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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Diagnostic transitions in mild cognitive impairment by use of simple Markov models
David Facal1, Joan Guàrdia-Olmos2, Onésimo Juncos-Rabadán1
1Departament of Developmental Psychology, University de Santiago de Compostela, A Coruña, Spain.
Background:
Mild cognitive impairment (MCI) is a complex entity, which can involve persistence of the symptoms, conversion to dementia or improvement. The aim was to study the transitions between normal cognitive ageing and three MCI subtypes by using Markov transition models for different intervals between baseline and the follow-up assessment.
Methods:
A total of 294 participants over 50 years old attending primary care centres were assessed and diagnosed at baseline as multi-domain amnestic MCI (22 participants), single domain amnestic MCI (44), non-amnestic MCI (non-amnestic MCI) (26) or controls (202). We adopted an overlapping interval strategy by constructing six different mid-point time intervals according to the time between the baseline and the follow-up assessment. We used Markov transition models to study diagnostic changes in the groups in the different time intervals
Results:
The rate of change was lowest in the control group. In the single domain amnestic MCI and non-amnestic MCI groups, the same diagnosis was usually retained or changed to normal cognitive functioning. In the multi-domain amnestic MCI group, the rate of transition to normal functioning was lowest, and the conversion to dementia was the highest of all groups. The best fit to the Markov models was found for the period between 18-21 months, whereas the worst fit was for the period between 9-15 months
Conclusions:
Markov models provide a comprehensive view of transitions between MCI and normal cognitive functioning. Time interval strategies seem to provide a good opportunity to monitor diagnostic transitions, although wider intervals including subsequent assessments are needed. The low rates of conversion to dementia are discussed.
Insights
Transitions between normal cognition and mild cognitive impairment (MCI) subtypes were studied using Markov models. Multi-domain amnestic MCI showed the highest dementia conversion rate, highlighting the need for monitoring cognitive changes.
Area of Science:
- Neurology
- Gerontology
- Biostatistics
Background:
- Mild cognitive impairment (MCI) is a heterogeneous condition with variable outcomes, including persistence, progression to dementia, or improvement.
- Understanding transitions between normal cognitive aging and MCI subtypes is crucial for patient management and prognosis.
Purpose of the Study:
- To investigate the dynamic transitions between normal cognitive aging and three subtypes of mild cognitive impairment (MCI) using Markov transition models.
- To analyze these transitions across different time intervals between baseline and follow-up assessments.
Main Methods:
- A cohort of 294 participants aged over 50 were assessed and categorized into normal cognition, multi-domain amnestic MCI, single-domain amnestic MCI, or non-amnestic MCI.
- Markov transition models were employed to analyze diagnostic changes over six overlapping time intervals.
- The best model fit was observed for intervals between 18-21 months, while the poorest fit was for 9-15 months.
Main Results:
- The control group exhibited the lowest rate of cognitive change.
- Participants with single-domain amnestic MCI and non-amnestic MCI typically maintained their diagnosis or reverted to normal cognition.
- The multi-domain amnestic MCI group demonstrated the lowest rate of transition to normal functioning and the highest rate of conversion to dementia.
Conclusions:
- Markov models effectively illustrate transitions between MCI subtypes and normal cognitive functioning.
- Strategic use of time intervals aids in monitoring diagnostic shifts in cognitive status.
- Further research with broader time intervals is recommended for comprehensive monitoring.
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