Artificial intelligence as a “co-pilot” in phenotyping patients with arterial hypertension and chronic cerebral ischemia
Introduction. The use of artificial intelligence (AI) technologies as an auxiliary tool makes it possible to improve the phenotyping of elderly patients with arterial hypertension (AH) and chronic cerebral ischemia (CCI) and increases the effectiveness of therapy, taking into account individual characteristics. Objective: To evaluate the potential of AI technologies as an assistive tool (a “copilot”) for phenotyping elderly patients with AH and CCI, as well as to examine the clinical outcomes of sequential therapy with ethylmethylhydroxypyridine succinate (EMHPS – Mexidol and Mexidol 250 FORTE) in this group. Material and methods. A prospective cohort study included 1,235 participants over 60 years of age (median age 68 [64; 73] years; 57.3% women) with confirmed AH and CCI stages I–II. Stratified randomization allocated participants into two groups: the main group (n = 618) received sequential EMHPS therapy (intravenous infusion of 500 mg (10 ml) once a day for 14 days, followed by oral administration of 250 mg three times daily for 60 days) in addition to basic care, while the control group (n = 617) received basic therapy alone. For patient phenotyping, a machine learning algorithm (random forest with gradient boosting) integrated into a clinical decision support system was used. Cognitive status (MoCA, FAB, 10-Word Recall Test), levels of anxiety and depression (HADS), and asthenia (MFI-20) were assessed on days 1, 14, and 74. Results. The AI algorithm identified four phenotypes of patients with AH and CCI: “cerebrovascular” (32.4%), “dyscirculatory with predominant anxiety” (28.7%), “asthenic” (22.1%), and “cognitive” (16.8%). In the main group, by day 74, significant improvements were observed in cognitive function: MoCA increased by 3.2 points (p <0.001 vs control) and FAB by 4.1 points (p <0.001). HADS anxiety and depression scores decreased by 7.8 (p <0.001) and 3.4 points (p <0.01), respectively; MFI-20 asthenia score decreased by 28.6 points (p <0.01). The greatest therapeutic effect was observed in the “dyscirculatory with anxiety” and “cognitive” phenotypes. Conclusion. The use of AI as a “co-pilot” allows for the personalization of treatment in elderly patients with AH and CCI. The addition of EMHPS to baseline therapy improves cognitive outcomes, and reduces anxiety, depression, and asthenia, with the maximal benefit observed in specific clinical phenotypes.I.A. Zolotovskaya, D.V. Duplyakov, P.R. Shatskaia
Keywords
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About the Authors
Irina A. Zolotovskaya, MD, Dr. Sci. (Medicine), head of the Department of scientific and innovative technologies in healthcare, professor of the Department of hospital therapy with courses in hematology and transfusiology, Samara State Medical University, Samara, Russian Federation.E-mail: i.a.zolotovskaya@samsmu.ru
ORCID: https://orcid.org/0009-0006-8541-9100
Dmitry V. Duplyakov, MD, Dr. Sci. (Medicine), professor, head of the Department of propaedeutics of internal diseases with a course in cardiology, Samara State Medical University, Samara, Russian Federation.
E-mail: d.v.duplyakov@samsmu.ru
ORCID: https://orcid.org/0000-0002-6453-2976
Polina R. Shatskaia, MD, assistant at the Department of propaedeutics of internal diseases with a course in cardiology, Samara State Medical University, Samara, Russian Federation.
Е-mail: polya.sha98@gmail.com
ORCID: https://orcid.org/0000-0001-5183-1208



