li Wenle
Department of Anesthesiology, Huadu District People's Hospital of Guangzhou, Guangzhou, China.
The Third School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China.
Int J Surg. 2025 Sep 1;111(9):5904-5913. doi: 10.1097/JS9.0000000000002730. Epub 2025 Jun 19.
Patients with severe lumbar disc herniation (LDH), particularly those complicated by spinal stenosis or vertebral instability, frequently require posterior lumbar interbody fusion to alleviate nerve compression and reconstruct spinal biomechanical stability. Aiming to optimize individualized surgical planning, it is necessary to establish accurate predictive models derived from multidimensional clinical data.
In this retrospective, multi-center study, the data utilized in this study were sourced from the Degenerative Spine Diseases in China (DSDC2024, NCT05867732). The model was trained on 3055 cases and externally validated across four geographically distinct cohorts ( n = 3186). Leveraging a two-stage ensemble framework, we first applied Lasso regression to select target predictive variables from 38 clinical accessibility features (demographics, comorbidities, surgical parameters, and laboratory indices), then integrated XGBoost, random forest, and logistic regression through stacked generalization. Bayesian optimization with 10-fold cross-validation refined hyperparameters, while decision curve analysis quantified clinical utility against traditional risk assessment methods. Shapley Additive exPlanations analysis quantified feature contributions and interaction effects.
Amongst the 70 algorithmic combinations evaluated, the integration of Lasso with Stack emerged as the most predictive, achieving an impressive average area under the receiver operating characteristic curve of 0.884. The top five significant predictors were the fusion levels, clinical course duration, preoperative hospitalization, preoperative hemoglobin, and preoperative albumin.
The IBLED-LDH model provides a valuable tool for preoperative intraoperative blood loss risk stratification, balancing predictive accuracy with interpretability through advanced ensemble learning.
重度腰椎间盘突出症（LDH）患者，尤其是合并椎管狭窄或椎体不稳的患者，常常需要进行腰椎后路椎间融合术以缓解神经压迫并重建脊柱生物力学稳定性。为了优化个体化手术规划，有必要基于多维临床数据建立准确的预测模型。
在这项回顾性多中心研究中，本研究使用的数据来源于中国退行性脊柱疾病研究（DSDC2024，NCT05867732）。该模型在3055例病例上进行训练，并在四个地理位置不同的队列（n = 3186）中进行外部验证。利用两阶段集成框架，我们首先应用套索回归从38个临床可及性特征（人口统计学、合并症、手术参数和实验室指标）中选择目标预测变量，然后通过堆叠泛化集成XGBoost、随机森林和逻辑回归。采用10折交叉验证的贝叶斯优化对超参数进行优化，同时决策曲线分析针对传统风险评估方法量化临床效用。Shapley值加法解释分析量化特征贡献和交互效应。
在评估的70种算法组合中，套索回归与堆叠集成的组合预测能力最强，在受试者操作特征曲线下的平均面积达到了令人印象深刻的0.884。最重要的五个预测因素是融合节段、临床病程持续时间、术前住院时间、术前血红蛋白和术前白蛋白。
IBLED-LDH模型为术前术中失血风险分层提供了一个有价值的工具，通过先进的集成学习在预测准确性和可解释性之间取得了平衡。
