Novel algorithm generating strategy to identify high fracture risk population using a hybrid intervention threshold

Chung Yuan Hsu, Chih Hsing Wu, Shan Fu Yu, Yu Jih Su, Wen Chan Chiu, Ying Chou Chen, Han Ming Lai, Jia Feng Chen, Chi Hua Ko, Jung Fu Chen, Tien Tsai Cheng

研究成果: Article同行評審

摘要

Introduction: The aim of this study was to develop an algorithm to identify high-risk populations of fragility fractures in Taiwan. Materials and methods: A total of 16,539 postmenopausal women and men (age ≥ 50 years) were identified from the Taiwan Osteoporosis Survey database. Using the Taiwan FRAX® tool, the 10-year probability of major osteoporotic fracture (MOF) and hip fracture (HF) and the individual intervention threshold (IIT) of each participant were calculated. Subjects with either a probability above the IIT or those with MOF ≥ 20% or HF ≥ 9% were included as group A. Subjects with a bone mineral density (BMD) T-score at femoral neck based on healthy subjects of ≤ − 2.5 were included in group B. We tested several cutoff points for MOF and HF so that the number of patients in group A and group B were similar. A novel country-specific hybrid intervention threshold along with an algorithm was generated to identify high fracture risk individuals. Results: 3173 (19.2%) and 3129 (18.9%) participants were categorized to groups A and B, respectively. Participants in group B had a significantly lower BMD (p < 0.001), but clinical characteristics, especially the 10-year probability of MOF (p < 0.001) or HF (p < 0.001), were significantly worse in group A. We found the algorithm generated from the hybrid intervention threshold is practical. Conclusion: The strategy of generating an algorithm for fracture prevention by novel hybrid intervention threshold is more efficient as it identifies patients with a higher risk of fragility fracture and could be a template for other country-specific policies.

原文English
頁(從 - 到)213-221
頁數9
期刊Journal of Bone and Mineral Metabolism
38
發行號2
DOIs
出版狀態Published - 2020 三月 1

All Science Journal Classification (ASJC) codes

  • 內分泌學、糖尿病和代謝
  • 骨科和運動醫學
  • 內分泌

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