ПРОГНОЗУВАННЯ ПОСТІНСУЛЬТНИХ НЕЙРОКОГНІТИВНИХ РОЗЛАДІВ З УРАХУВАННЯМ НЕЙРОПЛАСТИЧНОСТІ, НЕЙРОЗАПАЛЕННЯ ТА ЦЕРЕБРАЛЬНОЇ ПЕРФУЗІЇ

Post-stroke neurocognitive disorders (PSNCD) represent a leading cause of disability, significantly determining patient quality of life and the risk of recurrent vascular events. The prediction of PSNCD utilizing neuroplasticity and neuroinflammation biomarkers, combined with MR perfusion data, constitutes a highly relevant focus in contemporary neurology. Objective. To identify clinical, neurological, laboratory, and instrumental predictors of neurocognitive disorder development in the early and late periods of ischemic stroke, facilitating early patient risk stratification. Materials and Methods. A total of 59 patients with ischemic stroke sequelae (ICD-10: I63, I69) aged 45–75 years (mean age 61.4 ± 7.8 years; 57.6% male, 42.4% female) and 31 healthy volunteers were examined. Cognitive functions were assessed using the MoCA, FAB, and Trail Making Test (TMT) scales. Serum BDNF, IL-6, and TNF-α were quantified, and brain MRI with non-contrast ASL MR perfusion was performed. Statistical analysis was conducted using analysis of variance (ANOVA), the Kruskal–Wallis test, Pearson’s chi-square test, and Spearman’s rank correlation coefficient, incorporating receiver operating characteristic (ROC) analysis to determine predictive accuracy, with a p-value of less than 0.05 considered statistically significant. The study was conducted in accordance with the ethical principles of biomedical research and the World Medical Association Declaration of Helsinki (2013 revision), with written informed consents taken from either participant or his/her legal guardian prior the present study. The study protocol was approved by the Local Ethics Committee of Samarkand State Medical University. The research was conducted as part of the scientific research plan of Samarkand State Medical University on the topic "Improvement of methods for early diagnosis, treatment, and prevention of pathological conditions affecting the health of the population of Samarkand region" (2022–2026). Results. Decreased MoCA scores (< 26 points) were identified in 44 patients (74.6%). Progression in clinical severity was associated with a significant decrease in BDNF (from 21.4 ± 3.2 to 13.6 ± 2.4 ng/mL) and a concurrent elevation in IL-6 and TNF-α levels (p < 0.001). Cerebral blood flow (CBF), assessed via ASL MR perfusion, progressively declined from 42.6 ± 4.8 mL/100 g/min in the first group to 30.4 ± 4.6 mL/100 g/min in the third group (p < 0.001). The combined BDNF+IL-6+CBF model demonstrated the highest prognostic accuracy (AUC = 0.91; sensitivity 88.6%; specificity 84.1%; p < 0.001). Conclusion. Comprehensive assessment of BDNF, IL-6, TNF-α, and cerebral perfusion via ASL MR perfusion ensures high accuracy in predicting PSNCD. The multifactorial prognostic model significantly outperforms isolated indicators, justifying its implementation into clinical practice for early risk stratification.