老年骨质疏松性骨折再发风险预测模型构建

Establishment of predictive model for re-fracture in elderly osteoporotic fracture

目的探讨老年人首次发生骨质疏松性骨折后再发骨折的影响因素,并构建风险预测模型。方法选择在4家公立医院进行治疗的骨质疏松性骨折患者,按是否再发骨折分为再发骨折组(n=799)和非再发骨折组(n=1396),对两组资料进行对比,利用单因素分析和Cox比例风险模型分析骨质疏松性骨折患者再发骨折的影响因素,建立风险预测模型并绘制列线图,采用受试者工作曲线(receiver operating characteristic curve,ROC)曲线下面积(area under curve,AUC)及一致性指数评估模型。结果年龄、居住地、治疗方式、康复锻炼、既往病史、用药时间、跌倒风险、骨钙素、总胆固醇、碱性磷酸酶、同型半胱氨酸11个因素是骨质疏松性骨折再发的影响因素;预测模型验证结果显示,12、24和36个月建模组再发骨折风险ROC的AUC分别为0.790、0.804和0.801,验证组3个时间点AUC分别为0.787、0.779、0.801。模型一致性指数为0.86。结论保守治疗、农村居住、抗骨质疏松服药依从性差和跌倒高风险等可增加骨质疏松性骨折患者再发骨折的发生率,构建的模型可以较准确预测骨质疏松性骨折后患者再发骨折风险。

Objective To investigate the risk factors of re-fracture in osteoporotic fracture,and to establish a predictive model.Methods Osteoporotic fracture patients who underwent treatment in four public hospitals were selected and divided into a re-fracture group(n=799)and a none re-fracture group(n=1396),according to their fracture history after the first one.The clinical data were compared.Univariate analysis and multivariate Cox regression were used to analyze the independent risk factors of re-fracture.A risk prediction model was established,and a nomogram was drawn.The area under receiver operating characteristic curve(ROC)and C-index were used to evaluate the predictive effect of the model.Results The multivariate regression analysis showed that age,place of residence,treatment,rehabilitation,previous medical history,duration of medication,fall risk,osteocalcin,total cholesterol,alkaline phosphatase,homocysteine were independent risk factors of re-fracture.The area under curve(AUC)of developing group in 12,24,and 36 months was 0.790,0.804,and 0.801 respectively,and AUC of validation group in the three time points was 0.787,0.779,and 0.801 respectively.The C-index of the model was 0.86.Conclusions Conservative treatment,live in rural area,poor adherence to anti-osteoporosis medication,and high fall risk can increase the risk of re-fracture in patients with osteoporosis fractures.The model can accurately predict the risk of re-fractures in patient with primary osteoporotic fracture.