ST-elevation myocardial infarction (STEMI) is one of the main reasons for morbidity and mortality worldwide. In addition to the classic biomarker NT-proBNP, new biomarkers like ST2 and Pentraxin-3 (Ptx-3) have emerged as potential tools in stratifying risk in cardiac patients. Indeed, multimarker approaches to estimate prognosis of STEMI patients have been proposed and their potential clinical impact requires investigation. In our study, in 147 patients with STEMI, NT-proBNP as well as serum levels of ST2 and Ptx-3 were evaluated. During two-year follow-up (FU; 734.2 ± 61.2 d) results were correlated with risk for cardiovascular mortality (CV-mortality). NT-proBNP (HR = 1.64, 95% CI = 1.21–2.21, p = 0.001) but also ST2 (HR = 1.000022, 95% CI = 1.00–1.001, p < 0.001) were shown to be reliable predictors of CV-mortality, while the highest predictive power was observed with Ptx-3 (HR = 3.1, 95% CI = 1.63–5.39, p < 0.001). When two biomarkers were combined in a multivariate Cox regression model, relevant improvement of risk assessment was only observed with NT-proBNP+Ptx-3 (AIC = 209, BIC = 214, p = 0.001, MER = 0.75, MEV = 0.64). However, the highest accuracy was seen using a three-marker approach (NT-proBNP + ST2 + Ptx-3: AIC = 208, BIC = 214, p < 0.001, MER = 0.77, MEV = 0.66). In conclusion, after STEMI, ST2 and Ptx-3 in addition to NT-proBNP were associated with the incidence of CV-mortality, with multimarker approaches enhancing the accuracy of prediction of CV-mortality.

Introduction: COVID-19 survivors reveal an increased long-term risk for cardiovascular disease. Biomarkers like troponins and sST-2 improve stratification of cardiovascular risk. Nevertheless, their prognostic value for identifying long-term cardiovascular risk after having survived COVID-19 has yet to be evaluated. Methods: In this single-center study, admission serum biomarkers of sST-2 and hs-TnI in a single cohort of 251 hospitalized COVID-19 survivors were evaluated. Concentrations were correlated with major cardiovascular events (MACE) defined as cardiovascular death and/or need for cardiovascular hospitalization during follow-up after hospital discharge [FU: 415 days (403; 422)]. Results: MACE was a frequent finding during FU with an incidence of 8.4% (cardiovascular death: 2.8% and/or need for cardiovascular hospitalization: 7.2%). Both biomarkers were reliable indicators of MACE (hs-TnI: sensitivity = 66.7% & specificity = 65.7%; sST-2: sensitivity = 33.3% & specificity = 97.4%). This was confirmed in a multivariate proportional-hazards analysis: besides age (HR = 1.047, 95% CI = 1.012–1.084, p = 0.009), hs-TnI (HR = 4.940, 95% CI = 1.904–12.816, p = 0.001) and sST-2 (HR = 10.901, 95% CI = 4.509–29.271, p < 0.001) were strong predictors of MACE. The predictive value of the model was further improved by combining both biomarkers with the factor age (concordance index hs-TnI + sST2 + age = 0.812). Conclusion: During long-term FU, hospitalized COVID-19 survivors, hs-TnI and sST-2 at admission, were strong predictors of MACE, indicating both proteins to be involved in post-acute sequelae of COVID-19.

With the onset of the coronavirus pandemic, it has become clear that patients with diabetes are at risk for more severe and fatal COVID-19. Type 2 diabetes mellitus (T2D) is a major risk factor for adverse COVID-19 outcomes. The goal of study was to assess the characteristics and outcomes of hospitalized patients with COVID-19 with or without T2D in the hospital and at 10-month follow-up (FU). Methods: A total of 2486 hospitalized patients in the first wave of COVID-19 were analyzed according to the absence/presence of T2D, with 2082 (84.1%) patients in the control COVID-19 group and 381 (15.5%) in the T2D group. Twenty-three patients had other types of diabetes and were therefore excluded from the study. In-hospital mortality and cardiovascular endpoints (myocardial infarction, stroke, cardiovascular deaths and hospitalizations and composite endpoints) at the 10-month follow-up were analyzed. To remove bias in patients’ characteristics disproportion, Propensity Score Matching (PSM) was used for hospital and follow-up endpoints. Results. Hospital mortality was considerably greater in T2D than in the control COVID-19 group (13.89% vs. 4.89%, p < 0.0001), and the difference remained after PSM (p < 0.0001). Higher glucose-level T2D patients had a higher mortality rate (p = 0.018). The most significant predictors of hospital death in T2D patients were a high CRP, glucose, neutrophils count, and Charlson Comorbidity Index. The follow-up of patients over 10 months showed a non-significant increase for all endpoints in the T2D group (p > 0.05), and significant increase in stroke (p < 0.042). After the PSM, the difference decreased in stroke (p = 0.090), but became significant in cardiovascular hospitalizations (p = 0.023). Conclusion. In T2D patients with COVID-19, an increase in hospital mortality, stroke and cardiovascular hospitalizations rates in the follow-up was observed.

The article presents results of a study on influence of population dynamics, regional characteristics and the type structure of income on consumption. The ability to investigate spatial dependencies and territorial effects over time was made possible by autoregression spatial models built on panel data. The article describes features of such models, sequence of calculations, and also presents modified tests to justify the choice of the model specification.Calculations were made using data from 83 constituent entities of the Russian Federation (cross-sectional observations) for 2010–2019 (10 time periods). The analysis showed that both population income and retail turnover, which largely determine the level and structure of population consumption, have spatial dependencies. The built spatial error model with fixed effects showed a positive influence on population consumption in the neighboring territories. The model also confirmed previously identified relationships: the positive impact of average per capita income and the negative impact of the Gini index on consumption. The built model with fixed effects allowed to isolate the individual effects of the territories, visualized using cartogram. On the basis of these assessments, several groups of territories with common properties and characteristics have been identified.Unlike previously built models, the authors’ spatial error autoregression model, built on panel data, took into account both the geographical heterogeneity and spatial dependence of average per capita income and retail turnover, expanding the existing understanding of the relationship between consumption and income. This, in turn, enables management decisions that take into account previously undetected features and enhance their validity.

Прогностическая значимость комбинации новых биомаркеров в долгосрочной стратификации неблагоприятных исходов у пациентов с инфарктом миокарда с подъемом сегмента ST

Background: Increased mortality and occurrence of cardiovascular (CV) outcomes during hospitalization and in short-term follow-up for moderate to severe SARS-CoV-2 infection have been associated with male sex, yet data regarding long-term outcomes by sex and COVID-19 variant (Alpha, Delta, and Omicron) are limited. Methods: This prospective study of 4882 patients examines potential differences by sex in the occurrence of primary combined cardiovascular outcomes (CV death, CV hospitalization, myocardial infarction (MI), stroke, pulmonary embolism) as well as secondary outcomes (CV death, cardiovascular hospitalizations, myocardial infarction, stroke, pulmonary embolism) at 18-month follow-up after urgent hospitalization for SARS-CoV-2-associated pneumonia, as well as evaluating for differences during the three COVID-19 waves. Survival rate was analyzed for the entire cohort by sex and SARS-CoV-2 variant and adjusted for age using the multiple Kaplan–Meier method. To compare survival in groups of men and women for each wave, the Gehan–Wilcoxon test was applied with significance p < 0.05. Univariate Cox proportional hazards models were used to search for potential risk factors of CV death at 18-months follow-up separately for men and women in each COVID-19 wave. Results: Men had significantly higher 18-month CV mortality compared to women in the Delta wave (6.13% men vs. 3.62% women, p = 0.017). Although men had higher percentages of all other CV endpoints (excepting pulmonary embolism) at follow-up during the Delta wave, none were significant compared with women, except for the combined CV endpoint (16.87% men vs. 12.61% women, p = 0.017). No significant differences by sex in CV outcomes were seen during the Alpha and Omicron variants. Discrepancies in CV outcomes in demographical data and concomitant disease between the COVID-19 variants of concern existed. Conclusions: Higher male mortality and higher but non-significant incidences of CV outcomes occurred during the Delta wave of the COVID-19 pandemic, with the lowest incidence of CV outcomes observed during the Omicron variant.