Yuriy Aleksandrovich Trusov

The purpose of the research is analyze the possibility of using questionnaires: Clinical COPD Questionnaire, COPD Assessment Test and modified Medical Research Council Dyspnoea Scale - for remote assessment of the health status of patients with chronic obstructive pulmonary disease and their integration into a software package developed for the purpose of dynamic monitoring in an outpatient setting. Methods . The software system was developed using the Python programming language. Its architecture includes a real-time automated scoring module, a personal data de-identification system, and a local database. To ensure offline functionality, an offline version of the system was developed with a graphical user interface based on the Tkinter library. The algorithm incorporates mathematical models for interpreting the scales in accordance with international standards. Results . A functional software system was developed that enables the collection and structured storage of data on symptoms, functional status, and mental health status of patients. The system allows for the creation and maintenance of long-term archives of parameters to assess disease progression. Internal testing confirms 100% reproducibility of calculations and stability of data transfer. API integration ensures potential compatibility with state medical information systems. Conclusion . The developed software system aligns with the principles of lean healthcare and can reduce specialist physician time expenditure by 20–30% through the automation of routine calculations. Implementation of the system into outpatient practice facilitates a transition to a proactive model of COPD management, thereby enabling early detection of exacerbations and reducing the rate of rehospitalizations. Adaptation to Russian regulatory requirements positions the system as a promising solution for national telemedicine.

Highlights   Percutaneous coronary intervention (PCI) for chronic total occlusion (CTO) is an effective treatment option for patients with refractory angina, contributing to improved quality of life and reduction of ischemic symptoms, provided that patient selection and procedural planning are carefully performed. Preprocedural planning, including dual-contrast angiography, coronary computed tomography angiography, and the use of validated scoring systems (J-CTO, PROGRESS-CTO, CT-RECTOR), plays a critical role in selecting the optimal interventional strategy and minimizing the risk of complications. An algorithmic approach and multidisciplinary collaboration enhance the success and safety of CTO PCI, particularly when hybrid and global strategies are employed and tailored to the anatomical features of the lesion. Abstract Chronic total occlusions of the coronary arteries (CTO) represent one of the most complex forms of coronary artery disease, occurring in approximately 15–20% of patients with ischemic heart disease undergoing coronary angiography. Despite the long-standing absence of a unified treatment strategy, the interventional approach to CTO has undergone significant transformation in recent decades. This evolution is largely driven by advancements in imaging technologies, improvements in guidewire and catheter systems, and the implementation of algorithmic decision-making in procedural planning. Percutaneous coronary intervention (PCI) for CTO enables effective relief of anginal symptoms, improvement in quality of life, and enhancement of functional status, while minimizing the risk of serious complications. A critical factor in procedural success is thorough preprocedural planning, which includes dual-injection coronary angiography, coronary computed tomography angiography (CTCA), and assessment of anatomical complexity using scores such as J-CTO, PROGRESS-CTO, and CT-RECTOR. The use of hybrid and global CTO management algorithms, tailored to lesion morphology, facilitates individualized strategies and improves procedural outcomes. Operator experience, availability of dedicated equipment, and cohesive team coordination are also pivotal to clinical success. This review summarizes current approaches to the diagnosis, planning, and execution of PCI in CTO cases, emphasizing the importance of a multidisciplinary approach and the role of advanced imaging modalities in enhancing the safety and efficacy of interventional treatment.

The objective was to analyze the literature sources on monitoring and observation of patients with diseases of the gastrointestinal tract (GIT) in daily medical practice using machine learning methods. Methods and materials. To prepare the review, scientific publications were searched in databases such as PubMed, Web of Science, Scopus, CyberLeninka, eLibrary, and Google Scholar. The search strategy included the use of keywords in Russian and English: «diseases of the gastrointestinal tract», «gastroenterological diseases», «artificial intelligence», «machine learning», «deep learning», «patient monitoring», «remote monitoring». The inclusion of original research in the period 2015–2025 is based on an independent assessment by the authors. Results. Of the 594 publications, 9 studies meeting the inclusion criteria were included in the final analysis after screening. Conclusion. AI provides modern approaches to monitoring, diagnosing, and predicting complications of gastrointestinal diseases. The solutions created on its basis are characterized by high diagnostic and forecasting accuracy, often exceeding classical clinical scales, and form the foundation of intelligent decision support systems for doctors.

The objective was to analyze the literature sources on monitoring and observation of patients with diseases of the gastrointestinal tract (GIT) in daily medical practice using machine learning methods. Methods and materials. To prepare the review, scientific publications were searched in databases such as PubMed, Web of Science, Scopus, CyberLeninka, eLibrary, and Google Scholar. The search strategy included the use of keywords in Russian and English: «diseases of the gastrointestinal tract», «gastroenterological diseases», «artificial intelligence», «machine learning», «deep learning», «patient monitoring», «remote monitoring». The inclusion of original research in the period 2015–2025 is based on an independent assessment by the authors. Results. Of the 594 publications, 9 studies meeting the inclusion criteria were included in the final analysis after screening. Conclusion. AI provides modern approaches to monitoring, diagnosing, and predicting complications of gastrointestinal diseases. The solutions created on its basis are characterized by high diagnostic and forecasting accuracy, often exceeding classical clinical scales, and form the foundation of intelligent decision support systems for doctors.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a prevalent chronic condition, with an estimated global prevalence of 32% and rates reaching 37–42% in the Russian Federation. The disease is recognized as an independent risk factor for cardiovascular disease (CVD), which remains the leading cause of mortality in this patient population. This review aims to systematize current evidence on the association between MASLD and CVD, risk stratification approaches, and contemporary therapeutic strategies to mitigate the risk of severe cardiovascular complications within the Russian population. A literature search was conducted in the PubMed/Medline, ScienceDirect, eLibrary, and Google Scholar databases covering the period from 2020 to 2025. A total of 67 publications meeting the inclusion criteria were incorporated into this review. The analysis highlights the pivotal roles of insulin resistance, visceral obesity, systemic inflammation, dyslipidemia, and dysregulation of the gut–liver–heart axis in shaping cardiovascular risk. Liver fibrosis, defined by a FIB-4 index ≥1.3, serves as an independent predictor of CVD, conferring a two-fold increase in risk. MASLD is associated with a 2.19-fold higher risk of coronary heart disease, a 1.88-fold higher risk of stroke, and a 20% increased risk of atrial fibrillation. A stepwise risk stratification algorithm tailored to the Russian healthcare system is proposed, incorporating initial screening (using FLI or CMI), fibrosis assessment (FIB-4), advanced diagnostic refinement, and integration with established cardiovascular risk scales (SCORE2, ACC/AHA). The efficacy of non-pharmacological interventions, including weight reduction of ≥7–10%, adherence to a Mediterranean diet, and regular physical activity, is substantiated by meta-analytic data. Among pharmacological agents, GLP-1 receptor agonists (reducing CVD risk by 17%), SGLT2 inhibitors (improving survival rates to 95%), pioglitazone, and statins (lowering hepatocellular carcinoma risk by 48%) demonstrate robust cardioprotective effects. Emerging avenues include combination therapy with novel agents (resmetirom, efruxifermin) and the application of machine learning algorithms for personalized risk prediction. Identified gaps include a paucity of domestic randomized controlled trials and the absence of a dedicated national MASLD–CVD registry. The findings underscore the necessity of integrating screening protocols into routine preventive health examinations, developing regional patient referral pathways, and enhancing the competency of primary care physicians.

Aim . To review the literature on the effectiveness of artificial intelligence (AI) technologies for screening, diagnosis, and monitoring of cardiovascular diseases. Material and methods. We searched publications in PubMed, Web of Science, Scopus, CyberLeninka, eLibrary, and Google Scholar. The search strategy included the following keywords: "circulatory system diseases", "cardiovascular diseases", "artificial intelligence", "machine learning", "deep learning", "patient monitoring", "remote monitoring". Inclusion of original studies from 2015 to 2025 was based on independent author assessment. R e sults . Of 594 publications, 8 studies meeting the inclusion criteria were included in the final analysis. Conclusion . AI is a tool that is transforming modern methods of monitoring, diagnosing, and predicting cardiovascular outcomes. AI-based solutions demonstrate high diagnostic and prognostic efficacy, often exceeding traditional clinical scores, and form the basis of intelligent decision support systems for physicians.

This work presents a qualitative and quantitative analysis and synthesis of data on the effectiveness of components of organizational models of comprehensive cardiac rehabilitation with the goal of their further integration into the stage of medical and social expert evaluation in patients who have undergone cardiac surgery. The systematic review and meta-analysis were conducted in accordance with the PRISMA-2020 recommendations. Prior to conducting the review, a structured clinical question (PICOS) was formulated. The search for relevant sources was conducted in electronic bibliographic databases [PubMed, EMBASE (via Wiley Online Library), Science Direct, Google Scholar, eLIBRARY] covering the period 2015–2025. The review included randomized (RCT) and non-randomized controlled studies, as well as observational studies (retrospective and prospective cohort studies). After a multi-step screening process, 20 studies were included in the review, some of which were included in the meta-analysis using a random-effects model. Risk of bias assessment was conducted using the Cochrane Risk of Bias 2 (RoB 2) tool and the Newcastle Ottawa Scale (NOS). Meta-analysis of 7 RCTs (n = 547) demonstrated a significant increase in 6-minute walk test (6MWT) distance in groups undergoing comprehensive rehabilitation: the mean difference (MD) was 35.4 m [95% CI 22.1–48.7; p 0.001], and statistical heterogeneity was assessed as moderate (I² = 42%). Analysis of 3 RCTs (n = 324) revealed a trend toward reduced risk of rehospitalizations: the pooled odds ratio was 0.65 (95% CI 0.42–1.01; p = 0.054). Based on the analysis of 3 RCTs (n = 417), no significant improvement in quality of life (according to SF-36/SF-12 questionnaires) was noted: standardized mean difference was −0.17 (95% CI −0.39–0.05; p = 0.13). The results confirm the clinical relevance of structured cardiac rehabilitation programs with organizational, personnel, and technological components for improving functional outcomes. To optimize the medical and social expert process, multidisciplinary assessment, standardized instruments (6MWT), and telehealth technologies should be integrated, as this will help overcome the fragmentation of care and increase the objectivity of expert decisions. Limitations include study variability and a lack of data on long-term outcomes.

Cardiovascular diseases during pregnancy remain a leading cause of maternal morbidity and mortality worldwide. The development of digital technologies and artificial intelligence offers new opportunities to improve risk stratification, early diagnosis, and monitoring of cardiovascular complications in pregnant women. Although effective, conventional diagnostic approaches, including electrocardiography, echocardiography, and biochemical markers are often limited by sensitivity, reproducibility, and feasibility for immediate use during pregnancy. Artificial intelligence models integrating multimodal data—clinical history, imaging, laboratory values, and wearable data—demonstrate the potential to detect subclinical changes that may remain unrecognized using standard diagnostic approaches. Emerging evidence supports the effectiveness of artificial intelligence in detecting arrhythmias, diagnosing peripartum cardiomyopathy, assessing valvular heart disease, and predicting cardiovascular risk and hypertensive disorders of pregnancy, including preeclampsia. Neural network–based models have shown advantages over traditional statistical methods, achieving high predictive accuracy (with areas under the ROC curve 0.90 in some studies). Furthermore, the use of artificial intelligence in the interpretation of medical imaging and phonocardiographic recordings may reduce inter-observer variability and enhance diagnostic efficiency. Despite these promising findings, significant challenges remain, including data quality, algorithmic bias, ethical considerations, regulatory constraints, and limited clinical validation in pregnant populations. Responsible integration of artificial intelligence into obstetric and cardiovascular practice requires interdisciplinary collaboration, rigorous validation, and transparent control. In summary, artificial intelligence technologies possess a transformative potential for optimizing the management of pregnant women with cardiovascular disease and may contribute to reducing maternal morbidity and mortality, provided that ethical and organizational barriers are adequately addressed.

Aim   Analysis of literature on the effect of telemedicine technologies on the adherence of patients with chronic heart failure (CHF) to therapy at the outpatient stage of treatment. Material and methods Publications were searched for in PubMed, Web of Science, Scopus, CyberLeninka, and Google Scholar using the key terms "telemedicine technologies," "remote monitoring," "commitment to treatment," "mobile applications in treatment," "telemedicine," and "telemonitoring." Reports were selected (January 2015-January 2025) independently by two researchers who reached a consensus. Based on the selection of literature sources, four studies that met the search criteria were included in the meta-analysis. The analysis was performed with the Review Manager 5.4.1 and Comprehensive Meta-Analysis 3.0 software using the fixed-effects model (inverse variance method). The results were visualized using a forest plot. Heterogeneity assessment included the Q-test and the I2 index (criterion: Results   The overall adherence rate in the reviewed studies was 64.8% (79/122 patients) in the telemonitoring group and 59.2% (74/125 patients) in the standard observation group, for a total of 61.9% (153/247 patients). Thus, the relative risk of treatment adherence was 1.10 (95% confidence interval (CI) 0.91-1.33; p=0.33). When assessing the homogeneity of the studies with the Pearson’s chi-square test, a statistically non-significant result was obtained (Pearson’s chi-square 2.74 with a degree of freedom of 2, p=0.25), and the heterogeneity index I2=27%, which determines the low heterogeneity of the studies. To identify systematic errors in the studies that used telemonitoring and the standard approach, funnel plot data were accounted for, which indicated an asymmetric distribution of data. Conclusion Telemonitoring with mobile apps and wearable devices is beneficial for patient adherence to treatment. Active implementation of these technologies is required to improve the monitoring of the use of prescribed therapy by patients with CHF.

Aim. To analyze literature data on use of photoplethysmography (PPG) at the outpatient stage for remote monitoring of heart rate, arrhythmias, blood pressure (BP), temperature, respiratory rate.Research design. The search was carried out using the PubMed/Medline, Web of Science, Scopus and Google Scholar databases from 2015 to 2025.Results. A small number of studies were included in the meta-analysis. Mean values of systolic and diastolic BP depending on the measurement method used were presented in two studies, which implies high inconsistency in the presented studies and determines the need for a balanced interpretation of the pooled estimate of the difference in mean values for these studies.Absolute values determining the incidence of AF are indicated in four studies.Conclusion. Our systematic review and meta-analysis showed PPG effectiveness for arrhythmia and controlling systolic and diastolic BP at the outpatient stage.