Development and validation of a nomogram for predicting motoric cognitive risk syndrome among community-dwelling older adults in China: a cross-sectional study

• Published in: Frontiers in public health 2024-11, Vol.12, p.1482931
• Main Authors: Yuan, Huiqi, Jiang, Ye, Li, Yali, Bi, Lisha, Zhu, Shuhong
• Format: Article
• Language: English
• Subjects: Aged; Aged, 80 and over; China - epidemiology; Cognitive Dysfunction - diagnosis; Cross-Sectional Studies; dementia; Female; gait; Humans; Independent Living; logistic regression; Male; Middle Aged; motoric cognitive risk syndrome; nomogram; Nomograms; older adults; Prevalence; Public Health; Risk Factors; Walking Speed
• ISSN: 2296-2565; 2296-2565
• DOI: 10.3389/fpubh.2024.1482931

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.