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J Bone Miner Metab. 2019 Oct 3. doi: 10.1007/s00774-019-01046-4. [Epub ahead of print]

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

Author information

1
Division of Rheumatology, Allergy and Immunology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123, Ta-Pei Road, Kaohsiung, 833, Taiwan.
2
Department of Family Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
3
Division of Endocrinology and Metabolism, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan.
4
Division of Rheumatology, Allergy and Immunology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123, Ta-Pei Road, Kaohsiung, 833, Taiwan. tiantsai0919@gmail.com.

Abstract

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.

KEYWORDS:

Fracture; Intervention thresholds; Osteoporosis

PMID:
31583541
DOI:
10.1007/s00774-019-01046-4

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