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Updated: Jul 29, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Risk Factors and Annual Rates of Cognitive Decline in Elderly Mild Cognitive Impairment: A Retrospective Cohort Study
Mingyang Liu1, Zhihua Hao1, Jing Chen1
1Key Laboratory of Basic Theory of Traditional Chinese Medicine in Heilongjiang Province, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, People's Republic of China.
Objective:
To investigate the cognitive decline trajectories in elderly patients with MCI and identify related risk factors.
Methods:
This retrospective cohort study included 500 elderly patients diagnosed with MCI at our hospital between January 2021 and December 2024, with median follow-up of 36 months (IQR 24-48). Multivariable Cox proportional hazards regression models were used to identify risk factors associated with progression from MCI to dementia. Annual rate of cognitive decline was calculated as (final score - baseline score) divided by follow-up duration in years; multivariable linear regression was used to identify associated factors.
Results:
During follow-up, 78 participants (15.6%) progressed to dementia. The mean annual rate of decline in SLUMS score was 0.8 points per year (95% CI: 0.6, 1.0, p < 0.001), and the mean annual rate of decline in MoCA score was 0.7 points per year (95% CI: 0.5, 0.9, p < 0.001). For specific cognitive domains, the mean annual rate of decline in RAVLT score was 1.2 (95% CI: 0.9, 1.5, p < 0.001), and the mean annual rate of decline in CDT score was 0.3 (95% CI: 0.2, 0.4, p = 0.002).
Conclusion:
These findings associate older age, male sex, lower education, hypertension, and lower baseline SLUMS with faster cognitive decline in elderly MCI patients. Early risk stratification may inform intervention strategies, though causal inferences are limited. Prospective validation is needed.
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