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Performance of Osteoporosis Self-Assessment Tool (OST) in Predicting Osteoporosis—A Review

Author

Listed:
  • Shaanthana Subramaniam

    (Department of Pharmacology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Jalan Yaacob Latif, Bandar Tun Razak, Cheras, Kuala Lumpur 56000, Malaysia)

  • Soelaiman Ima-Nirwana

    (Department of Pharmacology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Jalan Yaacob Latif, Bandar Tun Razak, Cheras, Kuala Lumpur 56000, Malaysia)

  • Kok-Yong Chin

    (Department of Pharmacology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Jalan Yaacob Latif, Bandar Tun Razak, Cheras, Kuala Lumpur 56000, Malaysia)

Abstract

Bone health screening plays a vital role in the early diagnosis and treatment of osteoporosis to prevent fragility fractures among the elderly and high-risk individuals. Dual-energy X-ray absorptiometry (DXA), which detects bone mineral density, is the gold standard in diagnosing osteoporosis but is not suitable for screening. Therefore, many screening tools have been developed to identify individuals at risk for osteoporosis and prioritize them for DXA scanning. The Osteoporosis Self-assessment Tool (OST) is among the first tools established to predict osteoporosis in postmenopausal women. It can identify the population at risk for osteoporosis, but its performance varies according to ethnicity, gender, and age. Thus, these factors should be considered to ensure the optimal use of OST worldwide. Overall, OST is a simple and economical screening tool to predict osteoporosis and it can help to optimize the use of DXA.

Suggested Citation

  • Shaanthana Subramaniam & Soelaiman Ima-Nirwana & Kok-Yong Chin, 2018. "Performance of Osteoporosis Self-Assessment Tool (OST) in Predicting Osteoporosis—A Review," IJERPH, MDPI, vol. 15(7), pages 1-22, July.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:7:p:1445-:d:157023
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    Citations

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    Cited by:

    1. Maria Radeva & Dorothee Predel & Sven Winzler & Ulf Teichgräber & Alexander Pfeil & Ansgar Malich & Ismini Papageorgiou, 2021. "Reliability of a Risk-Factor Questionnaire for Osteoporosis: A Primary Care Survey Study with Dual Energy X-ray Absorptiometry Ground Truth," IJERPH, MDPI, vol. 18(3), pages 1-14, January.
    2. Wen-Yu Ou Yang & Cheng-Chien Lai & Meng-Ting Tsou & Lee-Ching Hwang, 2021. "Development of Machine Learning Models for Prediction of Osteoporosis from Clinical Health Examination Data," IJERPH, MDPI, vol. 18(14), pages 1-12, July.

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