Aims Patients with cardiac disease are considered high risk for poor outcomes following hospitalization with COVID-19. The primary aim of this study was to evaluate heterogeneity in associations between various heart disease subtypes and in-hospital mortality. Methods and results We used data from the CAPACITY-COVID registry and LEOSS study. Multivariable Poisson regression models were fitted to assess the association between different types of pre-existing heart disease and in-hospital mortality. A total of 16 511 patients with COVID-19 were included (21.1% aged 66–75 years; 40.2% female) and 31.5% had a history of heart disease. Patients with heart disease were older, predominantly male, and often had other comorbid conditions when compared with those without. Mortality was higher in patients with cardiac disease (29.7%; n = 1545 vs. 15.9%; n = 1797). However, following multivariable adjustment, this difference was not significant [adjusted risk ratio (aRR) 1.08, 95% confidence interval (CI) 1.02–1.15; P = 0.12 (corrected for multiple testing)]. Associations with in-hospital mortality by heart disease subtypes differed considerably, with the strongest association for heart failure (aRR 1.19, 95% CI 1.10–1.30; P < 0.018) particularly for severe (New York Heart Association class III/IV) heart failure (aRR 1.41, 95% CI 1.20–1.64; P < 0.018). None of the other heart disease subtypes, including ischaemic heart disease, remained significant after multivariable adjustment. Serious cardiac complications were diagnosed in <1% of patients. Conclusion Considerable heterogeneity exists in the strength of association between heart disease subtypes and in-hospital mortality. Of all patients with heart disease, those with heart failure are at greatest risk of death when hospitalized with COVID-19. Serious cardiac complications are rare during hospitalization.

The availability of simple, accurate, and affordable cuffless blood pressure (BP) devices has the potential to greatly increase the compliance with measurement recommendations and the utilization of BP measurements for BP telemonitoring. The aim of this study is to evaluate the correlation between findings from routine BP measurements using a conventional sphygmomanometer with the results from a portable ECG monitor combined with photoplethysmography (PPG) for pulse wave registration in patients with arterial hypertension. Methods: The study included 500 patients aged 32–88 years (mean 64 ± 7.9 years). Mean values from three routine BP measurements by a sphygmomanometer with cuff were selected for comparison; within one minute after the last measurement, an electrocardiogram (ECG) was recorded for 3 min in the standard lead I using a smartphone-case based single-channel ECG monitor (CardioQVARK®-limited responsibility company “L-CARD”, Moscow, Russia) simultaneously with a PPG pulse wave recording. Using a combination of the heart signal with the PPG, levels of systolic and diastolic BP were determined based on machine learning using a previously developed and validated algorithm and were compared with sphygmomanometer results. Results: According to the Bland–Altman analysis, SD for systolic BP was 3.63, and bias was 0.32 for systolic BP. SD was 2.95 and bias was 0.61 for diastolic BP. The correlation between the results from the sphygmomanometer and the cuffless method was 0.89 (p = 0.001) for systolic and 0.87 (p = 0.002) for diastolic BP. Conclusion: Blood pressure measurements on a smartphone-case without a cuff are encouraging. However, further research is needed to improve the accuracy and reliability of clinical use in the majority of patients.

Background: Until recently, Russia did not utilize noninvasive fractional flow reserve (FFR) assessment. We developed an automated algorithm for noninvasive assessment of FFR based on a one-dimensional (1D) mathematical modeling. Objective: The research aims to evaluate the diagnostic accuracy of this algorithm. Methods: The study enrolled 80 patients: 16 of them underwent 64-slice computed tomography – included retrospectively, 64 – prospectively, with a 640-slice CT scan. Specialists processed CT images and evaluated noninvasive FFR. Ischemia was confirmed if FFR < 0.80 and disproved if FFR ≥ 0.80. The prospective group of patients was hospitalized for invasive FFR assessment as a reference standard. If ischemic, patients underwent stent implantation. In the retrospective group, patients already had invasive FFR values. Statistical analysis was performed using GraphPad Prism 8. We compared two methods using a Bland–Altman plot and per-vessel ROC curve analysis. Considering the abnormality of distribution by the Kolmogorov-Smirnov test, we have used Spearman’s rank correlation coefficient. Results: During data processing, three patients of the retrospective and 46 patients of the prospective group were excluded. The sensitivity of our method was 66.67% (95% CI: 46.71–82.03); the specificity was 78.95% (95% CI: 56.67–91.49), p = 0.0052, in the per-vessel analysis. In per-patient analysis, the sensitivity was 69.57% (95% CI: 49.13–84.40); the specificity was 87.50% (95% CI: 52.91–99.36), p = 0.0109. The area under the ROC curve in the per-vessel analysis was 77.52% (95% CI: 66.97–88.08), p < 0.0001. Conclusion: The obtained indices of sensitivity, specificity, PPV, and NPV are, in general, comparable to those in other studies. Moreover, the noninvasive values of FFR yielded a high correlation coefficient with the invasive values. However, the AUC was not high enough, 77.52 (95% CI: 66.97–88.08), p < 0.0001. The discrepancy is probably attributed to the initial data heterogeneity and low statistical power.

Non-coding RNAs reflect many biological processes in the human body, including athero-sclerosis. In a cardiology outpatient department cohort (N = 83), we aimed to compare the levels of circulating microRNAs in groups with vulnerable plaques (N = 22), stable plaques (N = 23) and plaque-free (N = 17) depending on coronary computed tomography angiography and to evaluate associations of microRNA levels with calculated cardiovascular risks (CVR), based on the SCORE2 (+OP), ACC/AHA, ATP-III and MESA scales. Coronary computed tomography was performed on a 640-slice computed tomography scanner. Relative plasma levels of microRNA were assessed via a real-time polymerase chain reaction. We found significant differences in miR-143-3p levels (p = 0.0046 in plaque-free vs. vulnerable plaque groups) and miR-181b-5p (p = 0.0179 in stable vs. vulnerable plaques groups). Analysis of microRNA associations with CVR did not show significant differences for SCORE2 (+OP) and ATPIII scales. MiR-126-5p and miR-150-5p levels were significantly higher (p < 0.05) in patients with ACC/AHA risk >10% and miR-145-5p had linear relationships with ACC/AHA score (adjusted p = 0.0164). The relative plasma level of miR-195 was higher (p < 0.05) in patients with MESA risk > 7.5% and higher (p < 0.05) in patients with zero coronary calcium index (p = 0.036). A linear relationship with coronary calcium was observed for miR-126-3p (adjusted p = 0.0484). A positive correlation with high coronary calcium levels (> 100 Agatson units) was found for miR-181-5p (p = 0.036). Analyzing the biological pathways of these microRNAs, we suggest that miR-143-3p and miR-181-5p can be potential markers of the atherosclerosis process. Other miRNAs (miR-126-3p, 126-5p, 145-5p, 150-5p, 195-5p) can be considered as potential cardiovascular risk modifiers, but it is necessary to validate our results in a large prospective trial.

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.