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首页> 美国卫生研究院文献>International Journal of Environmental Research and Public Health >Development of Osteoporosis Screening Algorithm for Population Aged 50 Years and above in Klang Valley Malaysia
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Development of Osteoporosis Screening Algorithm for Population Aged 50 Years and above in Klang Valley Malaysia

机译：马来西亚巴生谷50岁及以上人口骨质疏松症筛查算法的开发

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摘要

The current osteoporosis screening instruments are not optimized to be used among the Malaysian population. This study aimed to develop an osteoporosis screening algorithm based on risk factors for Malaysians. Malaysians aged ≥50 years (n = 607) from Klang Valley, Malaysia were interviewed and their bone health status was assessed using a dual-energy X-ray absorptiometry device. The algorithm was constructed based on osteoporosis risk factors using multivariate logistic regression and its performance was assessed using receiver operating characteristics analysis. Increased age, reduced body weight and being less physically active significantly predicted osteoporosis in men, while in women, increased age, lower body weight and low-income status significantly predicted osteoporosis. These factors were included in the final algorithm and the optimal cut-offs to identify subjects with osteoporosis was 0.00120 for men [sensitivity 73.3% (95% confidence interval (CI) = 54.1%–87.7%), specificity 67.8% (95% CI = 62.7%–85.5%), area under curve (AUC) 0.705 (95% CI = 0.608–0.803), < 0.001] and 0.161 for women [sensitivity 75.4% (95% CI = 61.9%–73.3%), specificity 74.5% (95% CI = 68.5%–79.8%), AUC 0.749 (95% CI = 0.679–0.820), < 0.001]. The new algorithm performed satisfactorily in identifying the risk of osteoporosis among the Malaysian population ≥50 years. Further validation studies are required before applying this algorithm for screening of osteoporosis in public.
机译：当前的骨质疏松症筛查工具并未针对马来西亚人群进行优化。这项研究旨在开发一种基于马来西亚人危险因素的骨质疏松症筛查算法。来自马来西亚巴生谷的年龄≥50岁（n = 607）的马来西亚人接受了采访，并使用双能X射线吸收仪评估了他们的骨骼健康状况。该算法是基于骨质疏松症危险因素的多元logistic回归方法构建的，其性能通过接收者操作特征分析进行了评估。年龄增长，体重减轻和体育活动量减少，可明显预测男性骨质疏松症，而女性年龄增长，体重降低和低收入状态则可预测骨质疏松症。这些因素已包括在最终算法中，用于识别患有骨质疏松症的受试者的最佳临界值为0.00120（男性[敏感性73.3％（95％置信区间（CI）= 54.1％–87.7％）），特异性67.8％（95％CI） = 62.7％–85.5％），女性曲线下面积（AUC）0.705（95％CI = 0.608–0.803），<0.001]和0.161 [敏感性75.4％（95％CI = 61.9％–73.3％），特异性74.5 ％（95％CI = 68.5％–79.8％），AUC 0.749（95％CI = 0.679–0.820），<0.001]。新算法在识别50岁以上马来西亚人口中骨质疏松症的风险方面表现令人满意。在将该算法用于公众筛查骨质疏松症之前，需要进行进一步的验证研究。

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