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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Targeting HIV-Related Neurocognitive Impairments with Cognitive Training Strategies: Insights from the Cognitive
David E Vance1, Pariya L Fazeli2, John Cheatwood2
1School of Nursing, University of Alabama at Birmingham, Birmingham, AL, USA. devance@uab.edu.
Abstract:
Approximately 50% of older adults with HIV meet the Frascati diagnostic criteria of HIV-associated neurocognitive disorders (HAND) which can interfere with everyday function such as medication adherence, employment, and driving ability, thus reducing quality of life. As the number of older adults with HIV continues to grow, many will become vulnerable to cognitive frailty, especially as they experience multimorbidities, polypharmacy, and geriatric syndromes. Healthcare professionals need strategies to prevent, remediate, and compensate for cognitive losses observed in memory, language, executive functioning, and speed of processing. Sadly, there are no standard protocols or accepted treatment/intervention guidelines to address HAND at this time. Fortunately, evidence from the cognitive aging literature indicates that cognitive training can protect and improve cognition in normal older adults and may even reduce the incidence of dementia/MCI. This article provides the scientific context in which computerized cognitive training approaches have been successfully used in older adults and provides examples of how these approaches have been translated to adults with HIV. Evidence from ongoing clinical trials are also presented that suggest that reversing a diagnosis of HAND may be possible. Recommendations for clinical practice and research are provided.
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