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识别急性护理环境患者的肌肉减少症。

Identifying sarcopenia in acute care setting patients.

机构信息

Division of Geriatrics, Department of Medicine, University of Verona, Verona, Italy.

Division of Geriatrics, Department of Medicine, University of Verona, Verona, Italy.

出版信息

J Am Med Dir Assoc. 2014 Apr;15(4):303.e7-12. doi: 10.1016/j.jamda.2013.11.018. Epub 2014 Feb 6.

Abstract

OBJECTIVES

To evaluate the prevalence of sarcopenia by applying European Working Group on Sarcopenia in Older People (EWGSOP) flow chart in an acute care geriatric unit as well as to test a modified version of the EWGSOP diagnostic algorithm combining handgrip and gait speed test to identify subjects with low muscle mass.

DESIGN

Observational cohort study.

SETTING

Geriatric unit in an academic medical department.

PARTICIPANTS

One hundred nineteen acutely ill persons (34.4% female), with mean age 80.4 ± 6.9 years and body mass index 26.3 ± 4.9 kg/m(2).

MEASUREMENTS

Assessment of muscle mass by bioimpedence analysis, muscle strength by handheld dynamometer, and gait speed with the 4-meter walking test.

RESULTS

Using the EWGSOP classification for sarcopenia, 5.0% presented with sarcopenia and 21.0% with severe sarcopenia. Combining gait speed test and handgrip strength measurement, the highest predictive power in detecting subjects with low muscle mass was observed (sensitivity and specificity, 80.6% and 62.5%, respectively). Subjects presenting with both normal gait speed and handgrip showed normal values of muscle mass as assessed with bioimpedence analysis. By using the ROC method, when the 2 tests were combined, the AUC was statistically higher than when using each test separately (0.740; P = .018).

CONCLUSIONS

Our study shows that 1 of 4 patients admitted to the acute care department were recognized to be sarcopenic. When a modifived version of the EWGSOP flow chart, obtained combining both gait speed and handgrip was used, sensitivity and specificity of algorithm to identify subjects with low muscle mass was improved.

摘要

目的

通过应用欧洲老年人肌少症工作组(EWGSOP)流程图评估急性护理老年病房中的肌少症患病率,并测试一种改良的 EWGSOP 诊断算法,该算法结合握力和步态速度测试来识别低肌肉量的受试者。

设计

观察性队列研究。

设置

学术医学系的老年病房。

参与者

119 名急性疾病患者(女性占 34.4%),平均年龄为 80.4 ± 6.9 岁,体重指数为 26.3 ± 4.9 kg/m²。

测量

使用生物阻抗分析法评估肌肉量,使用手持测力计评估肌肉力量,使用 4 米步行测试评估步态速度。

结果

使用 EWGSOP 分类法诊断肌少症,5.0%的患者患有肌少症,21.0%的患者患有严重肌少症。结合步态速度测试和握力测量,检测低肌肉量受试者的预测能力最高(灵敏度和特异性分别为 80.6%和 62.5%)。表现出正常步态速度和握力的受试者,其肌肉量的生物阻抗分析值也正常。使用 ROC 方法,当两项测试联合使用时,AUC 显著高于单独使用每项测试时(0.740;P =.018)。

结论

我们的研究表明,在被收入急性护理病房的患者中,1/4的患者被诊断为肌少症。当使用改良的 EWGSOP 流程图(通过结合步态速度和握力获得)时,算法识别低肌肉量受试者的灵敏度和特异性得到提高。

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