Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital, Keelung 204, Taiwan.
Department of Medicine, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan.
Clin Interv Aging. 2020 Dec 23;15:2415-2422. doi: 10.2147/CIA.S287207. eCollection 2020.
Sarcopenia is highly prevalent among residents of assisted-living facilities. However, the optimal screening tools are not clear. Therefore, we compared the performance of four recommended screening tools for predicting sarcopenia.
The study recruited 177 people over 65 years of age in assisted-living facilities. Appendicular muscle mass index was measured using bioelectrical impedance analysis. Calf circumference (CC), handgrip, six-meters walking speed, and screening questionnaires including SARC-CalF, SARC-F and 5-item Mini Sarcopenia Risk Assessment (MSRA-5) were evaluated. The diagnosis criteria for sarcopenia were based on the Asian Working Group for Sarcopenia 2019 consensus. The area under the receiver operating characteristic curves (AUC) was used to contrast the diagnostic accuracy of screening tools.
The prevalence of sarcopenia was 52.7% among men and 51.2% among women. After adjusting for age, sex, body mass index and SARC-CalF score, CC remained significantly associated with sarcopenia in logistic regression analysis. The prediction model for sarcopenia based on CC alone had the highest accuracy compared to SARC-CalF, MSRA-5 and SARC-F (AUC, 0.819 vs 0.734 vs 0.600 vs 0.576; sensitivity/specificity, 80.4%/71.8% vs 38.0%/80.0% vs 60.7%/54.2% vs 10.9%/91.8%). Differences in AUCs between the prediction models were statistically significant (CC vs. SARC-CalF, P = 0.0181; SARC-CalF vs. MSRA-5, P = 0.0042). Optimal cutoff values for predicting sarcopenia were CC <34 cm in men and <33 cm in women.
To predict sarcopenia based on low CC alone is accurate, easy and inexpensive for use in assisted-living facility settings. Further validation studies in different populations are suggested.
在辅助生活设施中,肌少症的患病率非常高。然而,目前还不清楚最佳的筛查工具是什么。因此,我们比较了四种推荐的肌少症筛查工具在预测肌少症方面的表现。
这项研究招募了 177 名年龄在 65 岁以上的辅助生活设施居民。使用生物电阻抗分析测量四肢骨骼肌指数。评估了跟腱围度(CC)、握力、六米步行速度和包括 SARC-CalF、SARC-F 和五因素简易肌少症风险评估(MSRA-5)在内的筛查问卷。肌少症的诊断标准基于亚洲肌少症工作组 2019 年的共识。使用受试者工作特征曲线下面积(AUC)对比了筛查工具的诊断准确性。
男性肌少症的患病率为 52.7%,女性为 51.2%。在校正年龄、性别、体重指数和 SARC-CalF 评分后,CC 在逻辑回归分析中与肌少症仍显著相关。仅基于 CC 的肌少症预测模型与 SARC-CalF、MSRA-5 和 SARC-F 相比具有最高的准确性(AUC,0.819 对 0.734 对 0.600 对 0.576;敏感性/特异性,80.4%/71.8%对 38.0%/80.0%对 60.7%/54.2%对 10.9%/91.8%)。预测模型之间的 AUC 差异具有统计学意义(CC 对 SARC-CalF,P=0.0181;SARC-CalF 对 MSRA-5,P=0.0042)。预测肌少症的最佳 CC 截断值为男性<34cm,女性<33cm。
仅基于低 CC 预测肌少症既准确又简便,且经济实惠,适用于辅助生活设施环境。建议在不同人群中进一步验证这些研究结果。