The research on the levelling process of thick slabs based on the electric motor parameters monitoring

For the leveling machines in conditions of a small batch production which is typical for manufacturing sheets of non-ferrous alloys, in particular, titanium, the rollers heating is very influential. In order to expand the technological possibilities of the main equipment in non-stationary thermal working conditions of rollers that is an actual new scientific problem the experimental study of the loading in the hot leveling of slabs made from hardly-deformed titanium alloys was carried out on the 9-roll leveling machine. To achieve this goal the methodology of the complex experimental study of the actual loading definition of the leveling machine drive was developed. The experimental study was carried out for alloys Ti6Al4V (Vt-6s) and PT-3V with the initial heating temperature of the rolled product from 750 to 850 C. The slab leveling process was logged for slabs with the thickness from 20 to 68 mm, the width from 985 to 1720 mm by the number of passes through the leveling machine from 2 to 8. The treatment and analysis of experimental data was conducted on the basis of which the summary of permissibility of assortment expansion of leveling slabs has been formulated. © Published under licence by IOP Publishing Ltd

Прогнозирование неблагоприятного клинического исхода у беременных с тяжелой и крайне тяжелой формами коронавирусной инфекции

The objective was to identify prognostic criteria for unfavorable outcome in pregnant women with severe and extremely severe forms of COVID-19 and to build a model for predicting clinical outcome.Materials and methods. The cohort single-center retrospective study was conducted, which included 83 patients who were treated in the intensive care unit (ICU) from January 1 to December 31, 2021. Of these, 13 patients had an unfavorable outcome – death, and 70 patients with a successful outcome – recovery. The differences in the main clinical and laboratory parameters of patients of both groups during hospitalization in the ICU and on the 3rd day of treatment (Δ – delta) were analyzed.Results. The Cox regression analysis identified laboratory parameters, the difference of which (Δ) on admission to the ICU and on the 3rd day of treatment is associated with the development of the unfavorable outcome (death). These indicators were used as variables in a linear regression equation. The equation for calculating the prognostic index met the criteria of a statistically significant model (sensitivity 84.6 %, specificity 85.7 %, area under the operating characteristic curve (AUROC – Area Under Receiver Operator Curve) – 0.959 (95 % confidence interval [95 % CI] 0.918 – 1.0).Conclusion. The calculation of the prognostic index can be an additional clinical tool that allows one to predict the development of an unfavorable outcome, concentrate the work of a multidisciplinary team, attract additional reserves of a medical institution and/or evacuate such patients to high-level hospitals.Цель – выявить прогностические критерии неблагоприятного исхода у беременных с тяжелой и крайне тяжелой формами COVID-19 и построить модель для прогнозирования клинического исхода.Материалы и методы. Проведено когортное одноцентровое ретроспективное исследование, в которое включены 83 пациентки, находившиеся на лечении в отделении реанимации и интенсивной терапии (ОРИТ) в период с 1 января по 31 декабря 2021 г. Из них – 13 пациенток с неблагоприятным исходом – смертью, и 70 пациенток с благополучным исходом – выздоровлением. Были проанализированы разницы основных клинических и лабораторных показателей пациенток обеих групп при госпитализации в ОРИТ и на третьи сутки лечения (Δ – дельта).Результаты. Проведенный регрессионный анализ по методу Кокса выделил лабораторные показатели, разница которых (Δ) при поступлении в ОРИТ и на третьи сутки лечения связана с развитием неблагоприятного исхода (смерти). Эти показатели использовали в качестве переменных уравнения линейной регрессии. Уравнение расчета прогностического индекса соответствовало критериям статистически значимой модели (чувствительность 84,6 %, специфичность 85,7 %, площадь под рабочей характеристической кривой (AUROC – Area Under Receiver Operator Curve) – 0,959 (95 % доверительный интервал [95 % ДИ] 0,918 – 1,0).Вывод. Расчет прогностического индекса может являться дополнительным клиническим инструментом, позволяющим предполагать развитие неблагоприятного исхода, концентрировать работу мультидисциплинарной бригады, привлекать дополнительные резервы медицинского учреждения и/или осуществлять эвакуацию таких пациенток в стационары высокого уровня оказания помощи

Clinical and organizational factors associated with mortality during the peak of first COVID-19 wave: the global UNITE-COVID study

Purpose: To accommodate the unprecedented number of critically ill patients with pneumonia caused by coronavirus disease 2019 (COVID-19) expansion of the capacity of intensive care unit (ICU) to clinical areas not previously used for critical care was necessary. We describe the global burden of COVID-19 admissions and the clinical and organizational characteristics associated with outcomes in critically ill COVID-19 patients. Methods: Multicenter, international, point prevalence study, including adult patients with SARS-CoV-2 infection confirmed by polymerase chain reaction (PCR) and a diagnosis of COVID-19 admitted to ICU between February 15th and May 15th, 2020. Results: 4994 patients from 280 ICUs in 46 countries were included. Included ICUs increased their total capacity from 4931 to 7630 beds, deploying personnel from other areas. Overall, 1986 (39.8%) patients were admitted to surge capacity beds. Invasive ventilation at admission was present in 2325 (46.5%) patients and was required during ICU stay in 85.8% of patients. 60-day mortality was 33.9% (IQR across units: 20%–50%) and ICU mortality 32.7%. Older age, invasive mechanical ventilation, and acute kidney injury (AKI) were associated with increased mortality. These associations were also confirmed specifically in mechanically ventilated patients. Admission to surge capacity beds was not associated with mortality, even after controlling for other factors. Conclusions: ICUs responded to the increase in COVID-19 patients by increasing bed availability and staff, admitting up to 40% of patients in surge capacity beds. Although mortality in this population was high, admission to a surge capacity bed was not associated with increased mortality. Older age, invasive mechanical ventilation, and AKI were identified as the strongest predictors of mortality

Co-infection and ICU-acquired infection in COIVD-19 ICU patients: a secondary analysis of the UNITE-COVID data set

Background: The COVID-19 pandemic presented major challenges for critical care facilities worldwide. Infections which develop alongside or subsequent to viral pneumonitis are a challenge under sporadic and pandemic conditions; however, data have suggested that patterns of these differ between COVID-19 and other viral pneumonitides. This secondary analysis aimed to explore patterns of co-infection and intensive care unit-acquired infections (ICU-AI) and the relationship to use of corticosteroids in a large, international cohort of critically ill COVID-19 patients.Methods: This is a multicenter, international, observational study, including adult patients with PCR-confirmed COVID-19 diagnosis admitted to ICUs at the peak of wave one of COVID-19 (February 15th to May 15th, 2020). Data collected included investigator-assessed co-infection at ICU admission, infection acquired in ICU, infection with multi-drug resistant organisms (MDRO) and antibiotic use. Frequencies were compared by Pearson's Chi-squared and continuous variables by Mann-Whitney U test. Propensity score matching for variables associated with ICU-acquired infection was undertaken using R library MatchIT using the "full" matching method.Results: Data were available from 4994 patients. Bacterial co-infection at admission was detected in 716 patients (14%), whilst 85% of patients received antibiotics at that stage. ICU-AI developed in 2715 (54%). The most common ICU-AI was bacterial pneumonia (44% of infections), whilst 9% of patients developed fungal pneumonia; 25% of infections involved MDRO. Patients developing infections in ICU had greater antimicrobial exposure than those without such infections. Incident density (ICU-AI per 1000 ICU days) was in considerable excess of reports from pre-pandemic surveillance. Corticosteroid use was heterogenous between ICUs. In univariate analysis, 58% of patients receiving corticosteroids and 43% of those not receiving steroids developed ICU-AI. Adjusting for potential confounders in the propensity-matched cohort, 71% of patients receiving corticosteroids developed ICU-AI vs 52% of those not receiving corticosteroids. Duration of corticosteroid therapy was also associated with development of ICU-AI and infection with an MDRO.Conclusions: In patients with severe COVID-19 in the first wave, co-infection at admission to ICU was relatively rare but antibiotic use was in substantial excess to that indication. ICU-AI were common and were significantly associated with use of corticosteroids

Co-infection and ICU-acquired infection in COIVD-19 ICU patients: a secondary analysis of the UNITE-COVID data set

Background: The COVID-19 pandemic presented major challenges for critical care facilities worldwide. Infections which develop alongside or subsequent to viral pneumonitis are a challenge under sporadic and pandemic conditions; however, data have suggested that patterns of these differ between COVID-19 and other viral pneumonitides. This secondary analysis aimed to explore patterns of co-infection and intensive care unit-acquired infections (ICU-AI) and the relationship to use of corticosteroids in a large, international cohort of critically ill COVID-19 patients. Methods: This is a multicenter, international, observational study, including adult patients with PCR-confirmed COVID-19 diagnosis admitted to ICUs at the peak of wave one of COVID-19 (February 15th to May 15th, 2020). Data collected included investigator-assessed co-infection at ICU admission, infection acquired in ICU, infection with multi-drug resistant organisms (MDRO) and antibiotic use. Frequencies were compared by Pearson’s Chi-squared and continuous variables by Mann–Whitney U test. Propensity score matching for variables associated with ICU-acquired infection was undertaken using R library MatchIT using the “full” matching method. Results: Data were available from 4994 patients. Bacterial co-infection at admission was detected in 716 patients (14%), whilst 85% of patients received antibiotics at that stage. ICU-AI developed in 2715 (54%). The most common ICU-AI was bacterial pneumonia (44% of infections), whilst 9% of patients developed fungal pneumonia; 25% of infections involved MDRO. Patients developing infections in ICU had greater antimicrobial exposure than those without such infections. Incident density (ICU-AI per 1000 ICU days) was in considerable excess of reports from pre-pandemic surveillance. Corticosteroid use was heterogenous between ICUs. In univariate analysis, 58% of patients receiving corticosteroids and 43% of those not receiving steroids developed ICU-AI. Adjusting for potential confounders in the propensity-matched cohort, 71% of patients receiving corticosteroids developed ICU-AI vs 52% of those not receiving corticosteroids. Duration of corticosteroid therapy was also associated with development of ICU-AI and infection with an MDRO. Conclusions: In patients with severe COVID-19 in the first wave, co-infection at admission to ICU was relatively rare but antibiotic use was in substantial excess to that indication. ICU-AI were common and were significantly associated with use of corticosteroids. Trial registration ClinicalTrials.gov: NCT04836065 (retrospectively registered April 8th 2021). Graphical abstract: [Figure not available: see fulltext.]. © 2022, The Author(s)