Modernization of Regression Models to Predict the Number of Deaths from the New Coronavirus Infection.

The aim of the study was to modernize the existing prognostic regression models in the context of expanding knowledge about the new coronavirus infection. Materials and Methods: The modification of models and the increase in their predictive ability are based on collecting the available data from international and Russian databases. We calculated the traditional descriptive statistics and used the linear regression analysis for modeling. The work was performed using the IBM SPSS Statistics 26.0 and the R 3.6.0 (RStudio) software. Results: Manifestations of the COVID-19 epidemic process in several countries were studied; special attention was put to the number of deaths associated with the infection. A significant proportion of severe cases were noted among patients both in Russia and elsewhere. Considering that the disease incidence has reached its peak in China and Italy, we were able to improve the previously published (Sovremennye tehnologii v medicine 2020, Vol. 12, No.2) regression models and to compare their performance. The first modified model is based on the absolute increase in new cases of the infection: its regression coefficient is 0.16 (95% CI 0.137-0.181). In the extended version of the updated model, we additionally considered cases of aggravated COVID-19: the regression coefficients were 0.128 (95% CI 0.103-0.153) for model 2 and 0.053 (95% CI 0.029-0.077) for model 1.1; p=0.0001. Conclusion: Based on the most recent data (from January to May 2020) on the incidence of COVID-19 in the world, we have developed more specific versions of the basic and extended regression models of lethal outcomes. The resulting models are optimized and extrapolated to the current epidemiological situation; they will allow us to improve our analytical approach. For that purpose, data collection is currently ongoing.

Application of Phagotherapy in the Treatment of Burn Patients (Review).

Treatment of patients with a burn injury is a complex process involving multicomponent multidirectional intensive therapy of the majority of organs and systems damaged by thermal effects on the skin, alternating with repeated surgical interventions aimed at removing nonviable tissues with subsequent plastic closure of wound defects. After the recovery from the burn shock, local infectious complications are considered to be the leading problem that decelerates the process of recovery and is the main cause of lethal outcomes. Since the skin integrity is broken, microorganisms penetrate readily into the internal environment of the human organism resulting in a septic state with multiple organ failure. A widespread and often uncontrollable use of antibacterial drugs in medical practice has led to the emergence of multiple drug resistance (MDR) in microorganisms. Introduction of drugs made on the basis of bacteriophages into practice is presently becoming increasingly important. This is confirmed by the growing interest in this field of pharmacology, the development of special programs aimed at studying the processes of phage and bacterial cell interaction. This review presents the main types of bacteria pertaining to MDR pathogens, principles of their classification, and the risk factors for infecting patients. The mechanisms of the selective action of phage particles on a bacterial cell and the possibility of using phage therapy in the treatment of burn injury (experimental and clinical data) based on the analysis of foreign literature are demonstrated as well as new positive properties of phages related to the changes in the macroorganism immune status caused by the interaction with bacteriophage particles.

Regression Models Predicting the Number of Deaths from the New Coronavirus Infection.

Predicting the development of epidemic infection caused by the COVID-19 coronavirus is a matter of the utmost urgency for health care and effective anti-epidemic measures. Given the rapidly changing initial information and the ambiguous quality of data coming from various sources, it is important to quickly optimize the existing prognostic models by using more sophisticated algorithms. The aim of the study is to test the originally developed mathematical algorithms for predicting the development of the COVID-19 epidemic process. MATERIALS AND METHODS: To assess the situation in China, Italy, and the USA, we used the information from Russian- and English-language sources available in official websites. The generally accepted descriptive statistics were used; mathematical modeling was based on linear regression. Statistical data processing was performed using the IBM SPSS Statistics 24.0 and R (RStudio) 3.6.0. RESULTS: We found significant differences not only in the incidence rate of COVID-19 in the countries in question, but also in the death rate. The risk of death associated with COVID-19 is high due to the high number of severe clinical cases of the disease reported from these countries.Two preliminary regression models were created. The first, initial model was based on the increase in new cases of infection - this factor was significantly associated with the outcome; the regression coefficient was 0.02 (95% CI 0.01-0.03). In the second, expanded model, in addition to the increase in new cases, the increase in the number of severe forms of infection was also considered; the regression coefficients were 0.017 (95% CI 0.012-0.022) and 0.01 (95% CI 0.008-0.011), respectively. Adding the second variable contributed to a more accurate description of the available data by the model. CONCLUSION: The developed regression models for infection control and predicting the number of lethal outcomes can be successfully used under conditions of spreading diseases from the group of "new infections" when primary data received from various sourced are changing rapidly and updates of the information are continually required. In addition, our initial model can produce a preliminary assessment of the situation, and the expanded model can increase the accuracy and improve the analytic algorithm.