中国组织工程研究

中国组织工程研究 ›› 2026, Vol. 30 ›› Issue (11): 2727-2735.doi: 10.12307/2026.102

• 脊柱组织构建 spinal tissue construction • 上一篇    下一篇

青少年特发性脊柱侧弯进展的影响因素及列线图预测模型构建

李  洁1，2，赵晓峰3，曾  琪1，2，周润田3，陈  容1，胡希鉴3，赵  斌3   

  1. 山西医科大学，1医学科学院，2公共卫生学院流行病学教研室，3第二医院骨科，山西省太原市   030001


  • 收稿日期:2025-04-03 接受日期:2025-06-05 出版日期:2026-04-18 发布日期:2025-09-05
  • 通讯作者: 赵斌，博士，主任医师，山西医科大学第二医院骨科，山西省太原市 030001
  • 作者简介:李洁,女，1997年生，陕西省宝鸡市人，汉族，山西医科大学在读硕士，主要从事流行病与卫生统计方面的研究。
  • 基金资助:
    山西省卫生健康委科研课题(2020073)，项目负责人：赵斌

Influencing factors of spine deformity progression in adolescent idiopathic scoliosis and construction of a joint prediction model and nomogram

Li Jie1, 2, Zhao Xiaofeng3, Zeng Qi1, 2, Zhou Runtian3, Chen Rong1, Hu Xijian3, Zhao Bin3   

  1. 1Academy of Medical Sciences, Shanxi Medical University, Taiyuan 030001, Shanxi Province, China; 2Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, Shanxi Province, China; 3Department of Orthopedics, The Second Hospital of Shanxi Medical University, Taiyuan 030001, Shanxi Province, China
  • Received:2025-04-03 Accepted:2025-06-05 Online:2026-04-18 Published:2025-09-05
  • Contact: Zhao Bin, MD, Chief physician, Department of Orthopedics, The Second Hospital of Shanxi Medical University, Taiyuan 030001, Shanxi Province, China
  • About author:Li Jie, MS candidate, Academy of Medical Sciences, Shanxi Medical University, Taiyuan 030001, Shanxi Province, China; Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, Shanxi Province, China
  • Supported by:
    Scientific Research Project of Shanxi Provincial Health Commission, No. 2020073 (to ZB) 

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摘要：


文题释义：
LASSO回归：最小绝对收缩和选择算子(LASSO)是一种用于线性回归的正则化技术，通过引入惩罚系数λ，随着λ的增加，使变量回归系数逐步向零收缩。对于不重要的变量系数将较快削减为0，而对重要变量系数的影响较小。能够在控制模型复杂度的同时，筛选出重要的影响因素。
列线图预测模型：列线图是一种便捷高效的可视化预后评估工具，它提供了友好的用户交互界面，综合考虑各影响因素对结果的影响权重，通过对每一个影响因素进行赋分，使患者的总得分能够直观反映其预测结果的概率，为临床医生和患者提供个性化的疾病风险预测或生存预测。

背景：建立青少年特发性脊柱侧弯患者首次诊断后未来不同时点发生侧弯进展的预测模型，以期在疾病发展早期个体化精准识别和预测进展风险。
目的：探究青少年特发性脊柱侧弯首次确诊后侧弯进展的影响因素，并构建列线图风险预测模型。
方法：回顾性分析2019年1月至2023年6月于山西医科大学第二医院首次确诊青少年特发性脊柱侧弯患者的临床资料，并追踪随访至侧弯进展即主弯Cobb角进展≥6°、末次来访或截止日期(2023年6月)。按照8∶2随机拆分为训练集和验证集，以患者是否发生侧弯进展分为进展组与非进展组。采用最小绝对收缩和选择算子(LASSO)-Cox回归分析探究青少年特发性脊柱侧弯发生侧弯进展的独立影响因素，并构建基于Cox回归算法的列线图风险预测模型，以受试者工作特征曲线的曲线下面积、校准曲线、决策曲线分析对模型区分度、准确性和临床应用价值进行验证和评价。
结果与结论：①共纳入符合标准的294例青少年特发性脊柱侧弯患者，进展率为41.84%；②LASSO-Cox回归分析结果显示，初诊年龄≥14.5岁、支具治疗、Risser征≥1级、顶椎偏移> 1.6 cm、T1椎体倾斜角≥1.2°为侧弯进展的保护因素，而初诊Cobb角> 16.5°、顶椎旋转度≥2度、脊柱增长速率> 4.5 cm/年为侧弯进展的危险因素；③根据以上因素构建的列线图模型有出色的预测能力和临床意义，训练集和验证集6，12，18，24个月的曲线下面积值分别为0.731，0.852，0.855，0.843和0.766，0.850，0.850，0.830，C-index分别为0.795和0.771，模型区分度好；校准曲线显示实际观测结果与预测结果拟合较好，模型准确度高；此外，决策曲线分析表明使用模型做出临床决策将为患者带来净收益；④此次研究构建的青少年特发性脊柱侧弯进展风险预测列线图模型可使用简单变量判断患者在未来不同时点发生进展的风险概率，进而指导临床医生选择更加合理的治疗方式。
https://orcid.org/0009-0005-3351-4600 (李洁)；https://orcid.org/0000-0001-5360-6725 (赵斌) 

中国组织工程研究杂志出版内容重点：干细胞；骨髓干细胞；造血干细胞；脂肪干细胞；肿瘤干细胞；胚胎干细胞；脐带脐血干细胞；干细胞诱导；干细胞分化；组织工程

关键词: 青少年特发性脊柱侧弯, 侧弯进展, 影响因素, LASSO, Cox回归, 预测模型, 列线图

Abstract: BACKGROUND: To establish a model for predicting the progression of scoliosis at different time points after the first diagnosis of adolescent idiopathic scoliosis contributes to accurately identify and predict the risks of progression in the early stage of disease development.
OBJECTIVE: To explore the factors influencing the progression of scoliosis in patients with adolescent idiopathic scoliosis after first diagnosis, and to construct a nomogram risk prediction model.  
METHODS: The clinical data of patients with adolescent idiopathic scoliosis who were first diagnosed at the Second Hospital of Shanxi Medical University from January 2019 to June 2023 were retrospectively analyzed and followed up to the progress of lateral bending, that is, the progress of main bend Cobb angle ≥ 6°, the last visit or cutoff date (June 2023). The patients were randomly divided at the rate of 8:2 into training set and verification set, and the patients were divided into progressive group and non-progressive group according to whether the patients had lateral bending progress or not. Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression analyses were used to investigate the independent influences on the occurrence of scoliosis progression in adolescent idiopathic scoliosis and to construct a Cox regression algorithm-based risk prediction model for column-line diagrams based on the area under the curve (AUC) of the receiver operating characteristic curve of the subjects, calibration curve, and decision curve were used to validate and evaluate the differentiation, accuracy, and clinical application value of the model.  
RESULTS AND CONCLUSION: (1) A total of 294 patients with adolescent idiopathic scoliosis were included in this study, with a progression rate of 41.84%. (2) The results of LASSO-Cox regression analysis showed that age at first diagnosis ≥ 14.5 years, brace treatment, Risser’s sign ≥ 1 grade, parietal cone offset > 1.6 cm, and thoracic 1 cone tilt angle > 1.2° were protective factors for scoliosis progression, whereas age at first diagnosis Cobb’s angle > 16.5°, parietal cone rotation ≥ 2 degrees, and spinal growth rate > 4.5 cm/year were risk factors for scoliosis progression. (3) The nomogram model constructed according to the above factors had excellent predictive ability and clinical significance. The AUC values of 6, 12, 18 and 24 months in the training set and verification set were 0.731, 0.852, 0.855, 0.843 and 0.766, 0.850, 0.850 and 0.830, respectively, and the C-index of the model was 0.795 and 0.771 respectively, indicating that the model has good discrimination. The calibration curve showed that the actual observation results fitted well with the predicted results, and the accuracy of the model was high. In addition, the decision curve showed that the use of the model to make clinical decisions could bring net benefits to patients. Therefore, the adolescent idiopathic scoliosis progression risk prediction nomogram model constructed in this study can use simple variables to judge the risk probability of patients' progression at different time points in the future, and then guide clinicians to choose a more reasonable treatment.


Key words: adolescent idiopathic scoliosis, scoliosis progress, influencing factors, LASSO, Cox regression, prediction model, nomogram 

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