期刊
AGING AND DISEASE
卷 13, 期 4, 页码 1215-1238出版社
INT SOC AGING & DISEASE
DOI: 10.14336/AD.2021.1206
关键词
osteoporotic fractures; prediction model; systematic review; critical appraisal
资金
- Special Funding for the Construction of Innovative Provinces in Hunan [2019SK2141]
- National Key R&D Program of China [2020YFC2008600]
- China Oceanwide Holding Group Project Fund [143010100]
- High-level Talents Introduction Plan from Central South University [502045003]
This systematic review evaluated the performance and methodological quality of osteoporotic fracture risk prediction models. The results showed that there are a certain number of models available, but few of them have been thoroughly validated and all models have methodological shortcomings. Future research should focus on validating, improving, and analyzing the impact of existing models.
Osteoporotic fractures (OF) are a global public health problem currently. Many risk prediction models for OF have been developed, but their performance and methodological quality are unclear. We conducted this systematic review to summarize and critically appraise the OF risk prediction models. Three databases were searched until April 2021. Studies developing or validating multivariable models for OF risk prediction were considered eligible. Used the prediction model risk of bias assessment tool to appraise the risk of bias and applicability of included models. All results were narratively summarized and described. A total of 68 studies describing 70 newly developed prediction models and 138 external validations were included. Most models were explicitly developed (n=31, 44%) and validated (n=76, 55%) only for female. Only 22 developed models (31%) were externally validated. The most validated tool was Fracture Risk Assessment Tool. Overall, only a few models showed outstanding (n= 3, 1%) or excellent (n=32, 15%) prediction discrimination. Calibration of developed models ( n=25, 36%) or external validation models (n=33, 24%) were rarely assessed. No model was rated as low risk of bias, mostly because of an insufficient number of cases and inappropriate assessment of calibration. There are a certain number of OF risk prediction models. However, few models have been thoroughly internally validated or externally validated (with calibration being unassessed for most of the models), and all models showed methodological shortcomings. Instead of developing completely new models, future research is suggested to validate, improve, and analyze the impact of existing models.
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