Department of Healthcare Epidemiology, School of Public Health in the Graduate School of Medicine, Kyoto University, Kyoto City, Kyoto, Japan.
Department of Sociology, Kansai University, Suita City, Osaka, Japan.
Br J Nutr. 2021 Nov 14;126(9):1323-1330. doi: 10.1017/S0007114521000106. Epub 2021 Jan 14.
We aimed to develop and validate a new simple decision support tool (U-TEST) for diagnosis of sarcopenia in orthopaedic patients. We created seventeen candidate original questions to detect sarcopenia in orthopaedic patients with sarcopenia through expert opinions and a semi-structured interview. To derive a decision support tool, a logistic regression model with backward elimination was applied to select variables from the seventeen questions, age and underweight (BMI < 18·5 kg/m2). Sarcopenia was defined by Asian Working Group for Sarcopenia 2019 criteria. After assigning a score to each selected variable, the sum of scores was calculated. We evaluated the diagnostic performance of the new tool using a logistic regression model. A bootstrap technique was used for internal validation. Among a total of 1334 orthopaedic patients, sixty-five (4·9 %) patients were diagnosed with sarcopenia. We succeeded in developing a 'U-TEST' with scores ranging from 0 to 11 consisting of values for BMI (Underweight), age (Elderly) and two original questions ('I can't stand up from a chair without supporting myself with my arms' (Strength) and 'I feel that my arms and legs are thinner than they were in the past' (Thin)). The AUC was 0·77 (95 % CI 0·71, 0·83). With the optimal cut-off set at 3 or greater based on Youden's index, the sensitivity and the specificity were 76·1 and 63·6 %, respectively. In orthopaedic patients, our U-TEST scoring with two questions and two simple clinical variables can help to screen for sarcopenia.
我们旨在开发和验证一种新的简单决策支持工具(U-TEST),用于诊断骨科患者的肌肉减少症。我们通过专家意见和半结构化访谈创建了十七个候选原始问题,以通过检测骨科患者的肌肉减少症来检测肌肉减少症。为了得出决策支持工具,应用向后消除的逻辑回归模型从十七个问题、年龄和体重不足(BMI<18.5kg/m2)中选择变量。肌肉减少症根据亚洲肌肉减少症工作组 2019 标准定义。为每个选定的变量分配分数后,计算分数总和。我们使用逻辑回归模型评估新工具的诊断性能。使用自举技术进行内部验证。在总共 1334 名骨科患者中,有 65 名(4.9%)患者被诊断为肌肉减少症。我们成功地开发了一个“U-TEST”,分数范围为 0 至 11,由 BMI(体重不足)、年龄(老年人)和两个原始问题(“我不能站在椅子上而不支撑我的手臂”(力量)和“我觉得我的手臂和腿比过去瘦了”(瘦))的值组成。AUC 为 0.77(95%CI 0.71,0.83)。根据 Youden 指数,将最优截断值设定为 3 或更高,敏感性和特异性分别为 76.1%和 63.6%。在骨科患者中,我们的 U-TEST 评分与两个问题和两个简单的临床变量相结合,可以帮助筛查肌肉减少症。