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Updated: Dec 9, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Clinical and demographic parameters predict the progression from mild cognitive impairment to dementia in elderly
Giovanni Zuliani1, Michele Polastri2, Tommaso Romagnoli2
1Department of Morphology, Surgery, and Medical Sciences, University of Ferrara, Azienda Ospedaliero-Universitaria S. Anna, 44100, Ferrara, Italy. zlngnn@unife.it.
Objectives:
To evaluate the possibility of predicting the risk of progression from mild cognitive impairment (MCI) to dementia using a combination of clinical/demographic parameters.
Methods:
A total of 462 MCI elderly patients (follow-up: 33 months). Variable measured included cognitive functions, age, gender, MCI type, education, comorbidities, clinical chemistry, and functional status.
Results:
Amnestic type (aMCI) represented 63% of the sample, non-amnestic (naMCI) 37%; 190 subjects progressed to dementia, 49% among aMCI, and 28% among naMCI. At Cox multivariate regression analysis, only MMSE (one point increase HR 0.84; 95% CI 0.79-0.90), aMCI (HR 2.35; 95% CI 1.39-3.98), and age (1 year increase HR 1.05; 95% CI 1.01-1.10) were independently associated with progression to dementia. A score was created based on these dichotomized variables (score 0-3): age (≥ or < 78 years), MMSE score (≥ or < 25/30) and aMCI type. The conversion rate progressed from 6% in subjects with score 0 (negative predictive value: 0.94), to 31% in individuals with score 1, to 53% in subjects with score 2, to 72% in individuals with score 3 (positive predictive value: 0.72). ROC curve analysis showed an area under the curve of 0.72 (95% CI 0.66-0.75, p 0.0001).
Conclusions:
We have described a simple score, based on previously recognized predictors such as age, MMSE, and MCI type, which may be useful for an initial stratification of the risk of progression to dementia in patients affected by MCI. The score might help the clinicians to evaluate the need for more expansive/invasive examinations and for a closer follow-up in MCI patients.
Insights
A new score using age, MMSE, and mild cognitive impairment (MCI) type can predict dementia progression risk in elderly patients. This tool aids clinicians in stratifying patients needing further evaluation or closer monitoring.
Area of Science:
- Neurology
- Geriatrics
- Cognitive Science
Background:
- Mild cognitive impairment (MCI) is a transitional stage between normal aging and dementia.
- Predicting progression from MCI to dementia is crucial for timely intervention and management.
- Existing prediction models may not fully capture the risk stratification needs in diverse MCI populations.
Purpose of the Study:
- To develop and validate a predictive model for dementia progression in MCI patients.
- To identify key clinical and demographic factors associated with MCI to dementia conversion.
- To create a simple scoring system for initial risk stratification.
Main Methods:
- A cohort of 462 elderly patients with MCI was followed for 33 months.
- Clinical, cognitive, demographic, and functional status variables were assessed.
- Cox multivariate regression and ROC curve analyses were employed to identify predictors and assess model performance.
Main Results:
- Amnestic MCI (aMCI) patients had a higher progression rate (49%) than non-amnestic MCI (naMCI) patients (28%).
- Age, Mini-Mental State Examination (MMSE) score, and aMCI type were independent predictors of dementia progression.
- A validated score (0-3) incorporating these factors demonstrated increasing conversion rates from 6% to 72%.
Conclusions:
- A simple score based on age, MMSE, and MCI type effectively stratifies dementia progression risk.
- This score can assist clinicians in identifying MCI patients who may require more intensive evaluations or monitoring.
- The findings support the use of this score for initial risk assessment in MCI management.
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