Yuan Huiqi, Jiang Ye, Li Yali, Bi Lisha, Zhu Shuhong
Health Intelligence Research Center of Beijing Xicheng District, Beijing, China.
Department of Orthopedics, Peking University First Hospital, Beijing, China.
Front Public Health. 2024 Nov 27;12:1482931. doi: 10.3389/fpubh.2024.1482931. eCollection 2024.
Motoric cognitive risk (MCR) syndrome is characterized by slow gait speed and subjective cognitive complaints (SCC) and increases the risk of dementia and mortality.
This study aimed to examine the clinical risk factors and prevalence of MCR in community-dwelling older adults, with the goal of developing and validating a nomogram model for developing prevention strategies against MCR.
We enrolled community-dwelling participants aged 60-85 years at Guangwai Community Health Service Center between November 2023 and January 2024. A total of 1,315 older adults who met the criteria were randomly divided into a training set ( = 920) and a validation set ( = 395). By using univariate and stepwise logistic regression analysis in the training set, the MCR nomogram prediction model was developed. The area under the receiver operator characteristic curve (AUC), calibration plots, and Hosmer-Lemeshow goodness of fit test were used to evaluate the nomogram model's predictive performance, while decision curve analysis (DCA) was used to evaluate the model's clinical utility.
Education, physical exercise, hyperlipoidemia, osteoarthritis, depression, and Time Up and Go (TUG) test time were identified as independent risk factors and were included to develop a nomogram model. The model exhibited high accuracy with AUC values of 0.909 and 0.908 for the training and validation sets, respectively. Calibration curves confirmed the model's reliability, and DCA highlighted its clinical utility.
This study constructs a nomogram model for MCR with high predictive accuracy, which provides a reference for large-scale early identification and screening of high-risk groups for MCR.
运动认知风险（MCR）综合征的特征为步态速度缓慢和主观认知主诉（SCC），并增加痴呆和死亡风险。
本研究旨在探讨社区居住的老年人中MCR的临床危险因素和患病率，目标是开发并验证一种列线图模型，以制定针对MCR的预防策略。
我们于2023年11月至2024年1月在广外社区卫生服务中心招募了年龄在60 - 85岁的社区居住参与者。共有1315名符合标准的老年人被随机分为训练集（n = 920）和验证集（n = 395）。通过在训练集中使用单因素和逐步逻辑回归分析，开发了MCR列线图预测模型。采用受试者操作特征曲线（AUC）下面积、校准图和Hosmer-Lemeshow拟合优度检验来评估列线图模型的预测性能，同时使用决策曲线分析（DCA）来评估模型的临床实用性。
教育程度、体育锻炼、高脂血症、骨关节炎、抑郁症以及起立行走测试（TUG）时间被确定为独立危险因素，并被纳入以开发列线图模型。该模型表现出高准确性，训练集和验证集的AUC值分别为0.909和0.908。校准曲线证实了模型的可靠性，DCA突出了其临床实用性。
本研究构建了一个预测准确性高的MCR列线图模型，为大规模早期识别和筛查MCR高危人群提供了参考。
