Department of General Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P. R. China; Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P. R. China; Department and Institute of Infectious Disease, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Department of General Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P. R. China; Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P. R. China.
J Am Med Dir Assoc. 2023 Apr;24(4):497-503. doi: 10.1016/j.jamda.2023.02.001. Epub 2023 Mar 13.
Sarcopenia is associated with significantly higher mortality risk, and earlier detection of sarcopenia has remarkable public health benefits. However, the model that predicts sarcopenia in the community has yet to be well identified. The study aimed to develop a nomogram for predicting the risk of sarcopenia and compare the performance with 3 sarcopenia screen models in community-dwelling older adults in China.
Cross-sectional study.
A total of 966 community-dwelling older adults.
A total of 966 community-dwelling older adults were enrolled in the study, with 678 participants grouped into the Training Set and 288 participants grouped into the Validation Set according to a 7:3 randomization. Predictors were identified in the Training Set by univariate and multivariate logistic regression and then combined into a nomogram to predict the risk of sarcopenia. The performance of this nomogram was assessed by calibration, discrimination, and clinical utility.
Age, body mass index, calf circumference, congestive heart failure, and chronic obstructive pulmonary disease were demonstrated to be predictors for sarcopenia. The nomogram (named as AB3C model) that was constructed based on these predictors showed excellent calibration and discrimination in the Training Set with an area under the receiver operating characteristic curve (AUC) of 0.930. The nomogram also showed perfect calibration and discrimination in the Validation Set with an AUC of 0.897. The clinical utility of the nomogram was supported by decision curve analysis. Comparing the performance with 3 sarcopenia screen models (SARC-F, Ishii, and Calf circumference), the AB3C model outperformed the other models regarding sensitivity and AUC.
AB3C model, an easy-to-apply and cost-effective nomogram, was developed to predict the risk of sarcopenia, which may contribute to optimizing sarcopenia screening in community settings.
肌少症与更高的死亡率显著相关,早期发现肌少症具有显著的公共卫生效益。然而,预测社区肌少症的模型尚未得到很好的确定。本研究旨在为社区居住的老年人开发一种预测肌少症风险的列线图,并与 3 种肌少症筛查模型进行比较。
横断面研究。
共纳入 966 名社区居住的老年人,根据 7:3 的随机分组原则,将 678 名参与者分为训练集,288 名参与者分为验证集。通过单变量和多变量逻辑回归在训练集中确定预测因子,然后将这些预测因子组合成一个列线图来预测肌少症的风险。通过校准、区分度和临床实用性评估该列线图的性能。
年龄、体重指数、小腿围、充血性心力衰竭和慢性阻塞性肺疾病被证明是肌少症的预测因子。基于这些预测因子构建的列线图(命名为 AB3C 模型)在训练集中表现出良好的校准和区分度,受试者工作特征曲线下面积(AUC)为 0.930。该列线图在验证集中也表现出了极好的校准和区分度,AUC 为 0.897。决策曲线分析支持了该列线图的临床实用性。与 3 种肌少症筛查模型(SARC-F、Ishii 和小腿围)相比,AB3C 模型在灵敏度和 AUC 方面表现优于其他模型。
AB3C 模型是一种易于应用和具有成本效益的列线图,用于预测肌少症的风险,这可能有助于优化社区环境中的肌少症筛查。