联合椎体骨质量构建一个新型绝经后女性骨质疏松性椎体压缩性骨折的预测模型  

Construction of a Novel Predictive Model of Vertebral Compression Fracture in Combination with VBQ in Postmenopausal Women

作　　者：王松 张鑫 肖箫 汪洪宇 郑俊勇 杨大志 龙厚清 彭松林[1,2,3] WANG Song;ZHANG Xin;XIAO Xiao;WANG Hongyu;ZHENG Junyong;YANG Dazhi;LONG Houqing;PENG Songlin(The Second Clinical Medical College,Jinan University,Shenzhen 518020,China;Department of Spine Surgery,Shenzhen Peoples Hospital(First Affiliated Hospital of Southern University of Science and Technology),Shenzhen 518020,China;Shenzhen Clinical Research Centre for Geriatrics,Shenzhen 518020,China;Shenzhen Key Laboratory of Musculoskeletal Tissue Reconstruction and Function Restoration,Shenzhen 518020,China)

机构地区：[1]暨南大学第二临床医学院,广东深圳518020 [2]深圳市人民医院(南方科技大学第一附属医院)脊柱外科,广东深圳518020 [3]深圳市骨科研究所,广东深圳518020 [4]深圳市运动系统组织与功能重建重点实验室,广东深圳518020

出　　处：《骨科》2023年第5期393-400,共8页ORTHOPAEDICS

基　　金：国家自然科学基金(82272488)。

摘　　要：目的联合对骨、骨微结构及椎旁肌肉的高效评估工具,建立绝经后女性骨质疏松性椎体压缩性骨折(osteoporotic vertebral compression fracture,OVCF)的新型预测模型。方法回顾性分析2020年11月至2022年11月就诊于深圳市人民医院脊柱外科的210例绝经后女性病人,将其随机分为模型构建集和验证人群集,组内根据是否发生OVCF分为OVCF组和N-OVCF组。模型构建集通过单因素统计分析,筛选出具有统计学差异的参数进行二元Logistic回归分析及受试者工作特征曲线(ROC)对比。根据统计结果,纳入独立危险因素建立预测模型,绘制Nomogram图。最后,通过比较模型构建集及验证人群集的C指数、校准曲线、决策性曲线验证模型的预测能力。结果单因素统计分析显示骨密度、定量CT值(QCT)、椎体骨质量(VBQ)、腰旁肌最大横截面积(CSA_(PS))、多裂肌与竖脊肌最大横截面积(CSA_(ES+MF))、年龄、骨特异性碱性磷酸酶(BAP)、身体质量指数(BMI)与绝经后女性发生OVCF有关(P<0.05)。二元Logistic回归分析结果显示骨密度(OR=0.266,P=0.003)、QCT值(OR=0.965,P=0.008)、VBQ(OR=4.346,P=0.044)、CSA_(ES+MF)(OR=0.806,P=0.028)、CSA_(PS)(OR=0.588,P=0.025)为绝经后女性发生OVCF的独立危险因素。根据ROC曲线分析,计算曲线下面积(AUC),骨密度(AUC=0.931,P<0.001)、QCT(AUC=0.890,P<0.001)、VBQ(AUC=0.784,P<0.001)、CSA_(ES+MF)(AUC=0.697,P<0.001)、CSA_(PS)(AUC=0.830,P<0.001)对OVCF的发生均有一定的预测效力,而通过ROC曲线对比发现骨密度、QCT、CSA_(PS)的曲线下面积(AUC)更高(P<0.05)。最终构建出的Nomogram图C指数为0.964(0.935~0.993),验证人群组C指数为0.872(0.802~0.941),通过绘制校准曲线、决策性曲线结果显示模型的拟合度及临床效用较好。结论本次研究发现骨密度、QCT、VBQ、CSA_(ES+MF)、CSA_(PS)五个参数为绝经后女性病人发生OVCF的独立危险因素,通过这五个参数绘制了Nomogram图,建立了一个临床�Objective To establish a novel prediction model for osteoporotic vertebral compression fracture(OVCF)in postmenopausal women by combining the efficient assessment tools for bone,bone microstructure and paravertebral muscle.Methods The retrospective study collected 210 postmenopausal female patients who attended the Department of Spine Surgery of Shenzhen People's Hospital(the Second Clinical Medical College of Jinan University and the First Affiliated Hospital of Southern University of Science and Technology)from November 2020 to November 2022.The patients were randomly divided into model set and validator set,and the set was divided into non⁃OVCF group and OVCF group according to whether OVCF occurred.The statistically different parameters were screened out from the Modeling population through univariate statistical analysis for binary Logistic regression analysis and ROC curve comparison.According to the statistical results,the prediction model was established by incorporating sensitive parameters and the Nomogram plot was drawn.Finally,the C⁃index,calibration curve and decision curve analyses of the model construction set and the validator set were compared to verify the predictive ability of the model.Results Univariate statistical analysis showed that bone mineral density(BMD),quantitative computed tomography(QCT),vertebral body quality(VBQ),cross section area of psoas muscle(CSA_(PS)),cross section area of erector spinae muscle and multifidus muscle(CSA_(ES+MF)),age,bone⁃specific alkaline phosphatase(BAP),body mass index(BMI)were associated with OVCF in postmenopausal women(P<0.05).Binary Logistic regression analysis showed that BMD(OR=0.266,P=0.003),QCT(OR=0.965,P=0.008),VBQ(OR=4.346,P=0.044),CSA_(ES+MF)(OR=0.806,P=0.028)and CSA_(PS)(OR=0.588,P=0.025)were independent risk factors for OVCF in postmenopausal women.According to ROC curve analysis,BMD(AUC=0.931,P<0.001),QCT(AUC=0.890,P<0.001),VBQ(AUC=0.784,P<0.001),CSA_(ES+MF)(AUC=0.697,P<0.001)and CSA_(PS)(AUC=0.830,P<0.001)all had a certain predictive

关 键 词：骨质疏松性椎体压缩骨折 骨质疏松症 椎体骨质量 定量计算机断层扫描 预测模型 

分 类 号：R683[医药卫生—骨科学]