Wei An, Zou Yan, Tang Zhen-Hua, Guo Feng, Zhou Yan
Department of Ultrasound, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, No.89, GuHan Avenue, Changsha, HuNan, 410024, China.
Department of International Medicine, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, HuNan, China.
BMC Geriatr. 2025 Feb 5;25(1):81. doi: 10.1186/s12877-025-05733-y.
The correlation between calf circumference（CC）and sarcopenia has been demonstrated, but the correlation between calf maximum muscle circumference (CMMC) measured by ultrasound and sarcopenia has not been reported. We aims to construct a predictive model for sarcopenia based on CMMC in hospitalized older patients.
This was a retrospective controlled study of patients > 60 years of age hospitalized in the geriatric department of Hunan Provincial People's Hospital. The patients were thoroughly evaluated by questionnaires, laboratory, and ultrasound examinations, including measuring muscle thickness and calf muscle maximum circumference using ultrasound. Patients were categorized into sarcopenia and non-sarcopenia groups according to the consensus for diagnosis of sarcopenia recommended by the Asian Working Group on Sarcopenia 2019 (AWGS2). Independent predictors of sarcopenia were identified by univariate and multivariate logistic regression analyses, and a predictive model was developed and simplified. The prediction performance of the models was assessed using sensitivity, specificity, and area under the curve (AUC) and compared with independent predictors.
We found that patient age, albumin level (ALB), brachioradialis muscle thickness (BRMT), gastrocnemius lateral head muscle thickness (Glh MT), and calf maximum muscle circumference (CMMC) were independent predictors of sarcopenia in hospitalized older patients. The prediction model was established and simplified to Logistic P = -4.5 + 1.4 × age + 1.3 × ALB + 1.6 × BR MT + 3.7 × CMMC + 1.8 × Glh MT, and the best cut-off value of the model was 0.485. The sensitivity, specificity, and AUC of the model were 0.884 (0.807-0.962), 0.837 (0.762-0.911), and 0.927 (0.890-0.963), respectively. The kappa coefficient between this model and the diagnostic criteria recommended by AWGS2 was 0.709.
We constructed a sarcopenia prediction model with five variables: age, ALB level, BR MT, Glh MT, and CMMC. The model could quickly predict sarcopenia in older hospitalized patients.
小腿围（CC）与肌肉减少症之间的相关性已得到证实，但超声测量的小腿最大肌肉围（CMMC）与肌肉减少症之间的相关性尚未见报道。我们旨在构建基于住院老年患者CMMC的肌肉减少症预测模型。
这是一项对在湖南省人民医院老年科住院的60岁以上患者进行的回顾性对照研究。通过问卷调查、实验室检查和超声检查对患者进行全面评估，包括使用超声测量肌肉厚度和小腿肌肉最大围度。根据2019年亚洲肌肉减少症工作组（AWGS2）推荐的肌肉减少症诊断共识，将患者分为肌肉减少症组和非肌肉减少症组。通过单因素和多因素逻辑回归分析确定肌肉减少症的独立预测因素，并建立和简化预测模型。使用敏感性、特异性和曲线下面积（AUC）评估模型的预测性能，并与独立预测因素进行比较。
我们发现患者年龄、白蛋白水平（ALB）、肱桡肌厚度（BRMT）、腓肠肌外侧头肌厚度（Glh MT）和小腿最大肌肉围度（CMMC）是住院老年患者肌肉减少症的独立预测因素。建立并简化预测模型为Logistic P = -4.5 + 1.4×年龄 + 1.3×ALB + 1.6×BR MT + 3.7×CMMC + 1.8×Glh MT，模型的最佳截断值为0.485。该模型的敏感性、特异性和AUC分别为0.884（0.807 - 0.962）、0.837（0.762 - 0.911）和0.927（0.890 - 0.963）。该模型与AWGS2推荐的诊断标准之间的kappa系数为0.709。
我们构建了一个包含年龄、ALB水平、BR MT、Glh MT和CMMC五个变量的肌肉减少症预测模型。该模型可快速预测老年住院患者的肌肉减少症。
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