Background/Objectives: Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide, with a particularly high burden in Central Asian countries. Despite ongoing urbanization, rural populations constitute a significant demographic in this region, yet epidemiological data stratified by urban and rural residence are limited and fragmented. This systematic review aimed to synthesize current evidence on the incidence, prevalence, mortality, and risk factor profiles of CVDs among urban and rural populations in Central Asia, identify disparities, and inform targeted prevention and control strategies. Methods: A systematic literature search was conducted across the PubMed, Science Direct, Web of Science, and Google Scholar databases for studies published between 2015 and 2025. Included studies reported cardiovascular health indicators with urban–rural stratification in Kazakhstan, Kyrgyzstan, Uzbekistan, Tajikistan, and Turkmenistan. Data extraction and qualitative synthesis were performed, with methodological quality assessed using the Newcastle–Ottawa Scale. Results: Eight original studies met the inclusion criteria, encompassing national and regional datasets with diverse designs, including retrospective analyses, cross-sectional surveys, and registry data. Overall, CVD incidence and prevalence showed increasing trends in both urban and rural areas, with consistently higher mortality rates in urban populations. Key modifiable risk factors—hypertension, obesity, dyslipidemia, and smoking—were prevalent, particularly in rural settings. Variability in healthcare access and preventive program implementation contributed to the observed disparities. Limited data from some countries, particularly Tajikistan and Turkmenistan, highlight gaps in epidemiological surveillance. Conclusions: The cardiovascular disease burden in Central Asia demonstrates significant urban–rural disparities, underscoring the need for tailored public health interventions and enhanced healthcare resource allocation in rural regions. Strengthening epidemiological monitoring and implementing region-specific prevention programs targeting modifiable risk factors are imperative for reducing CVD morbidity and mortality. Further high-quality research is necessary to address existing data gaps and optimize cardiovascular health strategies across the region.

Cardiovascular disease (CVD) risk assessment in older adults requires models that are accurate, clinically interpretable, and able to retain performance in independent populations. This study developed an interpretable machine-learning framework for CVD risk stratification in individuals aged 65 years and older using routinely available clinical factors and a selected biochemical extension and then evaluated its performance in a substantially larger independent external cohort. Model development used a development cohort of 100 patients (Almaty, age ≥ 65) with leakage-free nested cross-validation and out-of-fold (OOF) probabilities. Three internally evaluated configurations were compared: a clinical logistic regression baseline (LR clinical), a biomarker-augmented logistic regression (LR selected), and a nonlinear random forest on the selected feature set (RF selected). Discrimination was assessed using ROC-AUC and PR-AUC; probabilistic accuracy using Brier score and log loss. Calibration was examined using OOF calibration curves with sigmoid calibration for selected models. Decision-analytic utility and exploratory operational thresholds were assessed using Decision Curve Analysis (DCA), yielding a three-tier scale with thresholds t__low = 0.23 and t__high = 0.40. In nested cross-validation, LR clinical achieved ROC-AUC 0.9425 ± 0.0188 and PR-AUC 0.9574 ± 0.0092 with Brier 0.1004 ± 0.0215 and log loss 0.3634 ± 0.0652; LR selected performed worse, while RF selected showed competitive discrimination. External validation on an independent cohort (n = 695) showed retained discrimination (ROC-AUC 0.8355; PR-AUC 0.9376) with acceptable probabilistic accuracy (Brier 0.1131; log loss 0.3760), and recalibration (intercept + slope) slightly improved probability metrics. Explainability analyses (odds ratios, permutation importance, SHAP) consistently identified heredity, BMI, physical activity, and diabetes as influential model-associated factors, with clinically plausible directionality. The results suggest that an interpretable model trained on a small geriatric cohort can retain meaningful predictive performance on a substantially larger external cohort, supporting the potential value of transparent risk stratification in older adults, while broader prospective and multi-center validation remains necessary before routine clinical implementation.

Background: Cardiovascular diseases (CVDs) remain the primary cause of global mortality, with arterial hypertension, ischemic heart disease (IHD), and cerebrovascular accident (CVA) forming a progressive continuum from early risk factors to severe outcomes. While numerous studies focus on isolated biomarkers, few integrate multidimensional visualization with artificial intelligence to reveal hidden, clinically relevant patterns. Methods: We conducted a comprehensive analysis of 106 patients using an integrated framework that combined clinical, biochemical, and lifestyle data. Parameters included renal function (glomerular filtration rate, cystatin C), inflammatory markers, lipid profile, enzymatic activity, and behavioral factors. After normalization and imputation, we applied correlation analysis, parallel coordinates visualization, t-distributed stochastic neighbor embedding (t-SNE) with k-means clustering, principal component analysis (PCA), and Random Forest modeling with SHAP (SHapley Additive exPlanations) interpretation. Bootstrap resampling was used to estimate 95% confidence intervals for mean absolute SHAP values, assessing feature stability. Results: Consistent patterns across outcomes revealed impaired renal function, reduced physical activity, and high hypertension prevalence in IHD and CVA. t-SNE clustering achieved complete separation of a high-risk group (100% CVD-positive) from a predominantly low-risk group (7.8% CVD rate), demonstrating unsupervised validation of biomarker discriminative power. PCA confirmed multidimensional structure, while Random Forest identified renal function, hypertension status, and physical activity as dominant predictors, achieving robust performance (Accuracy 0.818; AUC-ROC 0.854). SHAP analysis identified arterial hypertension, BMI, and physical inactivity as dominant predictors, complemented by renal biomarkers (GFR, cystatin) and NT-proBNP. Conclusions: This study pioneers the integration of multidimensional visualization and AI-driven analysis for CVD risk profiling, enabling interpretable, data-driven identification of high- and low-risk clusters. Despite the limited single-center cohort (n = 106) and cross-sectional design, the findings highlight the potential of interpretable models for precision prevention and transparent decision support in cardiovascular aging research.

Background: Diagnosing post-COVID syndrome (PCS) in older adults with hypertension is difficult due to heterogeneity and multimorbidity. We aimed to identify factors associated with PCS. Methods: An observational study was conducted from June 2024 to April 2025. Patients aged 60–89 years with arterial hypertension were enrolled; PCS was verified according to the national protocol. Between-group comparisons used standard tests. Multivariable logistic regression with pre-specified clinical predictors estimated independent associations. Results: A total of 291 patients with arterial hypertension were included in the study. Patients were grouped by PCS status (PCS = 101; controls = 190). In multivariable analysis, female sex (OR 3.64; 95% CI 1.22–10.82), younger age (OR 0.93; 95% CI 0.89–0.98), lower systolic blood pressure (SBP) (OR 0.98; 95% CI 0.96–1.00), and rhythm disturbances (OR 2.63; 95% CI 1.07–6.49) were associated with PCS; other predictors were not significant. Model discrimination was moderate (AUC 0.728; 95% CI 0.668–0.787; Brier score 0.193) with positive net benefit across thresholds ~0.10–0.65. Conclusions: In older hypertensive adults, female sex, younger age, lower SBP, and rhythm disturbances indicate higher PCS likelihood, supporting risk-stratified monitoring and management.

Background/Objectives: Autoimmune hepatitis (AIH) is a chronic immune-mediated inflammatory liver disease that, if not diagnosed and treated promptly, leads to cirrhosis and liver failure. Data on AIH in Central Asia, including Kazakhstan, remain limited. The aim of this study was to characterize the clinical profile of AIH in a Kazakhstani patient cohort, determine the timeliness of diagnosis, and develop an interpretable machine learning model for detecting liver fibrosis based on routine clinical and laboratory parameters. Methods: A retrospective observational study of adult patients with a diagnosis of AIH between 2015 and 2025 was conducted. Demographic, laboratory, instrumental, and histological data of patients with AIH were extracted from medical records. All statistical analyses were performed using SPSS 22.0. Results: The study included 240 patients with a mean age of 49.3 ± 14.3 years; 87.1% of patients were women. The Random Forest model showed the best results: ROC-AUC of 0.803 ± 0.057, PR-AUC of 0.868 ± 0.044, Brier of 0.180 ± 0.017, sensitivity of 0.816, and specificity of 0.641. SHAP analysis confirmed that platelet count, age, INR, disease duration, and bilirubin and albumin levels made the greatest contribution to the prognosis. Conclusions: This retrospective observational study of AIH in Kazakhstan identified a patient population characterized by late diagnosis and advanced disease stages at presentation, a high frequency of overlapping autoimmune liver diseases, and a significant burden of metabolic and extrahepatic autoimmune comorbidities. The results demonstrate that an interpretable machine learning model based on routine biomarkers can effectively detect fibrosis and provide clinically interpretable risk factors.

Background: Adverse event reporting is a critical component of patient safety systems; however, nurses’ engagement in reporting is influenced not only by reporting procedures but also by broader organizational characteristics of the nursing practice environment. Although previous studies have examined reporting behaviors in various healthcare settings, limited evidence is available regarding how organizational factors influence nurses’ perceptions of adverse event reporting in post-Soviet primary healthcare systems. Objective: To examine the relationship between the nursing practice environment and nurses’ perceived benefits of adverse event reporting in primary healthcare settings in Kazakhstan and to explore the underlying factor structure of the nursing practice environment within this context. Methods: A cross-sectional survey was conducted among 468 primary healthcare nurses from six major cities in Kazakhstan. Participants were recruited through professional and educational networks using a targeted convenience sampling strategy. The nursing practice environment was assessed using the Revised Professional Practice Environment (RPPE) scale, while attitudes toward adverse event reporting were measured using the Reporting of Clinical Adverse Events Scale (RoCAES), focusing on the perceived benefits of reporting dimension. Exploratory factor analysis was performed to identify the underlying structure of the RPPE scale. Associations between EFA-derived factors and perceived benefits of adverse event reporting were examined using Spearman correlation analysis and multivariable logistic regression with adjustment for age, gender, city, and professional position. Results: Exploratory factor analysis identified three dimensions of the nursing practice environment: Professional Motivation and Teamwork, Interprofessional Conflict and Workplace Relationships, and Staffing Adequacy. Spearman correlation analysis demonstrated significant associations between all three factors and perceived benefits of adverse event reporting. Factor 1 (Professional Motivation and Teamwork) showed the strongest negative correlation with the outcome (r = −0.562, p < 0.001), followed by Factor 3 (Staffing Adequacy) (r = −0.434, p < 0.001), whereas Factor 2 (Interprofessional Conflict and Workplace Relationships) demonstrated a positive correlation (r = 0.227, p < 0.001). In the multivariable logistic regression model adjusted for age, gender, city, and professional position, Factor 1 was negatively associated with favorable perceptions of adverse event reporting (OR = 0.389, p < 0.001), whereas Factor 2 demonstrated a positive association (OR = 1.763, p = 0.002). Factor 3 and demographic variables were not statistically significant. Conclusions: The findings suggest that nurses’ perceptions of the benefits of adverse event reporting are influenced by multiple dimensions of the nursing practice environment. Exploratory factor analysis identified three organizational dimensions—Professional Motivation and Teamwork, Interprofessional Conflict and Workplace Relationships, and Staffing Adequacy—that were associated with reporting perceptions. After adjustment for demographic characteristics, Professional Motivation and Teamwork and Interprofessional Conflict and Workplace Relationships remained independently associated with perceived benefits of adverse event reporting, whereas demographic factors did not demonstrate significant associations. These findings highlight the importance of organizational conditions, communication processes, and professional engagement in shaping nurses’ attitudes toward adverse event reporting. Efforts to strengthen patient safety reporting systems should therefore extend beyond reporting procedures alone and include broader organizational strategies aimed at improving communication, teamwork, and supportive work environments within primary healthcare settings.