Ye Bo, Sun Yang, Chen Guolin, Wang Bowen, Meng Hailan, Shan Lequn
Department of Spine Surgery, Honghui Hospital, Xi'an Jiaotong University, Shaanxi, China.
Yan'an University, Yan'an, China.
Eur Spine J. 2025 Aug 12. doi: 10.1007/s00586-025-09168-2.
Cervical 2 pedicle screw (C2PS) fixation is widely used in posterior cervical surgery but carries risks of vertebral artery injury (VAI), a rare yet severe complication. This study aimed to identify risk factors for VAI during C2PS placement and develop a machine learning (ML)-based predictive model to enhance preoperative risk assessment.
Clinical and radiological data from 280 patients undergoing head and neck CT angiography were retrospectively analyzed. Three-dimensional reconstructed images simulated C2PS placement, classifying patients into injury (n = 98) and non-injury (n = 182) groups. Fifteen variables, including characteristic of patients and anatomic variables were evaluated. Eight ML algorithms were trained (70% training cohort) and validated (30% validation cohort). Model performance was assessed using AUC, sensitivity, specificity, and SHAP (SHapley Additive exPlanations) for interpretability.
Six key risk factors were identified: pedicle diameter, high-riding vertebral artery (HRVA), intra-axial vertebral artery (IAVA), vertebral artery diameter (VAD), distance between the transverse foramen and the posterior end of the vertebral body (TFPEVB) and distance between the vertebral artery and the vertebral body (VAVB). The neural network model (NNet) demonstrated optimal predictive performance, achieving AUCs of 0.929 (training) and 0.936 (validation). SHAP analysis confirmed these variables as primary contributors to VAI risk.
This study established an ML-driven predictive model for VAI during C2PS placement, highlighting six critical anatomical and radiological risk factors. Integrating this model into clinical workflows may optimize preoperative planning, reduce complications, and improve surgical outcomes. External validation in multicenter cohorts is warranted to enhance generalizability.
颈椎2椎弓根螺钉（C2PS）固定在颈椎后路手术中广泛应用，但存在椎动脉损伤（VAI）风险，这是一种罕见但严重的并发症。本研究旨在确定C2PS置入过程中VAI的危险因素，并开发基于机器学习（ML）的预测模型，以加强术前风险评估。
回顾性分析280例行头颈部CT血管造影患者的临床和影像学资料。三维重建图像模拟C2PS置入，将患者分为损伤组（n = 98）和非损伤组（n = 182）。评估了15个变量，包括患者特征和解剖变量。训练了8种ML算法（70%训练队列）并进行验证（30%验证队列）。使用AUC、敏感性、特异性和SHAP（Shapley加性解释）评估模型性能以进行解释。
确定了六个关键危险因素：椎弓根直径、高位椎动脉（HRVA）、轴内椎动脉（IAVA）、椎动脉直径（VAD）、横突孔与椎体后端之间的距离（TFPEVB）以及椎动脉与椎体之间的距离（VAVB）。神经网络模型（NNet）表现出最佳预测性能，训练集AUC为0.929，验证集AUC为0.936。SHAP分析证实这些变量是VAI风险的主要因素。
本研究建立了C2PS置入过程中VAI的ML驱动预测模型，突出了六个关键的解剖和放射学危险因素。将该模型整合到临床工作流程中可优化术前规划、减少并发症并改善手术结果。有必要在多中心队列中进行外部验证以提高普遍性。
