It is known that the oxygen saturation of the peripheral blood is determined by the efficiency of the heart, the state of the microcirculatorybed, so position-dependent fluctuations in systolic blood pressure, pressure in the left renal and left adrenal veins,mediated bursts of hormones of the adrenal cortex can affect SO2. There is every reason to believe that SO2 will change in differentstatic positions. Aim. To study position-dependent changes in oxygen saturation based on the study of the pathogenetic effect of venous bloodflow in the “pool” of the left renal vein on the general hemodynamics and hormones of the adrenal cortex. Material and methods. A method for the polypositional assessment of oxygen saturation disturbances in six static states has beendeveloped: standing, sitting, on the back, on the abdomen, on your right side, on your left side. Statistical data processing was carriedout, which made it possible to determine the relationship between the indicators. Results. Polypositional studies of oxygen saturation hemodynamic parameters (SрO2) in six static states revealed the variability ofthe relationships of these groups when comparing them. The correlation was high, statistically significant between diastolic (DBP)and systolic (SBP) pressure, moderate between pulse (Ps) and SBP, pulse and DBP, weak between pulse and saturation. The groupsdivided by body positions relative to the pulse, SBP and DBP did not have a cluster structure. In the pron-position, SO2 had a minimalvalue, significantly different from the data in the other positions. Conclusion. Body position is one of the pathogenetically significant factors regulating blood oxygen saturation, which can helpin the treatment and rehabilitation of patients with respiratory failure (COVID-19). Polypositional saturation measurement in sixstatic states can determine a new, more effective algorithm for the management of patients with respiratory failure, both duringtreatment and during rehabilitation.

Objective: To study the features of the coronavirus infection course in cardiosurgical and thoracic patients to determine the factors potentially affecting the possibility of lethal outcome. To identify the predictors of fatal outcome based on the analyses of the features of the coronavirus infection course in this category of patients.Material and methods: During the analyzed period 80 patients from the departments of thoracic surgery and cardiac surgery were transferred to the infectious diseases department: 20 patients from the cardiac surgery department (CSD) – group 1; 60 patients from the thoracic surgery departments (TSD) – group 2. A control group number 3 consisting of 59 non-thoracic and non-cardiosurgical patients was also formed. According to the disease outcome the patients were divided into two groups: group 1 – fatal outcome, group 2 – recovery.Results: Out of 80 patients, lethal outcome was recorded in 25 cases: 22 patients of the thoracic profile (36% of the total number of transferred from this department) and 3 patients of the cardiosurgical profile (15% of the total number of those transferred from the cardiac surgery department). 20 out of 20 cardiac patients had been operated on the day before, 49 out of 60 thoracic patients also underwent surgery. 3 people from the group of non-operated patients transferred from departments of thoracic surgery died. Moreover, after pneumonectomy, fatal outcome was recorded in 7 out of 8 cases (87.5%).Conclusion: During the analyses of indicators it was revealed that the number of fatal outcomes in patients of the thoracic profile with COVID-19 infection is higher than of the cardiosurgical profile and in the infectious diseases department. Presumably, this is due to the fact that coronavirus infection affects the lungs to a greater extent, and in patients with a thoracic profile (in particular, those who have undergone resection interventions), the volume of the lung parenchyma is initially reduced. This is confirmed particularly by the highest percentage of fatal outcomes after pneumonectomy. Cardiosurgical patients after surgical interventions do not have a reduction in the functioning lung parenchyma, which creates an additional “reserve” for recovery. Moreover, men predominate among patients of the thoracic profile, with the survival rate lower in all groups compared to women. Patients transferred from thoracic departments showed higher rates of systemic inflammation, which indicates a more severe course of the viral infection and the possible development of complications.When analyzing the predictors of lethal outcome, the following factors were identified: male gender and, in general, a more severe course of a viral infection (low saturation, a high percentage of lung lesions on CT, more pronounced changes in laboratory screening). The studied factors are associated with a large number of fatal outcomes in thoracic and cardiac surgery patients. Among the factors that do not affect the prognosis are diabetes mellitus, stroke and myocardial infarction in history.Thus, patients diagnosed with coronavirus infection that developed after thoracic surgery had the most unfavorable prognosis. The revealed patterns are of interest for optimizing the routing of this category of patients in order to prevent coronavirus infection.

With the development of atrial fibrillation (AF), patients with acute coronary syndrome (ACS) are characterized by a twofold increase in the 30-day mortality compared with patients with sinus rhythm. In this regard, there is great interest in developing models of risk stratification to identify adverse outcomes in these patients with a view to more careful monitoring of patients in this group.Material and methods. For the construction of predictive models, a statistical method was used for the classification trees and, the procedure for neural networks implemented in the STATISTICA package. For the construction of prognostic models, a sample was used, consisting of 201 patients with and without fatal outcome; condition of each patient was described by 42 quantitative and qualitative clinical indices. Each patient belonged to one of 3 groups according to the type of AF: new-onset AF in ACS patient, paroxysmal AF, documented in an anamnesis before the episode of ACS and the constant or persistent form of AF.Results. To determine predictors of models predicting the possible fatal outcome of a patient, the Spearman correlation coefficient was used. Examination of the correlations for each of the 3 groups separately allowed to reveal clinical indicators for each group – predictors of predictive models with predominantly moderate correlations to the categorical variable “lethal outcome”. After analyzing the prognostic ability of the developed models, a software module was created in the Microsoft Visual C # 2015 programming environment to determine lethal outcome possibility in patients with ACS in the presence of AF using classification trees and neural networks.Conclusion. It is shown that for patients with ACS in the presence of AF, it is possible to construct mathematically based prognostic models that can reliably predict the lethal outcome possibility in patients based on actual values of clinical indices. In this case, clinical indicators can be both quantitative and qualitative (categorical), breaking patients into certain categories. Similar applications, unlike risk scales, are mathematically justified and can form the basis of systems for supporting decision-making.

Background. According to the literature data, acute coronary syndrome (ACS) in 2-20 % of cases is combined with atrial fibrillation (AF). According to the current guidelines of the European Society of Cardiology (ESC), patients with coexisting AF and ACS should receive dual antiplatelet therapy for the prevention of recurrent cardiovascular events and anticoagulant therapy for the prevention of thromboembolic complications. However, this combination is fraught with the development of hemorrhagic syndrome.Aim. To develop a model and software module for predicting possible bleeding in patients with ACS combined with AF taking three-component antithrombotic therapy.Materials and Methods. To build prognostic models for the development of hemorrhagic syndrome, a statistical method was used for classification trees and the neural network procedure implemented in the STATISTICA package. To build prognostic models, a sample was used consisting of 201 patients with a combination of ACS and AF with and without fatal outcome, the state of which was described by 42 quantitative and qualitative clinical indicators. The control group included 205 patients with ACS and intact sinus rhythm.Results. To identify predictors of predictive models of the possible development of hemorrhagic syndrome in patients with triple antithrombotic therapy, the Spearman correlation coefficient was used. The study of correlations allowed to reveal clinical indicators – predictors of prognostic models. After analyzing the predictive ability of the developed models, a software module was created in the Microsoft Visual C # 2015 programming environment that allows determining the possibility of hemorrhagic syndrome in patients with a combination of ACS and AF using classification trees and neural networks.Сonclusion. A classification model and a software module were developed to predict possible bleeding in patients taking three-component antithrombotic therapy. Models contain both quantitative and qualitative (categorical) clinical indicators. The current level of development of data analysis technologies opens up broad horizons for medicine in solving problems on real medical data, without translating them into scoring risk scales, including prediction of the diagnosis of the disease, stage of the disease, treatment outcome, possible complications, etc. High reliability of such systems can be provided by large volumes of medical data accumulated on servers.

No abstract