A Personalized Methodology for Assessing Early Post-Stroke Cognitive Impairment
<p id="p1">The purpose of а study is to identify the main indicators of the individual profile of patients with early post-stroke cognitive impairment. The study included 200 patients diagnosed with ischemic stroke with cognitive decline. Medical history included an assessment of demographic p...
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| Main Authors: | , , , |
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| Format: | Book Chapter |
| Language: | English |
| Published: |
IntechOpen
2024
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| Subjects: | |
| Online Access: | https://www.intechopen.com/chapters/89630 |
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| Summary: | <p id="p1">The purpose of а study is to identify the main indicators of the individual profile of patients with early post-stroke cognitive impairment. The study included 200 patients diagnosed with ischemic stroke with cognitive decline. Medical history included an assessment of demographic parameters, cardiovascular risk factors, and comorbidities. The functional status of patients was assessed using various assessment tools: the Barthel Index, the Modified Rankin Scale, and the National Institutes of Health Stroke Scale. The patient’s cognitive and psycho-emotional profile was assessed using scales: the Montreal Cognitive Assessment Scale, the Informant Questionnaire on Cognitive Decline in the Elderly, the Modified Hachinski Ischemic Scale, the Hospital Anxiety and Depression Scale, the Apathy Evaluation Scale, the Multidimensional Fatigue Inventory-20, the Buss-Perry Aggression Questionnaire-24 and additional scales for assessing praxis, semantic aphasia, perception, and executive function. To objectively assess cognitive dysfunction, long-latency acoustic endogenous evoked potential parameters were assessed. The laboratory tests included the evaluation of the levels of cytokines. Neuroimaging parameters (stroke location, preexisting vascular and neurodegenerative disease) were assessed using magnetic resonance imaging (MRI). The patient profile with varying severity of cognitive impairment, pre-stroke cognitive decline, and lesion lateralization was determined by discriminant analysis of clinical and paraclinical parameters using ML algorithms.</p> |
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| ISBN: | 978-0-85014-411-6 |
| DOI: | 10.5772/intechopen.115075 |