The international AKTIV register presents a detailed description of out- and inpatients with COVID-19 in the Eurasian region. It was found that hospitalized patients had more comorbidities. In addition, these patients were older and there were more men than among outpatients. Among the traditional risk factors, obesity and hypertension had a significant negative effect on prognosis, which was more significant for patients 60 years of age and older. Among comorbidities, CVDs had the maximum negative effect on prognosis, and this effect was more significant for patients 60 years of age and older. Among other comorbidities, type 2 and 1 diabetes, chronic kidney disease, chronic obstructive pulmonary disease, cancer and anemia had a negative impact on the prognosis. This effect was also more significant (with the exception of type 1 diabetes) for patients 60 years and older. The death risk in patients with COVID-19 depended on the severity and type of multimorbidity. Clusters of diseases typical for deceased patients were identified and their impact on prognosis was determined. The most unfavorable was a cluster of 4 diseases, including hypertension, coronary artery disease, heart failure, and diabetes mellitus. The data obtained should be taken into account when planning measures for prevention (vaccination priority groups), treatment and rehabilitation of COVID-19 survivors.

BackgroundThe systemic inflammatory response post-SARS-CoV-2 infection increases pro-inflammatory cytokine production, multi-organ damage, and mortality rates. Mast cells (MC) modulate thrombo-inflammatory disease progression (e.g., deep vein thrombosis) and the inflammatory response post-infection.ObjectiveTo enhance our understanding of the contribution of MC and their proteases in SARS-CoV-2 infection and the pathogenesis of the disease, which might help to identify novel therapeutic targets.MethodsMC proteases chymase (CMA1), carboxypeptidase A3 (CPA3), and tryptase beta 2 (TPSB2), as well as cytokine levels, were measured in the serum of 60 patients with SARS-CoV-2 infection (30 moderate and 30 severe; severity of the disease assessed by chest CT) and 17 healthy controls by ELISA. MC number and degranulation were quantified by immunofluorescent staining for tryptase in lung autopsies of patients deceased from either SARS-CoV-2 infection or unrelated reasons (control). Immortalized human FcεR1+c-Kit+ LUVA MC were infected with SARS-CoV-2, or treated with its viral proteins, to assess direct MC activation by flow cytometry.ResultsThe levels of all three proteases were increased in the serum of patients with COVID-19, and strongly correlated with clinical severity. The density of degranulated MC in COVID-19 lung autopsies was increased compared to control lungs. The total number of released granules and the number of granules per each MC were elevated and positively correlated with von Willebrand factor levels in the lung. SARS-CoV-2 or its viral proteins spike and nucleocapsid did not induce activation or degranulation of LUVA MC in vitro.ConclusionIn this study, we demonstrate that SARS-CoV-2 is strongly associated with activation of MC, which likely occurs indirectly, driven by the inflammatory response. The results suggest that plasma MC protease levels could predict the disease course, and that severe COVID-19 patients might benefit from including MC-stabilizing drugs in the treatment scheme.

BackgroundPrediction of the severity of COVID-19 at its onset is important for providing adequate and timely management to reduce mortality.ObjectiveTo study the prognostic value of damage parameters and cytokines as predictors of severity of COVID-19 using an extensive immunologic profiling and unbiased artificial intelligence methods.MethodsSixty hospitalized COVID-19 patients (30 moderate and 30 severe) and 17 healthy controls were included in the study. The damage indicators high mobility group box 1 (HMGB1), lactate dehydrogenase (LDH), aspartate aminotransferase (AST), alanine aminotransferase (ALT), extensive biochemical analyses, a panel of 47 cytokines and chemokines were analyzed at weeks 1, 2 and 7 along with clinical complaints and CT scans of the lungs. Unbiased artificial intelligence (AI) methods (logistic regression and Support Vector Machine and Random Forest algorithms) were applied to investigate the contribution of each parameter to prediction of the severity of the disease.ResultsOn admission, the severely ill patients had significantly higher levels of LDH, IL-6, monokine induced by gamma interferon (MIG), D-dimer, fibrinogen, glucose than the patients with moderate disease. The levels of macrophage derived cytokine (MDC) were lower in severely ill patients. Based on artificial intelligence analysis, eight parameters (creatinine, glucose, monocyte number, fibrinogen, MDC, MIG, C-reactive protein (CRP) and IL-6 have been identified that could predict with an accuracy of 83−87% whether the patient will develop severe disease.ConclusionThis study identifies the prognostic factors and provides a methodology for making prediction for COVID-19 patients based on widely accepted biomarkers that can be measured in most conventional clinical laboratories worldwide.

Aim. To study the clinical course specifics of coronavirus disease 2019 (COVID-19) and comorbid conditions in COVID-19 survivors 3, 6, 12 months after recovery in the Eurasian region according to the AKTIV register. Material and methods.The AKTIV register was created at the initiative of the Eurasian Association of Therapists. The AKTIV register is divided into 2 parts: AKTIV 1 and AKTIV 2. The AKTIV 1 register currently includes 6300 patients, while in AKTIV 2 — 2770. Patients diagnosed with COVID-19 receiving in- and outpatient treatment have been anonymously included on the registry. The following 7 countries participated in the register: Russian Federation, Republic of Armenia, Republic of Belarus, Republic of Kazakhstan, Kyrgyz Republic, Republic of Moldova, Republic of Uzbekistan. This closed multicenter register with two nonoverlapping branches (in- and outpatient branch) provides 6 visits: 3 in-person visits during the acute period and 3 telephone calls after 3, 6, 12 months. Subject recruitment lasted from June 29, 2020 to October 29, 2020. Register will end on October 29, 2022. A total of 9 fragmentary analyzes of the registry data are planned. This fragment of the study presents the results of the post-hospitalization period in COVID-19 survivors after 3 and 6 months. Results. According to the AKTIV register, patients after COVID-19 are characterized by long-term persistent symptoms and frequent seeking for unscheduled medical care, including rehospitalizations. The most common causes of unplanned medical care are uncontrolled hypertension (HTN) and chronic coronary artery disease (CAD) and/or decompensated type 2 diabetes (T2D). During 3- and 6-month follow-up after hospitalization, 5,6% and 6,4% of patients were diagnosed with other diseases, which were more often presented by HTN, T2D, and CAD. The mortality rate of patients in the post-hospitalization period was 1,9% in the first 3 months and 0,2% for 4-6 months. The highest mortality rate was observed in the first 3 months in the group of patients with class II-IV heart failure, as well as in patients with cardiovascular diseases and cancer. In the pattern of death causes in the post-hospitalization period, following cardiovascular causes prevailed (31,8%): acute coronary syndrome, stroke, acute heart failure. Conclusion. According to the AKTIV register, the health status of patients after COVID-19 in a serious challenge for healthcare system, which requires planning adequate health system capacity to provide care to patients with COVID-19 in both acute and post-hospitalization period.

The organizer of the registers “Dynamics analysis of comorbidities in SARSCoV-2 survivors” (AKTIV) and “Analysis of hospitalizations of comorbid patients infected during the second wave of SARS-CoV-2 outbreak” (AKTIV 2) is the Eurasian Association of Therapists (EAT). Currently, there are no clinical registries in the Eurasian region designed to collect and analyze information on long-term outcomes of COVID-19 survivors with comorbid conditions. The aim of the register is to assess the impact of a novel coronavirus infection on long-term course of chronic non-communicable diseases 3, 6, 12 months after recovery, as well as to obtain information on the effect of comorbidity on the severity of COVID-19. Analysis of hospitalized patients of a possible second wave is planned for register “AKTIV 2”. To achieve this goal, the register will include men and women over 18 years of age diagnosed with COVID-19 who are treated in a hospital or in outpatient basis. The register includes 25 centers in 5 federal districts of the Russian Federation, centers in the Republic of Armenia, the Republic of Kazakhstan, the Republic of Kyrgyzstan, the Republic of Belarus, the Republic of Moldova, and the Republic of Uzbekistan. The estimated capacity of the register is 5400 patients.