摘要
目的探讨绝经后老年女性发生骨质疏松性椎体压缩骨折(OVCFs)的相关危险因素,开发并验证骨折风险预测模型和Nomogram图(诺模图)。方法选取2020年9月至2022年3月在锦州医科大学附属第一医院就诊的331例女性骨质疏松症患者作为研究对象,根据是否合并椎体压缩性骨折分为骨折组(169例)和非骨折组(162例)。收集两组患者临床基线资料,包括年龄、体质量指数、跌倒史、骨折史、吸烟史、饮酒史、日常活动<30 min、腰椎骨密度(BMD)、股骨BMD及实验室检查结果。采用电化学发光法检测骨转换标志物:Ⅰ型胶原氨基端肽(PINP)、Ⅰ型胶原羧基端肽(CTX)、25-羟基维生素D[25(OH)D]、骨碱性磷酸酶和骨钙素N端片段;骨代谢相关指标:钙、磷和甲状旁腺素。采用单因素分析、多因素Logistic回归分析,筛选独立危险因素,建立预测模型和诺模图。计算C指数(C-index值)、受试者工作特征曲线下面积(AUC);绘制校准图和决策曲线判断模型区分度、校准度和临床实用性。结果多因素Logistic回归分析结果显示,患者年龄(OR=1.110)、跌倒史(OR=1.828)、日常活动<30 min(OR=1.878)、腰椎BMD(OR=1.789)、CTX(OR=1.004)、PINP(OR=1.250)水平升高是OVCFs发生的独立危险因素(P<0.05);25(OH)D(OR=0.866)水平升高是OVCFs发生的保护因素(P<0.05)。预测模型AUC=0.867,灵敏度和特异度分别为0.858、0.728。预测模型和Bootstrap内部验证的C-index值分别为0.867、0.870。结论年龄、腰椎BMD、跌倒史、日常活动<30 min和血清CTX、PINP水平升高将增加绝经后老年女性OVCFs发生的风险。诺模图可以准确、方便地对绝经后OVCFs发生的风险进行预测,为临床诊治提供理论依据及相应措施。
Objective To analyze the related risk factors for osteoporotic vertebral compression fractures(OVCFs)in postmenopausal elderly women and to test prediction model and Nomogram evaluation tool for OVSFs.Methods 331 female patients with osteoporosis who visited the First Affiliated Hospital of Jinzhou Medical University from September 2020 to March 2022 were selected as the study subjects.All eligible patients were divided into fracture group(169 cases)and non fracture group(162 cases)according to whether there was vertebral compression fracture.Clinical baseline data were collected for both groups of patients,including age,body mass index,history of falls,fracture history,smoking history,drinking history,daily activity<30 minutes,lumbar bone mineral density(BMD),femoral BMD,and laboratory examination results.The markers of bone turnover were detected by electrochemiluminescence method:typeⅠcollagen amino terminal peptide(PINP),typeⅠcollagen carboxy terminal peptide(CTX),25-hydroxyvitamin D[25(OH)D],bone alkaline phosphatase,and N-terminal fragment of osteocalcin;Related indicators of bone metabolism:calcium,phosphorus,and parathyroid hormone.Single factor analysis and multivariate Logistic regression analysis were used to screen independent risk factors and establish prediction models and nomographs.The C index and the area under the receiver operating characteristic curve(AUC)were calculate.Calibration charts and decision curves to determine model differentiation,calibration,and clinical applicability were drawn.Results Multivariate Logistic regression analysis showed that the patient′s age(OR=1.110),history of falls(OR=1.828),daily activities<30 min(OR=1.878),elevated levels of lumbar spine BMD(OR=1.789),CTX(OR=1.004),PINP(OR=1.250)were independent risk factors for the happening of OVCFs(P<0.05),and 25(OH)D(OR=0.866)levels were independent protective factors for fracture(P<0.05),and they were statistically significant.The model AUC value was 0.867 and the sensitivity and specificity were 0.858 and 0.728,and the optimal cutoff value was 0.566.The C-index values for the prediction model and Bootstrap internal validation are 0.867 and 0.870,respectively.Conclusion Age,lumbar BMD,history of falls,daily activity<30 minutes,and elevated serum CTX,PINP,and 25(OH)D levels increase the risk of OVCFs in postmenopausal elderly women.The Nomogram can conveniently predict the risk of vertebral fracture in OVCFs osteoporosis patients and provide theoretical basis and corresponding measures for clinical diagnosis and treatment.
作者
马季
智晓东
王浩
王伟
MA Ji;ZHI Xiaodong;WANG Hao;WANG Wei(Department of Orthopedics,First Affiliated Hospital of Jinzhou Medical University,Jinzhou,Liaoning 121000,China;Liaoning Provincial Key Laboratory of Medical Tissue Engineering,Jinzhou,Liaoning 121000,China;Institute of Orthopaedic Sciences,Jinzhou Medical University,Jinzhou,Liaoning 121000,China)
出处
《检验医学与临床》
CAS
2023年第8期1057-1062,1067,共7页
Laboratory Medicine and Clinic
基金
锦州医科大学校企合作基金项目(2020002)。
