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一个增强的骨折风险评估模型 (FREM) 使用国家关于发病率和药物的健康数据

Sören Möller^1,2, Marlene Rietz^1,3, Frederik Lykke Petersen²

¹Research Unit OPEN, Department of Clinical Research, University of Southern Denmark, 5000 Odense, Denmark.

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research

|October 31, 2025

概括

这项研究增强了骨折风险评估模型 (FREM) 以提高骨质疏松症骨折风险评估使用行政卫生数据. 更新的模型,FREMVer2,显示了机会性查的潜力,以更早地识别高风险个体.

科学领域:

老年学是指老年学的学科.
流行病学流行病学
医疗信息学医疗信息学

背景情况:

骨质疏松症对老年人构成重大健康风险,往往导致骨折和残疾.
目前的诊断方法经常在骨质疏松症的晚期发现,通常是在骨折发生后.
有效的案例寻找策略对于早期干预和预防骨折至关重要.

研究的目的:

扩展骨折风险评估模型 (FREM) 以更好地预测主要骨质疏松性骨折 (MOF) 和部骨折 (HF).
将发病率和药物数据纳入FREM以加强风险评估.
评估MOF和HF预测的特定年龄风险切断值.

主要方法:

利用了来自45岁以上的丹麦人口的行政健康数据 (N=2,493,180).
开发了FREMVer2使用后勤回归与LASSO规范化对诊断和处方从2007-2021年.
在性别和年龄分层的子组中验证了模型.

主要成果:

在FREMVer2中,MOF (0.656-0.714) 和HF (0.728-0.764) 的曲线下面积 (AUC) 达到了可接受的水平.
该模型在大多数子组中显示出高灵敏度 (约80%或更高).
使用的预测器可以在丹麦的全科医生电子健康记录中轻松获得.

结论:

FREMVer2是一个可行的决策支持系统,用于利用行政数据进行机会性骨质疏松症查.
改进的模型可以帮助识别患有骨质疏松性骨折高风险的个体.
这种方法支持在医疗机构及时评估风险和启动治疗.

关键词:

管理卫生数据管理卫生数据自动化风险计算自动化风险计算决策支持工具决策支持工具一般的做法一般的做法.部骨折骨折部骨折部骨折严重的骨质疏松性骨折骨质疏松症是一种骨质疏松症.登记数据登记数据登记风险预测风险预测查检查查检查查检查查检查

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