文献类型：期刊文献

中文题名：老年性骨质疏松患者骨折风险预测工具的范围综述

英文题名：Fracture risk prediction tools in elderly patients with osteoporosis:a scoping review

作者：张红霞[1];杨巧巧[2];党晨珀[2];张文芳[1];张晓敏[1];邵转兰[1]

第一作者：张红霞

机构：[1]甘肃中医药大学护理学院,甘肃兰州730000;[2]中国人民解放军联勤保障部队第九四〇医院运动医学科,甘肃兰州730050

第一机构：甘肃中医药大学护理学院

年份：2023

卷号：30

期号：3

起止页码：57

中文期刊名：护理学报

外文期刊名：Journal of Nursing(China)

收录：CSTPCD;;北大核心:【北大核心2020】；

基金：甘肃省青年科技基金计划(20JR10RA008);甘肃省卫生行业科研计划项目(GSWSKY-2019-12)。

语种：中文

中文关键词：老年性骨质疏松;骨质疏松性骨折;风险预测;模型构建;人口健康管理

外文关键词：senile osteoporosis;osteoporotic fractures;risk assessment;model construction;population health management

摘要：目的综合分析老年性骨质疏松患者骨折风险预测工具,为研究人员开发或引入符合本国国情的预测工具提供参考。方法采用范围综述方法,检索PubMed、Embase、Web of Science、中国知网、万方数据知识服务平台、维普中文科技期刊数据及中国生物医学文献数据库7个中英文数据库,由2名研究者独立筛选文献和提取数据,并进行偏倚风险及适用性评价。结果最后纳入18篇英文文献,包括12项预测工具开发研究及6项预测工具效能验证研究,共涉及12个老年性骨质疏松患者骨折风险预测工具,工具类型主要为风险预测模型及风险评估表。结论老年性骨质疏松患者骨折风险预测工具种类繁多,预测性能良好但总体偏倚风险较高。相关研究人员一方面应对现有的预测工具进行验证及校准,另一方面应基于本土数据开发低偏倚风险、高临床适用性的风险预测工具,为老年性骨质疏松患者的精准健康管理提供参考。
Objective To comprehensively analyze fracture risk prediction tools in senile osteoporosis patients,and provide reference for researchers to develop or introduce prediction tools in line with national conditions.Methods Scoping review was conducted and we searched 7 Chinese and English databases including PubMed,Embase,Web of Science,CNKI,Wanfang Data Knowledge Service Platform,VIP Chinese scientific journal data and China Biomedical Literature Database for eligible literature.Two researchers screened the literature and conducted data extraction,and assessment of risk of bias and applicability independently.Results Eighteen pieces of English literature were included,including 12 prediction tool development studies and 6 prediction tool efficacy validation studies,involving 12 fracture risk prediction tools in elderly patients with osteoporosis.The types of tools were mainly risk prediction models and risk assessment tables.Conclusion There are various kinds of fracture risk prediction tools for elderly patients with osteoporosis,with good prediction performance,but overall high risk of bias.The existing prediction tools should be verified and calibrated.Local data-based risk prediction tools with low risk of bias and high clinical applicability should be developed,so as to provide reference for the precise health management of elderly patients with osteoporosis.