Related Experiment Video
Updated: Jun 21, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Development and validation of a risk prediction model for motoric cognitive risk syndrome in older adults
Yaqin Li1, Yuting Huang1, Fangxin Wei1
1School of Nursing, Jinan University, Guangzhou, Guangdong Province, China.
Objective:
The objective of this study was to develop a risk prediction model for motoric cognitive risk syndrome (MCR) in older adults.
Methods:
Participants were selected from the 2015 China Health and Retirement Longitudinal Study database and randomly assigned to the training group and the validation group, with proportions of 70% and 30%, respectively. LASSO regression analysis was used to screen the predictors. Then, identified predictors were included in multivariate logistic regression analysis and used to construct model nomogram. The performance of the model was evaluated by area under the receiver operating characteristic (ROC) curve (AUC), calibration curves and decision curve analysis (DCA).
Results:
528 out of 3962 participants (13.3%) developed MCR. Multivariate logistic regression analysis showed that weakness, chronic pain, limb dysfunction score, visual acuity score and Five-Times-Sit-To-Stand test were predictors of MCR in older adults. Using these factors, a nomogram model was constructed. The AUC values for the training and validation sets of the predictive model were 0.735 (95% CI = 0.708-0.763) and 0.745 (95% CI = 0.705-0.785), respectively.
Conclusion:
The nomogram constructed in this study is a useful tool for assessing the risk of MCR in older adults, which can help clinicians identify individuals at high risk.
