OPEN-Odense Patient Data Explorative Network, Department of Clinical Research, University of Southern Denmark, and Odense University Hospital, Odense, Denmark.
National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark.
J Bone Miner Res. 2018 Nov;33(11):1967-1979. doi: 10.1002/jbmr.3528. Epub 2018 Aug 22.
Some conditions are already known to be associated with an increased risk of osteoporotic fractures. Other conditions may also be significant indicators of increased risk. The aim of the current study was to identify conditions for inclusion in a fracture prediction model (fracture risk evaluation model [FREM]) for automated case finding of high-risk individuals of hip or major osteoporotic fractures (MOFs). We included the total population of Denmark aged 45+ years (N = 2,495,339). All hospital diagnoses from 1998 to 2012 were used as possible conditions; the primary outcome was MOFs during 2013. Our cohort was split randomly 50/50 into a development and a validation dataset for deriving and validating the predictive model. We applied backward selection on ICD-10 codes (International Classification of Diseases and Related Health Problems, 10th Revision) by logistic regression to develop an age-adjusted and sex-stratified model. The FREM for MOFs included 38 and 43 risk factors for women and men, respectively. Testing FREM for MOFs in the validation cohort showed good accuracy; it produced receiver-operating characteristic (ROC) curves with an area under the ROC curve (AUC) of 0.750 (95% CI, 0.741 to 0.795) and 0.752 (95% CI, 0.743 to 0.761) for women and men, respectively. The FREM for hip fractures included 32 risk factors for both genders and showed an even higher accuracy in the validation cohort as AUCs of 0.874 (95% CI, 0.869 to 0.879) and 0.851 (95% CI, 0.841 to 0.861) for women and men were found, respectively. We have developed and tested a prediction model (FREM) for identifying men and women at high risk of MOFs or hip fractures by using solely existing administrative data. The FREM could be employed either at the point of care integrated into electronic patient record systems to alert physicians or deployed centrally in a national case-finding strategy where patients at high fracture risk could be invited to a focused DXA program. © 2018 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research (ASBMR).
一些病症已被证实与骨质疏松性骨折风险增加有关。其他病症也可能是增加风险的重要指标。本研究的目的是确定纳入骨折预测模型（骨折风险评估模型 [FREM]）的病症，以自动发现髋部或主要骨质疏松性骨折（MOF）高危人群。我们纳入了丹麦所有年龄在 45 岁以上的人群（N=2,495,339）。使用 1998 年至 2012 年的所有医院诊断作为可能的病症；主要结局是 2013 年的 MOF。我们的队列随机分为 50/50 的开发和验证数据集，用于推导和验证预测模型。我们应用逻辑回归对 ICD-10 代码（国际疾病分类和相关健康问题，第 10 版）进行向后选择，以开发一个年龄调整和性别分层的模型。MOF 的 FREM 分别为女性和男性纳入 38 个和 43 个风险因素。在验证队列中测试 MOF 的 FREM 显示出良好的准确性；它产生的接收者操作特征（ROC）曲线的 ROC 曲线下面积（AUC）分别为 0.750（95%CI，0.741 至 0.795）和 0.752（95%CI，0.743 至 0.761）。髋部骨折的 FREM 包括两性的 32 个风险因素，在验证队列中表现出更高的准确性，女性的 AUC 为 0.874（95%CI，0.869 至 0.879），男性为 0.851（95%CI，0.841 至 0.861）。我们已经开发并测试了一种预测模型（FREM），仅使用现有的行政数据来识别 MOF 或髋部骨折风险较高的男性和女性。FREM 可以在护理点集成到电子患者记录系统中以提醒医生使用，也可以在中央部署全国病例发现策略，在该策略中，可以邀请高骨折风险的患者参加重点 DXA 计划。
