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Updated: May 26, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Development and internal validation of a risk prediction model for dementia in a rural older population in China
Keke Liu1,2, Tingting Hou1,2, Yuqi Li1
1Department of Neurology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China.
Introduction:
We sought to develop a practical tool for predicting dementia risk among rural-dwelling Chinese older adults.
Methods:
This cohort study included 2220 rural older adults (age ≥ 65 years) who were dementia-free at baseline (2014) and were followed in 2018. Dementia was diagnosed following the DSM-IV criteria. The prediction model was constructed using Cox models. We used C-index and calibration plots to assess model performance, and the decision curve analysis (DCA) to assess clinical usefulness.
Results:
During the 4-year follow-up, 134 individuals were diagnosed with dementia. We identified age, education, self-rated AD8 score, marital status, and stroke for the prediction model, with the C-index being 0.79 (95% confidence interval = 0.75-0.83) and the corrected C-index for internal validation being 0.79. Calibration plots showed good performance in predicting up to 4-year dementia risk and DCA indicated good clinical usefulness.
Discussion:
The 4-year dementia risk can be accurately predicted using five easily available predictors in a rural Chinese older population.
Highlights:
We developed and internally validated a practical tool for dementia risk prediction among a rural older population in China. The prediction tool showed good discrimination and excellent calibration for predicting up to 4-year risk of dementia. The prediction tool can be used to identify individuals at a high risk for dementia for early preventive interventions.
