局解手术学杂志2026，Vol.35Issue(4)：303-307,5.DOI:10.11659/jjssx.04E025107
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
Analysis of risk factors and construction of a prediction model for osteoporotic vertebral compression fracture
李俊潮 1王洪伟 2韩康恩 1邢乐 2胡寅 1顾洪闻 2张智昊 2于海龙2
作者信息
- 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
- 折叠
摘要
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%CI: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
骨质疏松性椎体压缩性骨折/机器学习/预测模型/动态列线图Key words
osteoporotic vertebral compression fracture/machine learning/prediction model/dynamic nomogram分类
医药卫生引用本文复制引用
李俊潮,王洪伟,韩康恩,邢乐,胡寅,顾洪闻,张智昊,于海龙..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(4):303-307,5.
