Analysis of risk factors and construction of a prediction model for osteoporotic vertebral compression fracture

LI Jun-chao; WANG Hong-wei; HAN Kang-en; XING Le; HU Yin; GU Hong-wen; ZHANG Zhi-hao; YU Hai-long

doi:10.11659/jjssx.04E025107

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Analysis of risk factors and construction of a prediction model for osteoporotic vertebral compression fracture

Orthopedics Column | 更新时间：2026-04-20

- Analysis of risk factors and construction of a prediction model for osteoporotic vertebral compression fracture
- JOURNAL OF REGIONAL ANATOMY AND OPERATIVE SURGERY Vol. 35, Issue 4, Pages: 303-307(2026)
- 作者机构：
  
  1.大连医科大学研究生院，辽宁大连 116044
  2.北部战区总医院骨科，辽宁沈阳 110016
- 作者简介：
- 基金信息：
- DOI：10.11659/jjssx.04E025107
  CLC： R683.2
- Received：21 April 2025，
  
  Published：25 April 2026
- 稿件说明：
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李俊潮,王洪伟,韩康恩等.骨质疏松性椎体压缩性骨折的风险因素分析及预测模型构建[J].局解手术学杂志,2026,35(04):303-307.

LI Jun-chao,WANG Hong-wei,HAN Kang-en,et al.Analysis of risk factors and construction of a prediction model for osteoporotic vertebral compression fracture[J].JOURNAL OF REGIONAL ANATOMY AND OPERATIVE SURGERY,2026,35(04):303-307.
李俊潮,王洪伟,韩康恩等.骨质疏松性椎体压缩性骨折的风险因素分析及预测模型构建[J].局解手术学杂志,2026,35(04):303-307. DOI： 10.11659/jjssx.04E025107.

LI Jun-chao,WANG Hong-wei,HAN Kang-en,et al.Analysis of risk factors and construction of a prediction model for osteoporotic vertebral compression fracture[J].JOURNAL OF REGIONAL ANATOMY AND OPERATIVE SURGERY,2026,35(04):303-307. DOI： 10.11659/jjssx.04E025107.

摘要

目的

应用机器学习技术开发骨质疏松性椎体压缩性骨折（OVCF）的风险预测模型，并阐释其决策机制，以促进OVCF的早期风险识别。

方法

将2022年1月至2024年12月于北部战区总医院经腰椎MRI证实为OVCF的188例患者纳入病例组，同期未骨折的394例患者作为对照组。通过R语言整合数据，以7∶3的比例分配为训练队列（408例）与验证队列（174例），并筛选出关键预测因子，构建十种机器学习模型，结合SHAP对最优模型进行分析使模型预测可视化并分析模型决策逻辑。

结果

通过单因素及多因素分析筛选出性别、外伤史、年龄、饮酒史及体质量指数（BMI）均为OVCF的预测因子。通过十种机器学习证明

Logistic

回归模型的受试者工作特征曲线下面积（

AUC

）值最高（

AUC

=0.851，95%

:0.790~0.911），在风险阈值范围标准化净收益稳定，预测概率与实际概率一致性较高，

Logistic

回归模型为最优模型；基于SHAP构建的可视化分析显示，外伤史、性别、饮酒史、年龄及BMI是对模型预测贡献最大的关键特征。

结论

基于机器学习模型可显著提升OVCF诊断效能，

Logistic

回归结合列线图工具有助于临床决策支持。

Abstract

Objective

To develop a risk prediction model for osteoporotic vertebral compression fracture （OVCF） using machine learning （ML） techniques and to interpret its decision-making mechanism， and thereby facilitating early risk identification of OVCF.

Methods

A total of 188 patients confirmed with OVCF via lumbar MRI at General Hospital of Northern Theater Command from January 2022 to December 2024 were enrolled as the case group， while 394 patients without fractures during the same period were included as the control group. Data were integrated using R language and randomly divided into training cohort （408 patients） and validation cohort （174 patients） at a ratio of 7∶3. The key predictive factors were selected to construct ten machine learning models. The optimal model was analyzed in combination with SHAP to visualize model predictions， and interpret its decision-making logic.

Results

Univariate and multivariate analysis identified that gender， history of trauma， age， history of alcohol consumption， and body mass index （BMI） were the predictive factors for OVCF. Ten machine learning models demonstrated that the

Logistic

regression model achieved the highest value of

area under the receiver operating characteristic curve

（

AUC

）（

AUC

=0.851， 95%

： 0.790 to 0.911）， with stable

standardized net benefits across the risk threshold range and high consistency between the predicted probabilities and actual probabilities. The

Logistic

regression model was identified as the optimal model. Additionally， SHAP-based visualization model revealed that the history of trauma， gender， history of alcohol consumption， age， and BMI were the key factors.

Conclusion

Machine learning-based models can significantly improve the diagnostic efficacy of OVCF， and

Logistic

regression combined with a nomogram tool is conducive to clinical decision support.

关键词

Keywords

references

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