Zhou Chao, Yin Jun, Wang Yanguo, Cong Wei
Department of Spine Surgery, Qingdao Medical Engineering Interdisciplinary Key Laboratory, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, Shandong, China.
Front Surg. 2025 Jul 14;12:1619360. doi: 10.3389/fsurg.2025.1619360. eCollection 2025.
To explore the clinical value of a surgical effect prediction model for patients with lumbar spinal canal stenosis and degenerative scoliosis (LSS-DS). The model is based on the spine-pelvis compensation state measured by a three-dimensional gait system.
A total of 215 patients with LSS-DS who underwent surgery from January 2022 to December 2024 were enrolled. They were randomly divided into a training set ( = 151) and a validation set ( = 64) at a 7:3 ratio. Spine and pelvis parameters were measured using a three-dimensional gait system. Multivariate logistic regression analysis was used to screen independent predictors of surgical effect, and a nomogram model was constructed.
In the training cohort, 35 cases (23.18%) had suboptimal surgical outcomes, while the validation cohort showed 15 cases (23.44%) with unsatisfactory results ( = 0.872, χ = 0.006). Multivariate analysis identified the Cobb angle of scoliosis, preoperative sagittal vertical axis, pelvic incidence-lumbar lordosis difference (PI-LL), pace, step size, affected lower extremity support time proportion, and preoperative VAS score as independent risk factors ( < 0.05). The nomogram model had a C-index of 0.852 and 0.849 in the training and validation sets, respectively. The AUC values were 0.860 (95% CI: 0.768-0.953) and 0.856 (95% CI: 0.712-0.980), with sensitivities/specificities of 0.759/0.896 and 0.572/0.500.
The nomogram model based on spine-pelvis compensation can effectively predict surgical outcomes in LSS-DS patients. It provides a basis for individualized treatment.
探讨腰椎管狭窄症合并退变性脊柱侧凸（LSS-DS）患者手术效果预测模型的临床价值。该模型基于三维步态系统测量的脊柱-骨盆代偿状态。
纳入2022年1月至2024年12月期间接受手术的215例LSS-DS患者。按照7:3的比例将他们随机分为训练集（n = 151）和验证集（n = 64）。使用三维步态系统测量脊柱和骨盆参数。采用多因素逻辑回归分析筛选手术效果的独立预测因素，并构建列线图模型。
在训练队列中，35例（23.18%）手术效果欠佳，而验证队列中有15例（23.44%）结果不理想（P = 0.872，χ² = 0.006）。多因素分析确定脊柱侧凸的Cobb角、术前矢状垂直轴、骨盆入射角-腰椎前凸角差（PI-LL）、步速、步长、患侧下肢支撑时间比例和术前VAS评分是独立危险因素（P < 0.05）。列线图模型在训练集和验证集中的C指数分别为0.852和0.849。AUC值分别为0.860（95%CI：0.768 - 0.953）和0.856（95%CI：0.712 - 0.980），敏感度/特异度分别为0.759/0.896和0.572/0.500。
基于脊柱-骨盆代偿的列线图模型能够有效预测LSS-DS患者的手术效果。它为个体化治疗提供了依据。
