Major osteoporosis fracture prediction in type 2 diabetes: a derivation and comparison study

弗雷克斯 医学 内科学 2型糖尿病 骨质疏松症 危险系数 队列 人口 髋部骨折 队列研究 糖尿病 物理疗法 骨质疏松性骨折 置信区间 内分泌学 骨矿物 环境卫生
作者
Xiao-ke Kong,Zhiyun Zhao,Zongquan Deng,Rui Xie,Lin Sun,Hongyan Zhao,Guang Ning,Weiqing Wang,Jianmin Liu,Bei Tao
出处
期刊:Osteoporosis International [Springer Nature]
卷期号:33 (9): 1957-1967 被引量:3
标识
DOI:10.1007/s00198-022-06425-8
摘要

The widely recommended fracture prediction tool FRAX was developed based on and for the general population. Although several adjusted FRAX methods were suggested for type 2 diabetes (T2DM), they still need to be evaluated in T2DM cohort.This study was undertaken to develop a prediction model for Chinese diabetes fracture risk (CDFR) and compare its performance with those of FRAX.In this retrospective cohort study, 1730 patients with T2DM were enrolled from 2009.08 to 2013.07. Major osteoporotic fractures (MOFs) during follow-up were collected from Electronic Health Records (EHRs) and telephone interviews. Multivariate Cox regression with backward stepwise selection was used to fit the model. The performances of the CDFR model, FRAX, and adjusted FRAX were compared in the aspects of discrimination and calibration.6.3% of participants experienced MOF during a median follow-up of 10 years. The final model (CDFR) included 8 predictors: age, gender, previous fracture, insulin use, diabetic peripheral neuropathy (DPN), total cholesterol, triglycerides, and apolipoprotein A. This model had a C statistic of 0.803 (95%CI 0.761-0.844) and calibration χ2 of 4.63 (p = 0.86). The unadjusted FRAX underestimated the MOF risk (calibration χ2 134.5, p < 0.001; observed/predicted ratio 2.62, 95%CI 2.17-3.08), and there was still significant underestimation after diabetes adjustments. Comparing FRAX, the CDFR had a higher AUC, lower calibration χ2, and better reclassification of MOF.The CDFR model has good performance in 10-year MOF risk prediction in T2DM, especially in patients with insulin use or DPN. Future work is needed to validate our model in external cohort(s).
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