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Clinical Features - Review

A review on the performance of osteoporosis self-assessment tool for Asians in determining osteoporosis and fracture risk

Pages 734-746 | Received 03 May 2017, Accepted 06 Jul 2017, Published online: 24 Jul 2017
 

ABSTRACT

The prevalence of osteoporosis in Asian countries is growing. An effective screening method will enable patients at risk for osteoporosis to receive early diagnosis and treatment, and avoid overcrowding the limited dual-energy x-ray absorptiometry (DXA) machines available in Asian countries. Many simple osteoporosis screening algorithms have been developed but they are not validated for use in Asian populations. osteoporosis self-assessment tools for Asians (OSTA), established using a multinational Asian cohort, is the first screening algorithm that caters for the Asian populations. It considers only body weight and age in the algorithm. It shows consistently high performance and sensitivity in identifying postmenopausal women at risk for osteoporosis in many Asian countries. Its usage has been expanded for identifying osteoporosis in men, as well as determining fracture risk for both sexes. However, the performance of OSTA is influenced by age, sex, ethnicity and site of BMD measurement to define osteoporosis. Its usage is also limited in individuals without apparent risk factors. These limitations should be noted by physicians considering the use of OSTA in clinical setting. As a conclusion, OSTA is a cost-effective measure for osteoporosis screening in primary healthcare setting.

Declaration of interest

The author has no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Additional information

Funding

The author thanks Universiti Kebangsaan Malaysia for sponsoring his research via GGPM-2015-036 and FF-2017-013.

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