Abstract
OBJECTIVES: To enhance the public awareness and facilitate diagnosis of osteoporosis, we aim to develop a new Chinese Osteoporosis Screening Algorithm (COSA) to identify people at high risk of osteoporosis. METHODS: A total of 4747 postmenopausal women and men aged ≥ 50 from the Hong Kong Osteoporosis Study were randomly split into a development (N = 2373) and an internal validation cohort (N = 2374). An external validation cohort comprising 1876 community-dwelling subjects was used to evaluate the positive predictive value (PPV). RESULTS: Among 11 predictors included, age, sex, weight, and history of fracture were significantly associated with osteoporosis after correction for multiple testing. Age- and sex-stratified models were developed due to the presence of significant sex and age interactions. The area under the curve of the COSA in the internal validation cohort was 0.761 (95% CI, 0.711-0.811), 0.822 (95% CI, 0.792-0.851), and 0.946 (95% CI, 0.908-0.984) for women aged < 65, women aged ≥ 65, and men, respectively. The COSA demonstrated improved reclassification performance when compared to Osteoporosis Self-Assessment Tool for Asians. In the external validation cohort, the PPV of COSA was 40.6%, 59.4%, and 19.4% for women aged < 65, women aged ≥ 65, and men, respectively. In addition, COSA > 0 was associated with an increased 10-year risk of hip fracture in women ≥ 65 (OR, 4.65; 95% CI, 2.24-9.65) and men (OR, 11.51; 95% CI, 4.16-31.81). CONCLUSIONS: We have developed and validated a new osteoporosis screening algorithm, COSA, specific for Hong Kong Chinese.