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Updated: Mar 6, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Predictors That a Diagnosis of Mild Cognitive Impairment Will Remain Stable 3 Years Later
Matthew A Clem1, Ryan P Holliday, Seema Pandya
1Departments of *Psychiatry §Clinical Sciences, and ∥Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center at Dallas, Dallas, Texas †Veterans Affairs North Texas Health Care System, Dallas, Texas ¶Seton Brain & Spine Institute, Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas ‡Department of Clinical and Health Psychology, University of Florida, Gainesville, Florida.
Background And Objective:
In half to two thirds of patients who are diagnosed with mild cognitive impairment (MCI), the diagnosis neither converts to dementia nor reverts to normal cognition; however, little is known about predictors of MCI stability. Our study aimed to identify those predictors.
Methods:
We obtained 3-year longitudinal data from the National Alzheimer's Coordinating Center Uniform Data Set for patients with a baseline diagnosis of MCI. To predict MCI stability, we used the patients' baseline data to conduct three logistic regression models: demographics, global function, and neuropsychological performance.
Results:
Our final sample had 1059 patients. At the end of 3 years, 596 still had MCI and 463 had converted to dementia. The most reliable predictors of stable MCI were higher baseline scores on delayed recall, processing speed, and global function; younger age; and absence of apolipoprotein E4 alleles.
Conclusions:
Not all patients with MCI progress to dementia. Of the protective factors that we identified from demographic, functional, and cognitive data, the absence of apolipoprotein E4 alleles best predicted MCI stability. Our predictors may help clinicians better evaluate and treat patients, and may help researchers recruit more homogeneous samples for clinical trials.
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