Yuan Guiying, Ye Guoxi, Hu Jianguang, Hu Huimin, Shi Chanmei, Zhang Ye, Huang Junbing, Li Zhiqiong, Zeng Xuwen, Tan Rongshao, Xiong Yuchao
Department of Respiratory, Guangzhou Red Cross Hospital (Guangzhou Red Cross Hospital of Jinan University), Guangzhou, China.
Department of Radiology, Guangzhou Red Cross Hospital (Guangzhou Red Cross Hospital of Jinan University), 396 Tongfu road Guangzhou, Guangzhou, Guangdong Province, 510220, China.
BMC Geriatr. 2025 Jan 31;25(1):69. doi: 10.1186/s12877-025-05730-1.
Sarcopenia is an age-related syndrome that can impact the physical and mental health of older adults. However, it is often overlooked in clinical practice. Therefore, we aim to construct a nomogram based on simplified discriminant parameters for screening older adult patients for sarcopenia risk.
This cross-sectional study included 654 patients aged ≥ 60 years who underwent an examination in the radiology department between October 2023 and June 2024. Patients were diagnosed with sarcopenia according to the method and cutoff value criteria proposed the Asian Working Group on Sarcopenia (AWGS) 2019 criteria. Calf circumference (CC), SARC-F score, mid-upper arm circumference (MUAC), and SARC-CalF score were used as simplified discriminant parameters for sarcopenia. The discriminative ability of these parameters for sarcopenia was assessed using receiver operating characteristic analysis. Additionally, we included each screening parameter and evaluated it's important for screening for the presence of sarcopenia via univariate and multivariate logistic regression analysis to develop a new screening nomogram model. The performance of the nomogram was evaluated using receiver operating characteristic curves, and the performance of the nomogram model was compared to that of CC, SARC-F, MUAC, and the SARC-CalF using the Delong test.
Of the 654 subjects, 120 (18.3%) were diagnosed with sarcopenia, and the areas under the curve (AUCs) of the CC, SARC-F, MUAC, and SARC-CalF were 0.73, 0.61, 0.66, and 0.70, respectively. The multivariate analysis results revealed that older age, male sex, low CC, low MUAC, and low strength were related to sarcopenia. A nomogram model constructed with these five variables had an AUC of 0.84. The DeLong test showed that the diagnostic efficacy of the joint model was significantly higher than that of CC, SARC-F, MUAC, and SARC-CalF.
Our simple nomogram based on simplified discriminant parameters offers personalized sarcopenia screening for older adult patients attending the radiology department.
肌肉减少症是一种与年龄相关的综合征,会影响老年人的身心健康。然而,在临床实践中它常常被忽视。因此,我们旨在构建一种基于简化判别参数的列线图,用于筛查老年患者的肌肉减少症风险。
这项横断面研究纳入了654例年龄≥60岁的患者,这些患者于2023年10月至2024年6月在放射科接受了检查。根据亚洲肌肉减少症工作组(AWGS)2019标准提出的方法和临界值标准对患者进行肌肉减少症诊断。小腿围(CC)、SARC - F评分、上臂中段围(MUAC)和SARC - CalF评分被用作肌肉减少症的简化判别参数。使用受试者工作特征分析评估这些参数对肌肉减少症的判别能力。此外,我们纳入了每个筛查参数,并通过单因素和多因素逻辑回归分析评估其对筛查肌肉减少症存在情况的重要性,以开发一种新的筛查列线图模型。使用受试者工作特征曲线评估列线图的性能,并使用德龙检验将列线图模型的性能与CC、SARC - F、MUAC和SARC - CalF的性能进行比较。
在654名受试者中,120名(18.3%)被诊断为肌肉减少症,CC、SARC - F、MUAC和SARC - CalF的曲线下面积(AUC)分别为0.73、0.61、0.66和0.70。多因素分析结果显示,年龄较大、男性、低CC、低MUAC和低肌力与肌肉减少症有关。由这五个变量构建的列线图模型的AUC为0.84。德龙检验表明,联合模型的诊断效能显著高于CC、SARC - F、MUAC和SARC - CalF。
我们基于简化判别参数的简易列线图为在放射科就诊的老年患者提供了个性化的肌肉减少症筛查。