Age-Related Development During Predictors and Clinical Neurodiagnostic Criteria of Cognitive Impairment in the General Medical Network

Abstract

Background: prevalence, severity and heterogeneity of cognitive impairment in elderlies along with limited therapy options determine the relevance of the problem of timely diagnostics of cognitive disfunction. The purpose of this study is to identify a combination of the most informative patterns that allow a differentiated approach to the diagnosis of age-related cognitive impairment. Patients and methods: 213 patients were examined (99 patients 50–65 years old, 114 patients over 65 years of age) of “Federal Centre for High Medical Technologies” of Russian Ministry of Health (Kaliningrad). All patients complained for impaired mental performance, memory and attention. A neuropsychologic testing was conducted using next scales: Montreal Cognitive Assessment (MoCA), Hospital Anxiety and Depression Scale (HADS), Multidimensional Fatigue Inventory (MFI-20) and additional cognitive impairment tests. For statistical analysis, machine learning algorithms, Python programming language, and Pandas and SciPy libraries were used. Results: for patients in the 50–65 age category, high relevance was found for executive dysfunction, decreased attention span, fatigue, anxiety, and endocrine system disorders. For patients over 65 years of age, significant features were semantic aphasia, perceptual and memory impairment, hyperlipidemia, history of ischemic stroke, and obesity. A significant negative correlation for the age index was found with the parameters of depression and anxiety; a positive correlation was found with the index of physical asthenia, disorders of perception, memory and semantic processing of information. Conclusion: the results demonstrate prevalence of cognitive dysfunctions in elderly patients. The tests assessing visual perception and semantic information processing can be of interest in early degenerative cognitive impairments diagnosis in elderly age. Discriminant analysis of a wide range of age-related variables will allow to make more effective aging trajectories prediction without any time-consuming diagnostic methods.