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Updated: Jul 26, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Development of a Swedish short version of the Montreal Cognitive Assessment for cognitive screening in patients with
Tamar Abzhandadze1, Erik Lundström2, Dongni Buvarp3
1Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg; Department of Occupational Therapy and Physiotherapy, Sahlgrenska University Hospital, Gothenburg. tamar.abzhandadze@vgregion.se.
Objective:
The primary objective was to develop a Swedish short version of the Montreal Cognitive Assessment (s-MoCA-SWE) for use with patients with stroke. Secondary objectives were to identify an optimal cut-off value for the s-MoCA-SWE to screen for cognitive impairment and to compare its sensitivity with that of previously developed short forms of the Montreal Cognitive Assessment.
Design:
Cross-sectional study.
Subjects/Patients:
Patients admitted to stroke and rehabilitation units in hospitals across Sweden.
Methods:
Cognition was screened using the Montreal Cognitive Assessment. Working versions of the s-MoCA-SWE were developed using supervised and unsupervised algorithms.
Results:
Data from 3,276 patients were analysed (40% female, mean age 71.5 years, 56% minor stroke at admission). The suggested s-MoCA-SWE comprised delayed recall, visuospatial/executive function, serial 7, fluency, and abstraction. The aggregated scores ranged from 0 to 16. A threshold for impaired cognition ≤ 12 had a sensitivity of 97.41 (95% confidence interval, 96.64-98.03) and positive predictive value of 90.30 (95% confidence interval 89.23-91.27). The s-MoCA-SWE had a higher absolute sensitivity than that of other short forms.
Conclusion:
The s-MoCA-SWE (threshold ≤ 12) can detect post-stroke cognitive issues. The high sensitivity makes it a potentially useful "rule-out" tool that may eliminate severe cognitive impairment in people with stoke.
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