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Updated: Jun 4, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Identifying and characterizing trajectories of cognitive change in older persons with mild cognitive impairment
Haiqun Xie1, Nancy Mayo, Lisa Koski
1Divisions of Geriatric Medicine and Clinical Epidemiology, Faculty of Medicine, McGill University, Research Institute of the McGill University Health Centre, Montreal, Que., Canada.
Background:
Mild cognitive impairment (MCI) represents a state of high risk for dementia but is heterogeneous in its course. To date, the trajectories reflecting distinct developmental courses of cognition among patients with MCI have not been well defined.
Aim:
To identify the developmental trajectory of groups with distinct cognitive change patterns among a cohort of MCI patients.
Methods:
187 MCI patients from 2 geriatric outpatient clinics were evaluated serially with the Mini-Mental State Examination (MMSE) for up to 3.5 years. Group-based trajectory analysis was applied to identify distinct trajectories. Estimates of decline for each group were compared with the mean rate of decline obtained from mixed modeling of the entire sample.
Results:
5 trajectories were identified and labeled based on their baseline MMSE score and course: (1) 29/stable (6.5%); (2) 27/stable (53.9%); (3) 25/slow decline (23.8%); (4) 24/slow decline (11.6%); (5) 25/rapid decline (4.2%). Annual rate of change in the MMSE score for these 5 groups was 0.09, -0.43, -1.23, -1.84, and -4.6 points, respectively. None corresponded to the mean rate of -0.82 points estimated for the group as a whole. A majority of MCI patients (60.4%) follow stable cognitive trajectories over time. Within the 3 groups with declining trajectories, cognitive decline occurs slowly in a vast majority of MCI patients (98.5%).
Conclusions:
Results provide direct evidence for the heterogeneous course of cognitive decline that has been suggested by the variable prognosis for patients with MCI.
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