Ischemic heart disease (IHD) remains the leading global cause of mortality and morbidity. Current diagnostic approaches face significant limitations in accessibility, cost, invasiveness, and accuracy, creating an urgent need for innovative non-invasive screening methods. To evaluate the ability of a machine learning (ML) model based on dynamic exhaled breath volatile organic compound (VOC) patterns to detect stress-induced myocardial perfusion defects, used here as the study reference outcome related to IHD. This prospective single-center study enrolled 80 participants (31 with stress-induced myocardial perfusion defects confirmed by multidetector computed tomography with perfusion assessment and 49 controls). All participants underwent real-time breath analysis using PTR-TOF-MS-1000 at rest and after bicycle ergometry stress testing. ML models were developed using delta changes in VOC patterns between baseline and post-exertion measurements, with rigorous leave-one-out cross-validation and comprehensive performance metrics. The weighted ensemble classifier achieved an AUC of 0.743 (95% CI: 0.622-0.840), with a sensitivity of 0.774 (95% CI: 0.600-0.905) and a specificity of 0.633 (95% CI: 0.500-0.760). Feature importance analysis identified specific VOCs, particularly those with m/z 94.053731, 90.951122, and 60.055305, which demonstrated consistent diagnostic significance across different temporal measurement points (delta10). This study demonstrates the feasibility of dynamic exhaled breath analysis combined with ML for the detection of IHD. However, the present findings should be considered preliminary and exploratory, as the model was validated internally only. Given the current sensitivity, the approach is not suitable as a standalone screening tool, but may serve as a non-invasive adjunctive or triage-support tool. External validation in independent cohorts is required before clinical implementation.

BACKGROUND Current advances in diagnostic and therapeutic strategies remain insufficient to reduce the prevalence and incidence rate of diabetes mellitus (DM). AIM To investigate any association between single-lead electrocardiography (ECG) parameters and the diagnosis of DM. METHODS A single center study involved participants of Caucasian origin for the period between May 2, 2022 and August 23, 2025 with or without DM and aged >= 18 years. All participants participating in the study passed the cardiologist's, random glucose measurement using a glucometer, single lead-ECG registration (using Cardio-Qvark (R)) and transthoracic echocardiography. Statistical analysis conducted using the R programming language (version 4.5). RESULTS The built logistic regression machine learning model demonstrated diagnostic performance in discriminating (area under the curve) type 1 DM 0.84 (95%CI: 0.76-0.91), type 2 DM 0.69 (95%CI: 0.61-0,76), and healthy control 0.82 (95%CI: 0.76-0.87). CONCLUSION The developed model demonstrates an association between single-lead ECG parameters and diabetes status that can support the clinical identification of individuals who would benefit from confirmatory testing. This is probably attributable to relatively stable and long-term physiological alterations associated with the state of the disease.

BACKGROUND Diabetes mellitus (DM) and the related sequalae remains one of the most frequently reported cause of morbidity and mortality in our era. This returns to the non-sufficient screening methods for DM at early stages.AIM To assess the diagnostic capabilities of the parameters of single lead electrocardiography (ECG) in the diagnosis of DM utilizing machine learning model.METHODS A single center study involved 629 participants with vs without DM. All the study participants passed transthoracic echocardiography, fasting blood glucose measurement, standard 12-lead ECG recording, and single lead ECG registration using the Cardio-Qvark (R) device. A gradient boosting machine model, specifically the XGBoost implementation, was developed using R v4.2 and Python v3.10. The model was trained and validated using a novel cluster-stratified approach - training on three phenotypic clusters and testing on the fourth - to isolate DM-specific ECG signatures from confounding cardiovascular disease.RESULTS The cluster-stratified analysis revealed that the model performed best in cluster 4 (patients with high DM prevalence and significant comorbidities), achieving a sensitivity of 75%, specificity of 83%, and an area under the curve of 88%.CONCLUSION This study demonstrates that a phenotype-stratified approach is crucial for effective ECG-based DM screening. By identifying a specific clinical profile (cluster 4: High comorbidity burden with preserved cardiac function), we developed a model that accurately detects DM from a single-lead ECG. This phenotype-specific strategy overcomes the confounding effect of cardiovascular disease, moving beyond one-size-fits-all algorithms towards a precise and clinically viable tool for non-invasive DM detection in high-risk populations.

Background and Objectives: Given the non-specificity of symptoms and complex methods for diagnosing heart failure, which are not applicable in screening, it is of great importance to develop a simple screening method for identifying systolic dysfunction of the heart based on available biosignals, one of which is a single-channel electrocardiogram (ECG). The method does not require the participation of medical staff. Aim: To create a screening model for detecting left ventricular systolic dysfunction in a complex analysis of single-channel ECG parameters using machine learning algorithms Methods: We included 624 patients aged 18 to 90 years. All patients underwent echocardiography and single-channel I-lead ECG recording using a portable electrocardiograph. The left ventricle ejection fraction (LV EF) was determined in the apical 2-chamber and 4-chamber view using the BIPLANE Simpson method and confirmed by two independent experts. Single-channel ECG analysis was performed using advanced signal processing and machine learning techniques. Results: For identifying LV EF below 52% in men and below 54% in women, the best result was demonstrated by “Lasso regression”: sensitivity 79.2%, specificity 81.7%, AUC = 0.849. For detection of LVEF below 40%, the “Extra Trees” model was the best, with a sensitivity of 83.1% and a specificity of 82.7%, AUC = 0.972. External testing of the algorithm was conducted on a sample of 600 patients. The accuracy was 98%, specificity 98.4%, and sensitivity 93.5%. Conclusions: The results indicate quite high diagnostic accuracy of screening for left ventricular systolic dysfunction when analyzing single-channel ECG parameters using modern signal processing and machine learning technologies.

BACKGROUND:Cardiovascular disease (CVD) and associated sequalae remain the leading cause of disability worldwide. Ischemic heart disease (IHD) and heart failure are the most common etiologies of morbidity and mortality worldwide. This is due to the poor diagnostic and management methods for heart failure and IHD. Early detection of related risk factors through modern strategies is underestimated and requires further research. AIM:To interpret data from the published literature on volatile organic compounds (VOC), including all the methods used to analyze exhaled breath in patients with IHD and heart failure. METHODS:Searches for specific keywords were performed on Scopus and PubMed. A total of 20 studies were identified in breath analysis and IHD and heart failure. The study is registered in PROSPERO (Registration No. CRD42023470556). RESULTS:Considering the articles found, more research is required to gain a full understanding of the role of VOCs in IHD and heart failure. However, the existing literature demonstrates that cardiac metabolic changes can be expressed in exhaled air. The number of papers found is extremely low, making interpretation extremely difficult. CONCLUSION:Exhaled breath analysis can be a novel biomarker for the diagnosis and prevention of heart failure and IHD. Exhaled breath analysis can be used as a mirror to reflect the metabolic changes related to IHD and heart failure.

Background: Diabetic retinopathy is the most common complication of diabetes mellitus and is one of the leading causes of vision impairment globally, which is also relevant for the Russian Federation. Objective: To evaluate the diagnostic efficiency of a convolutional neural network trained for the detection of diabetic retinopathy and estimation of its severity in fundus images of the Russian population. Methods: In this cross-sectional multicenter study, the training data set was obtained from an open source and relabeled by a group of independent retina specialists; the sample size was 60,000 eyes. The test sample was recruited prospectively, 1186 fundus photographs of 593 patients were collected. The reference standard was the result of independent grading of the diabetic retinopathy stage by ophthalmologists. Results: Sensitivity and specificity were 95.0% (95% CI; 90.8-96.4) and 96.8% (95% CI; 95.599.0), respectively; positive predictive value - 98.8% (95% CI; 97.6-99.2); negative predictive value - 87.1% (95% CI, 83.4-96.5); accuracy - 95.9% (95% CI; 93.3-97.1); Kappa score - 0.887 (95% CI; 0.839-0.946); F1score - 0.909 (95% CI; 0.870-0.957); area under the ROC-curve 95.9% (95% CI; 93.3-97.1). There was no statistically significant difference in diagnostic accuracy between the group with isolated diabetic retinopathy and those with hypertensive retinopathy as a concomitant diagnosis. Conclusion: The method for diagnosing DR presented in this article has shown its high accuracy, which is consistent with the existing world analogues, however, this method should prove its clinical efficiency in large multicenter multinational controlled randomized studies, in which the reference diagnostic method would be unified and less subjective than an ophthalmologist.

Aim. To assess the effect of intermittent hypoxic-hyperoxic exposures (IHHE) on the outcomes of on-pump cardiac surgery.Material and methods. This prospective, single-center, randomized, controlled study was conducted in 110 patients with heart valve defects and/or aortic pathology from the cardiac surgery clinic of the I. M. Sechenov First Moscow State Medical University. The total sample was randomly divided into a group of patients who underwent IHHE (n=66) and a control group of patients who underwent placebo procedures with ambient air (n=44). The frequency and structure of intra-and postoperative complications were analyzed within 30 days after surgery. The presence of cognitive impairment, as well as serum troponin I and lactate concentrations were analyzed before and after surgery.Results. Peri- and early postoperative complications such as cardiac death, non-fatal infarction and acute heart failure occurred significantly less frequently in patients treated with IHHE compared with placebo group (1,6% vs 16,7%, p=0,009; 1,6% vs 16,7%, p=0,009; 6,3% vs 33,3%, p<0,001, respectively). The median troponin I values 24 hours after surgery were 1,068 ng/ml (0,388-1,397 ng/ml) in the IHHE group and were significantly lower compared to the control group (1,980 ng/ml (1,068-3,239 ng/ml)). The serum lactate level after surgery was 1,8±0,7 mmol/l in the IHHE group and was significantly lower compared to the control group — 2,4±1,2 (p=0,05). Cognitive function, assessed by MOCA and MMSE tests, turned out to be significantly higher in patients who underwent a preoperative IHHE. No significant complications or serious adverse events were observed during the IHHE procedures.Conclusion. The use of individually adapted hypoxic preconditioning procedures reduces the incidence of peri- and postoperative complications, which is accompanied by a lower ischemia-reperfusion myocardial injury during artificial circulation with preservation of cognitive functions. IHHE procedures ramp up prehabilitation of patients referred for on-pump surgery of heart defects and aortic pathology.

Background: Anti -cancer treatment can be fraught with cardiovascular complications, which is the most common cause of death among oncological survivors. Without appropriate cardiomonitoring during anti -cancer treatment, it becomes challenging to detect early signs of cardiovascular complications. In order to achieve higher survival rates, it is necessary to monitor oncological patients outpatiently after anti -cancer treatment administration. In this regard, we aim to evaluate the efficacy of single -lead ECG remote monitoring to detect cardiotoxicity in cancer patients with minimal cardiovascular diseases after the first cycle of polychemotherapy. Materials and methods: The study included patients 162 patients over 18 years old with first diagnosed different types of solid tumors, planed for adjuvant (within 8 weeks after surgery) or neoadjuvant polychemotherapy. All patients were monitored, outpatiently, during 14-21 days (depending on the regimen of polychemotherapy) after polychemotherapy administration using single -lead ECG. Results: QTc > 500 mc prolongation was detected in 8 patients (6.6 %), first -diagnosed arial fibrillation was detected in 11 patients (9 %) in period after chemotherapy administration. Moreover, left ventricular diastolic dysfunction using single -lead ECG after polychemotherapy was detected in 49 (40.1 %) patients with sensitivity 80 %, specificity 95 %, AUC 0.88 (95 % CI, 0.82-0.93). Conclusions: The side effects of cancer treatment may cause life -threatening risks. Early identification of cardiotoxicity plays a vital role in the solution of this problem. Using portable devices to detect early cardiotoxicity is a simple, convenient and affordable screening method, that can be used for promptly observation of patients.

Recent endeavors have led to the exploration of Machine Learning (ML) to enhance the detection and accurate diagnosis of heart pathologies. This is due to the growing need to improve efficiency in diagnostics and hasten the process of delivering treatment. Several institutions have actively assessed the possibility of creating algorithms for advancing our understanding of atrial fibrillation (AF), a common form of sustained arrhythmia. This means that artificial intelligence is now being used to analyze electrocardiogram (ECG) data. The data is typically extracted from large patient databases and then subsequently used to train and test the algorithm with the help of neural networks. Machine learning has been used to effectively detect atrial fibrillation with more accuracy than clinical experts, and if applied to clinical practice, it will aid in early diagnosis and management of the condition and thus reduce thromboembolic complications of the disease. In this text, a review of the application of machine learning in the analysis and detection of atrial fibrillation, a comparison of the outcomes (sensitivity, specificity, and accuracy), and the framework and methods of the studies conducted have been presented.

Adequate personalized numerical simulation of hemodynamic indices in coronary arteries requires accurate identification of the key parameters. Elastic properties of coronary vessels produce a significant effect on the accuracy of simulations. Direct measurements of the elasticity of coronary vessels are not available in the general clinic. Pulse wave velocity (AoPWV) in the aorta correlates with aortic and coronary elasticity. In this work, we present a neural network approach for estimating AoPWV. Because of the limited number of clinical cases, we used a synthetic AoPWV database of virtual subjects to train the network. We use an additional set of AoPWV data collected from real patients to test the developed algorithm. The developed neural network predicts brachial–ankle AoPWV with a root-mean-square error (RMSE) of 1.3 m/s and a percentage error of 16%. We demonstrate the relevance of a new technique by comparing invasively measured fractional flow reserve (FFR) with simulated values using the patient data with constant (7.5 m/s) and predicted AoPWV. We conclude that patient-specific identification of AoPWV via the developed neural network improves the estimation of FFR from 4.4% to 3.8% on average, with a maximum difference of 2.8% in a particular case. Furthermore, we also numerically investigate the sensitivity of the most useful hemodynamic indices, including FFR, coronary flow reserve (CFR) and instantaneous wave-free ratio (iFR) to AoPWV using the patient-specific data. We observe a substantial variability of all considered indices for AoPWV below 10 m/s and weak variation of AoPWV above 15 m/s. We conclude that the hemodynamic significance of coronary stenosis is higher for the patients with AoPWV in the range from 10 to 15 m/s. The advantages of our approach are the use of a limited set of easily measured input parameters (age, stroke volume, heart rate, systolic, diastolic and mean arterial pressures) and the usage of a model-generated (synthetic) dataset to train and test machine learning methods for predicting hemodynamic indices. The application of our approach in clinical practice saves time, workforce and funds.

ЦЕЛЬ ИССЛЕДОВАНИЯ Оценка риска фибрилляции предсердий (ФП) у пациентов с хронической сердечной недостаточностью (ХСН) при помощи Холтеровского мониторирования и дистанционной записи ЭКГ портативным одноканальным аппаратом. МАТЕРИАЛ И МЕТОДЫ В исследовании приняли участие 100 пациентов с ХСН 1—4-го функционального класса по NYHA с синусовым ритмом. Всем пациентам выполнен расширенный протокол эхокардиографии (ЭхоКГ) с определением глобальной деформации миокарда левого предсердия и диастолической дисфункции. Наряду с суточным мониторированием ЭКГ, проводили дополнительное 3-суточное мониторирование, а также дистанционное наблюдение за параметрами ЭКГ средствами телемедицинских технологий (аппарат CardioQVARK). Срок наблюдения составил 1 мес или до выявления эпизода ФП. РЕЗУЛЬТАТЫ В ходе исследования у 20 (20%) пациентов выявлен пароксизм ФП различными способами. При записи ЭКГ по жалобам выявлено 10% пароксизмов ФП. С помощью холтеровского мониторирования ЭКГ в течение 24, 48 и 72 ч выявлены 5, 10 и 20% случаев ФП соответственно. При использовании одноканального аппарата CardioQVARK 1, 2 и 3 раза в сутки пароксизмы ФП выявлены в 5, 55 и 15% случаев соответственно. Это показывает значительное преимущество переносного аппарата для записи ЭКГ по отношению к другим методам контроля синусового ритма у пациентов с ХСН. Также отмечено значимое изменение показателей у пациентов с выявленной ФП. Фракцию выброса левого предсердия (ФВ ЛП) <36% чаще выявляли у пациентов с пароксизмами ФП (отношение шансов (ОШ) 5,3, 95% ДИ 1,8—15,6, p=0,001). Глобальную деформацию миокарда ЛП <9,5% чаще выявляли у пациентов с пароксизмами ФП (ОШ 12,2, 95% ДИ 3,7—40,2, p=0,003). TDI E med <6,5 см/с также чаще выявляли у пациентов с пароксизмами ФП (ОШ 10,2, 95% ДИ 2,2—47,6, p=0,001). ВЫВОД Риск ФП у пациентов с ХСН высокий (до 20%). При ведении пациентов с ХСН целесообразно использование дистанционных методов контроля ЭКГ для раннего выявления пароксизмов ФП и своевременного начала антикоагулянтной терапии. ЭхоКГ у данной группы пациентов целесообразно проводить с оценкой показателей глобальной деформации миокарда ЛП для определения риска ФП.

ЦЕЛЬ ИССЛЕДОВАНИЯ Разработка моделей машинного обучения для определения снижения систолической функции миокарда левого желудочка (ЛЖ) по данным электрокардиограммы (ЭКГ) и фотоплетизмограммы (ФПГ), зарегистрированным с помощью одноканального ЭКГ-монитора с функцией фотоплетизмографии. МАТЕРИАЛ И МЕТОДЫ В исследование были проспективно включены 400 пациентов. Каждому участнику исследования была выполнена эхокардиография, при которой определяли фракцию выброса (ФВ) ЛЖ и VTI выносящего тракта ЛЖ. Затем проводили регистрацию ЭКГ и ФПГ одноканальным ЭКГ-монитором с функцией фотоплетизмографии, который имеет вид чехла для смартфона. Затем все зарегистрированные записи передавали на единый сервер, где проводили расчет параметров ЭКГ и ФПГ. На основе полученных параметров были построены модели для оценки прогнозирования снижения систолической функции ЛЖ с применением регрессии Лассо и алгоритма «случайный лес». РЕЗУЛЬТАТЫ Были получены модели для ФВ менее 55, 40, 30% и VTI менее 16 и 13 см соответственно. Для каждой модели рассчитывали площадь под ROC-кривой (AUC), чувствительность, специфичность. Для моделей с применением регрессии Лассо результаты были следующими: для ФВ <55% AUC составила 0,857 (чувствительность 0,818, специфичность 0,860); для ФВ <40% — 0,971; для ФВ <30% — 0,982; для VTI <13 — 0,754, для VTI <16 — 0,746. Для моделей, построенных на основе алгоритма «случайный лес», результаты были также достаточно высокими: для ФВ <55% AUC составила 0,913; для ФВ <40% — 0,955; для ФВ <30% — 0,962; для VTI <13 — 0,776, для VTI <16 — 0,782. ВЫВОД Модели на основе машинного обучения, построенные с использованием параметров ЭКГ и ФПГ, показали достаточно высокую точность в оценке снижения систолической функции ЛЖ.

It would be useful to develop a reliable method for the cuffless measurement of blood pressure (BP), as such a method could be made available anytime and anywhere for the effective screening and monitoring of arterial hypertension. The purpose of this study is to evaluate blood pressure measurements through a CardioQVARK device in clinical practice in different patient groups. Methods: This study involved 167 patients aged 31 to 88 years (mean 64.2 ± 7.8 years) with normal blood pressure, high blood pressure, and compensated high blood pressure. During each session, three routine blood pressure measurements with intervals of 30 s were taken using a sphygmomanometer with an appropriate cuff size, and the mean value was selected for comparison. The measurements were carried out by two observers trained at the same time with a reference sphygmomanometer using a Y-shaped connector. In the minute following the last cuff-based measurements, an electrocardiogram (ECG) with an I-lead and a photoplethysmocardiogram were recorded simultaneously for 3 min with the CardioQVARK device. We compared the systolic and diastolic BP obtained from a cuff-based mercury sphygmomanometer and smartphone-case-based BP device: the CardioQVARK monitor. A statistical analysis plan was developed using the IEEE Standard for Wearable Cuffless Blood Pressure Devices. Bland–Altman plots were used to estimate the precision of cuffless measurements. Results: The mean difference between the values defined by CardioQVARK and the cuff-based sphygmomanometer for systolic blood pressure (SBP) was 0.31 ± 3.61, while that for diastolic blood pressure (DBP) was 0.44 ± 3.76. The mean absolute difference (MAD) for SBP was 3.44 ± 2.5 mm Hg, and that for DBP was 3.21 ± 2.82 mm Hg. In the subgroups, the smallest error (less than 3 mm Hg) was observed in the prehypertension group, with a slightly larger error (up to 4 mm Hg) found among patients with a normal blood pressure and stage 1 hypertension. The largest error was found in the stage 2 hypertension group (4–5.5 mm Hg). The largest error was 4.2 mm Hg in the high blood pressure group. We, therefore, did not record an error in excess of 7 mmHg, the upper boundary considered acceptable in the IEEE recommendations. We also did not reach a mean error of 5 mmHg, the upper boundary considered acceptable according to the very recent ESH recommendations. At the same time, in all groups of patients, the systolic blood pressure was determined with an error of less than 5 mm Hg in more than 80% of patients. While this study shows that the CardioQVARK device meets the standards of IEEE, the Bland–Altman analysis indicates that the cuffless measurement of diastolic blood pressure has significant bias. The difference was very small and unlikely to be of clinical relevance for the individual patient, but it may well have epidemiological relevance on a population level. Therefore, the CardioQVARK device, while being worthwhile for monitoring patients over time, may not be suitable for screening purposes. Cuffless blood pressure measurement devices are emerging as a convenient and tolerable alternative to cuff-based devices. However, there are several limitations to cuffless blood pressure measurement devices that should be considered. For instance, this study showed a high proportion of measurements with a measurement error of <5 mmHg, while detecting a small, although statistically significant, bias in the measurement of diastolic blood pressure. This suggests that this device may not be suitable for screening purposes. However, its value for monitoring BP over time is confirmed. Furthermore, and most importantly, the easy measurement method and the device portability (integrated in a smartphone) may increase the self-awareness of hypertensive patients and, potentially, lead to an improved adherence to their treatment. Conclusion: The cuffless blood pressure technology developed in this study was tested in accordance with the IEEE protocol and showed great precision in patient groups with different blood pressure ranges. This approach, therefore, has the potential to be applied in clinical practice.

Background. Analysis of a single-channel electrocardiogram can potentially be used as a screening method to detect systolic dysfunction of the left ventricle. The purpose of our study was to develop a new screening method for detecting a decrease in systolic function of the left ventricle based on single-channel ECG and pulse wave recording using machine learning methods. Materials and methods. The study prospectively included 1039 patients aged 18 years and above. A transthoracic echocardiographic study and 1-minute single channel electrocardiogram were performed for each patient. Spectral analysis of the electrocardiogram based on the Fourier transform. More than 200 parameters were included in machine learning algorithms. Results. For ejection fraction decrease: Lasso regression showed a sensitivity of 92,2%, specificity of 90,1% (AUC=0.920); Random Forest Classifier sensitivity - 88.2%, specificity - 83,3% (AUC=0.834). Algorithm approbation has shown diagnostic accuracy of 90,1% in left ventricular systolic dysfunction. Conclusions. Machine learning models, based on the single lead ECG parameters, as well as age and gender may simplify screening diagnostics of ejection fraction decrease prior to echocardiographic study for in time heart failure diagnostics with high accuracy.

Highlights . The article presents a novel and unique method for assessment of left ventricular systolic disfunction using electrocardiography and photoplethysmography data. This method will improve and simplify the detection of cardiovascular diseases. Aim . To evaluate left ventricular (LV) systolic function using electrocardiogram (ECG) and photoplethysmogram (PPG) signals recorded by a single-channel ECG and PPG-based monitor. Methods . The prospective study included 489 patients over 18 years old with various cardiovascular diseases. All participants underwent echocardiography to determine the main indicators of LV systolic function: LV ejection fraction (EF), LV outflow tract velocity time integral (LVOT VTI), and global longitudinal strain (GLS). Moreover, all patients underwent 1-lead ECG and PPG recording using a single-channel ECG and PPG-based monitor (CardioQvark). The obtained data were analyzed, and ROC curve analysis was performed. Results . We have identified ECG and PPG parameters associated with a decrease in LV contractile function. During the analysis, the ECG, T-wave amplitude (TA) and RonsF parameters showed the highest diagnostic accuracy. With EF below 55%, the area under the ROC curve (AUC) was 0.822, sensitivity (Se) 80%, specificity (Sp) 69% in EF below 55% in TA; in RonsF AUC was 0.743, Se 81%, Sp 77%. With EF below 40%, AUC was 0.915, Se 85%, Sp 83% in TA, and in RonsF AUC was 0.844, Se 82%, Sp 82%. Diagnostic accuracy of ECG signals in case of LVOT VTI lower than 16 cm was measured: TA (AUC 0.755, Se 82%, Sp 70%), RonsF (AUC 0.620, Se 77%, Sp 72%). PPG signals were not significantly associated with reduced EF; however, the pulse wave parameters were associated with lower LVOT VTI: in DP-B0 AUC was 0.687, Se 71%, Sp 74%. The combination of ECG and PPG signals was significantly associated with EF below 40% (RonsF * DP-SEP (AUC 0.877, Se 86%, Sp 85%). ECG and PPG signals were not associated with LV GLS. Conclusion . Assessment of LV systolic function can be performed by analyzing ECG and PPG signals recorded using a portable single-channel CardioQvark monitor.

ЦЕЛЬ ИССЛЕДОВАНИЯ Описать протокол диастолического стресс-теста у пациентов с ишемической болезнью сердца (ИБС) с указанием временных рамок оценки диастолической функции (ДФ) левого желудочка (ЛЖ), а также определить диагностическую точность диастолического стресс-теста по разработанному протоколу в верификации ИБС. МАТЕРИАЛ И МЕТОДЫ Обследованы 80 пациентов старше 18 лет; 12 из них имели подтвержденные данные о перенесенном инфаркте миокарда со снижением систолической функции ЛЖ; 15 — исходно сниженную ДФ ЛЖ. Средний возраст пациентов составил 56,0±12,2 года. Пациенты с абсолютными противопоказаниями к нагрузочному тестированию входили в группу исключения. Всем пациентам после сбора анамнеза, физикального осмотра, измерения артериального давления и электрокардиографии в покое была выполнена стресс-эхокардиография с использованием тредмил-теста. Оценку ДФ начинали на 10, 20, 40 и 60-й секундах после прекращения нагрузки в течение в среднем 12 с. Всем пациентам была выполнена визуализация коронарных артерий с контрастированием. РЕЗУЛЬТАТЫ Наибольшая диагностическая точность динамики параметров ДФ ЛЖ (сравнение данных ДФ до и после нагрузки) в выявлении значимого поражения коронарного русла выявлена на 10—20-й секунде после нагрузки: чувствительность 81,2% (95% ДИ 65,1—92,5), специфичность 89,3% (95% ДИ 82,0—97,7), прогностическая значимость положительного результата 90,2% (95% ДИ 80,0—95,2), прогностическая значимость отрицательного результата 84,6% (95% ДИ 77,2—93,2), диагностическая точность 86,9% (95% ДИ 80,2—94,2). ВЫВОД Оценку параметров ДФ ЛЖ после достижения пика нагрузки при тредмил-тесте оптимально начинать в течение 40 с после нагрузки. Следует получить необходимые изображения в течение 10—15 с. Анализ параметров ДФ, зарегистрированных с 40—60-й секунды после нагрузки, приводит к снижению чувствительности и специфичности диастолического стресс-теста.

Background:Screening for atrial fibrillation has the potential to significantly reduce cardiovascular morbidity and mortality. However, questions in regard to how to screen, on whom to screen, and the optimal setting of screening remain unanswered.Objective:To assess the applicability of a federal cardiac monitoring for atrial fibrillation (AF) screening and remote heart rhythm monitoring in patients at high cardiovascular risk in a mixed urban and rural population in Russia.Methods:This is a prospective multicenter cohort study including 3249 individuals with high cardiovascular risk (mean age 56 ± 12.8 years) from the larger Moscow region who were screened for AF using a smartphone-case based single-lead ECG monitor over a period of 18 month. The endpoints were considered as number of newly diagnosed AF; mean time to diagnosis; number of patients for the first time assigned to anticoagulation therapy; frequency of adverse events.Results:A trial fibrillation was diagnosed in 126 patients, 36 of them for the first time. The mean time to diagnosis was 3 ± 2 days. Of 36 patients, the CHA2DS2-VASc score was ≥1 in 34 cases, ≥2 in 29 cases. Anticoagulant therapy was first induced in 31 patients. One death in newly diagnosed group and two deaths in chronic group were registered. There were a total of eight hospitalizations: one in newly diagnosed and seven in chronic AF patients.Conclusion:Our results indicate that a Federal AF screening system in patients at high cardiovascular risk by using a smartphone-case based single lead ECG which is supported by centrally located ECG specialist and central data management is feasible and reliable when performed in a mixed urban and rural area. Further studies are needed to evaluate the full potential of this approach.

Aims:To investigate the potential of a signal processed by smartphone-case based on single lead electrocardiogram (ECG) for left ventricular diastolic dysfunction (LVDD) determination as a screening method. Methods and Results:We included 446 subjects for sample learning and 259 patients for sample test aged 39 to 74 years for testing with 2D-echocardiography, tissue Doppler imaging and ECG using a smartphone-case based single lead ECG monitor for the assessment of LVDD. Spectral analysis of ECG signals (spECG) has been used in combination with advanced signal processing and artificial intelligence methods. Wavelengths slope, time intervals between waves, amplitudes at different points of the ECG complexes, energy of the ECG signal and asymmetry indices were analyzed. The QTc interval indicated significant diastolic dysfunction with a sensitivity of 78% and a specificity of 65%, a Tpeak parameter >590 ms with 63% and 58%, a T value off >695 ms with 63% and 74%, and QRSfi > 674 ms with 74% and 57%, respectively. A combination of the threshold values from all 4 parameters increased sensitivity to 86% and specificity to 70%, respectively (OR 11.7 [2.7-50.9], P < .001). Algorithm approbation have shown: Sensitivity-95.6%, Specificity-97.7%, Diagnostic accuracy-96.5% and Repeatability-98.8%. Conclusion:Our results indicate a great potential of a smartphone-case based on single lead ECG as novel screening tool for LVDD if spECG is used in combination with advanced signal processing and machine learning technologies.

The aim of this study was to evaluate efficacy and applicability of the “intermittent hypoxic-hyperoxic exposures at rest” (IHHE) protocol as an adjuvant method for metabolic syndrome (MS) cardiometabolic components. A prospective, single-center, randomized controlled clinical study was conducted on 65 patients with MS subject to optimal pharmacotherapy, who were randomly allocated to IHHE or control (CON) groups. The IHHE group completed a 3-week, 5 days/week program of IHHE, each treatment session lasting for 45 min. The CON group followed the same protocol, but was breathing room air through a facial mask instead. The data were collected 2 days before, and at day 2 after the 3-week intervention. As the primary endpoints, systolic (SBP) and diastolic (DBP) blood pressure at rest, as well as arterial stiffness and hepatic tissue elasticity parameters, were selected. After the trial, the IHHE group had a significant decrease in SBP and DBP (Cohen’s d = 1.15 and 0.7, p < 0.001), which became significantly lower (p < 0.001) than in CON. We have failed to detect any pre-post IHHE changes in the arterial stiffness parameters (judging by the Cohen’s d), but after the intervention, cardio-ankle vascular indexes (RCAVI and LCAVI) were significantly lowered in the IHHE group as compared with the CON. The IHHE group demonstrated a medium effect (0.68; 0.69 and 0.71 Cohen’s d) in pre-post decrease of Total Cholesterol (p = 0.04), LDL (p = 0.03), and Liver Steatosis (p = 0.025). In addition, the IHHE group patients demonstrated a statistically significant decrease in pre-post differences (deltas) of RCAVI, LCAVI, all antropometric indices, NTproBNP, Liver Fibrosis, and Steatosis indices, TC, LDL, ALT, and AST in comparison with CON (p = 0.001). The pre-post shifts in SBP, DBP, and HR were significantly correlated with the reduction degree in arterial stiffness (ΔRCAVI, ΔLCAVI), liver fibrosis and steatosis severity (ΔLFibr, ΔLS), anthropometric parameters, liver enzymes, and lipid metabolism in the IHHE group only. Our results suggested that IHHE is a safe, well-tolerated intervention which could be an effective adjuvant therapy in treatment and secondary prevention of atherosclerosis, obesity, and other components of MS that improve the arterial stiffness lipid profile and liver functional state in MS patients.