Khodaei Mahdieh, Parent Eric C, Le Lawrence H, Stampe Kyle, Southon Hryniuk Sarah, Lou Edmond
University of Alberta, Edmonton, Canada.
Alberta Health Services, Edmonton, Canada.
Eur Spine J. 2025 Jul;34(7):2653-2661. doi: 10.1007/s00586-025-08922-w. Epub 2025 May 21.
This study aimed to identify and validate prognostic factors to predict scoliosis progression.
One hundred sixty-two girls, aged 13.5 ± 1.7 years old, diagnosed with idiopathic scoliosis were recruited. One hundred were used for model development, and 62 for testing, in which the number of progression cases was 25 and 11, respectively. All participants were scanned by an ultrasound (US) system for two consecutive visits (baseline and follow-up). The baseline parameters included (a) demographic: age, body mass index (BMI), and menarche status; (b) radiographic: X-ray Cobb angle, number-of-curve (NOC), and Risser sign; (c) ultrasonic: US Cobb, maximum axial vertebral rotation (AVR), Cobb angle at the plane of maximum curvature (PMC), kyphotic angle (KA) and reflection coefficient (RC) index. For the follow-up visit, only the US Cobb was recorded. The demographic and X-ray parameters were extracted from the scoliosis clinical records. The US parameters were measured by trained raters with good measurement reliability. The prediction model was developed using logistic regression analysis.
The final predictors were US Cobb change, RC index, and NOC, and the probability of curve progression risk was Log (p/1-p) = -1.40 + 0.28(US Cobb change)-39.45(RC) + 1.36 (NOC). The model achieved sensitivity, specificity, and accuracy of 90% on the 62 test dataset, which was much better than using US Cobb change only.
The results indicated that participants with lower RC (weaker bone, RC ≤ 0.06), larger US Cobb change (≥ 5°), and multiple curvatures (NOC > 1) were at a higher risk of curve progression. A large study including more progression cases is needed for further validation.
本研究旨在识别和验证预测脊柱侧弯进展的预后因素。
招募了162名年龄在13.5±1.7岁、诊断为特发性脊柱侧弯的女孩。其中100名用于模型开发,62名用于测试,进展病例数分别为25例和11例。所有参与者均通过超声(US)系统进行连续两次检查(基线和随访)。基线参数包括:(a)人口统计学参数:年龄、体重指数(BMI)和月经初潮状态;(b)影像学参数:X线Cobb角、侧弯数(NOC)和Risser征;(c)超声参数:US Cobb角、最大轴向椎体旋转度(AVR)、最大曲率平面处的Cobb角(PMC)、后凸角(KA)和反射系数(RC)指数。随访时仅记录US Cobb角。人口统计学和X线参数从脊柱侧弯临床记录中提取。US参数由训练有素、测量可靠性良好的评估人员测量。采用逻辑回归分析建立预测模型。
最终的预测因素为US Cobb角变化、RC指数和NOC,曲线进展风险概率为Log(p/1 - p)=-1.40 + 0.28(US Cobb角变化)- 39.45(RC)+ 1.36(NOC)。该模型在62个测试数据集上的敏感性、特异性和准确性达到了90%,远优于仅使用US Cobb角变化的情况。
结果表明,RC较低(骨质较弱,RC≤0.06)、US Cobb角变化较大(≥5°)和存在多个弯曲(NOC>1)的参与者曲线进展风险较高。需要开展纳入更多进展病例的大型研究进行进一步验证。