以质量求发展，以服务铸品牌

Journal of Nursing ›› 2023, Vol. 30 ›› Issue (3): 57-62.doi: 10.16460/j.issn1008-9969.2023.03.057

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

ZHANG Hong-xia1, YANG Qiao-qiao2, DANG Chen-po2, ZHANG Wen-fang1, ZHANG Xiao-min1, SHAO Zhuan-lan1   

  1. 1. School of Nursing, Gansu University of Chinese Medicine, Lanzhou 730000, China;
    2. Dept. of Sports Medicine,No.940 Hospital of the Chinese People's Liberation Army Joint Security Force, Lanzhou 730050, China
  • Received:2022-09-30 Online:2023-02-10 Published:2023-03-14

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Abstract: 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.

Key words: senile osteoporosis, osteoporotic fractures, risk assessment, model construction, population health management

CLC Number: 

  • R473.59
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