Background: Catheter-associated urinary tract infections (CA-UTIs) pose a significant challenge in intensive care unit (ICU) patients with COVID-19. Objective: The study aims to assess the prevalence of CA-UTIs, identify the causative pathogens and their resistance profiles, and determine the risk factors and outcomes associated with CA-UTIs in ICU patients with COVID-19. Design: Single-center, retrospective cohort study. Methods: The study included 201 adult ICU patients diagnosed with COVID-19 between March 2020 and July 2021. Patients were categorized into CA-UTI ( n = 56) and non-CA-UTI ( n = 145) groups. Data on demographic characteristics, clinical course, treatment, and outcomes were collected. Logistic regression analysis was used to identify risk factors for CA-UTI. Results: CA-UTIs developed in 28% of patients ( n = 56). Incidence density of 15.8 episodes per 1000 catheter days. The average onset occurrence is 7.2 days after ICU admission. Patients with CA-UTI had longer ICU stays (18.8 days vs 10.5 days, p < 0.001) and more elevated mortality rates (75.0% vs 54.5%, p = 0.010), higher mechanical ventilation (MV) usage (98.2% vs 88.3%, p = 0.027), a longer average duration of MV (16.6 days vs 9.1 days, p < 0.001). Longer ICU and hospital stays were significant risk factors for CA-UTI. Other factors, such as the use of corticosteroids, chronic organ insufficiency or immunocompromized status, female sex, age, diabetes mellitus, and the duration of urinary catheterization, did not show significant associations with CA-UTI risk in this cohort. Gram-negative bacteria, particularly Klebsiella pneumoniae (28 cases), was the most common pathogen, with a high prevalence of multidrug resistance (38.8%) with type ESBL, MBL, NDM, and OXA-48. The occurrence of multidrug resistant (MDR) organisms was 68.8%. Conclusion: The findings of this study underscore the prevalence of CA-UTIs in ICU patients with COVID-19, significantly impacting patient outcomes. Effective infection control and targeted antimicrobial therapy are crucial to managing these infections.

Systemic vaccination with the BNT162b2 mRNA vaccine stimulates the humoral response. Our study aimed to compare the intensity of the humoral immune response, measured by SARS-CoV-2 IgG, SARS-CoV-2 IgM, and S-RBD-neutralizing IgG antibody levels after COVID-19 vaccination versus after SARS-CoV-2 infection. We analyzed 1060 people in the following groups: convalescents; healthy unvaccinated individuals; individuals vaccinated with Comirnaty, AstraZeneca, Moderna, or Johnson & Johnson; and vaccinated SARS-CoV-2 convalescents. The concentrations of SARS-CoV-2 IgG, SARS-CoV-2 IgM, and S-RBD-neutralizing antibodies were estimated in an oncology hospital laboratory by chemiluminescent immunoassay (CLIA; MAGLUMI). Results: (1) We observed a rise in antibody response in both the SARS-CoV-2 convalescent and COVID-19-vaccinated groups. (2) The levels of all antibody concentrations in vaccinated COVID-19 convalescents were significantly higher. (3) We differentiated asymptomatic SARS-CoV-2 convalescents from the control group. Our analysis suggests that monitoring SARS-CoV-2 IgG antibody concentrations is essential as an indicator of asymptomatic COVID-19 and as a measure of the effectiveness of the humoral response in convalescents and vaccinated people. Considering the time-limited effects of post-SARS-CoV-2 infection recovery or vaccination and the physiological half-life, among other factors, we suggest monitoring IgG antibody levels as a criterion for future vaccination.

The Coronavirus Disease 2019 (COVID-19) has caused a global health crisis. Mortality predictors in critically ill patients remain under investigation. A retrospective cohort study included 201 patients admitted to the intensive care unit (ICU) due to COVID-19. Data on demographic characteristics, laboratory findings, and mortality were collected. Logistic regression analysis was conducted with various independent variables, including demographic characteristics, clinical factors, and treatment methods. The study aimed to identify key risk factors associated with mortality in an ICU. In an investigation of 201 patients comprising non-survivors (n = 80, 40%) and Survivors (n = 121, 60%), we identified several markers significantly associated with ICU mortality. Lower Interleukin 6 and White Blood Cells levels at both 24- and 48-hours post-ICU admission emerged as significant indicators of survival. The study employed logistic regression analysis to evaluate risk factors for in-ICU mortality. Analysis results revealed that demographic and clinical factors, including gender, age, and comorbidities, were not significant predictors of in-ICU mortality. Ventilator-associated pneumonia was significantly higher in Survivors, and the use of antibiotics showed a significant association with increased mortality risk in the multivariate model (OR: 11.2, p = 0.031). Our study underscores the significance of monitoring Il-6 and WBC levels within 48 hours of ICU admission, potentially influencing COVID-19 patient outcomes. These insights may reshape therapeutic strategies and ICU protocols for critically ill patients.

Introduction. The Coronavirus Disease 2019 (COVID-19) pandemic has had a significant impact on global healthcare, with high mortality and severe complications remaining a major concern. Understanding the predictors of COVID-19 severity may improve patient management and outcomes. While considerable research has focused on the pathogenesis of the virus and vaccine development, the identification of reliable demographic, clinical and laboratory predictors of severe disease remains critical. Hypothesis. Specific demographic factors, clinical signs and laboratory markers can reliably predict the severity of COVID-19. A comprehensive analysis integrating these predictors could provide a more accurate prognosis and guide timely interventions. Aim. The aim of this study is to identify and evaluate the demographic, clinical and laboratory factors that can serve as reliable predictors of severe COVID-19, thereby aiding in the prediction and prevention of adverse outcomes. Methodology. The methods of analysis, synthesis, generalization and descriptive statistics were used to achieve this objective. Results. The analysis showed that demographic factors such as age over 60 and male sex are significant predictors of severe COVID-19. Clinical predictors include respiratory symptoms, especially dyspnoea, and comorbidities such as hypertension, coronary artery disease, chronic obstructive pulmonary disease, respiratory failure, asthma, diabetes mellitus and obesity. Laboratory markers with high prognostic value include elevated levels of C-reactive protein, interleukin-6, ferritin, neutrophil/lymphocyte ratio, d-dimer, aspartate aminotransferase enzyme and decreased lymphocyte count. Conclusion. The study concludes that a holistic approach incorporating demographic, clinical and laboratory data is essential to accurately predict the severity of COVID-19. This integrated model may significantly improve patient prognosis by facilitating early identification of high-risk individuals and allowing timely, targeted interventions. The results highlight the importance of comprehensive patient assessment in managing and mitigating the impact of COVID-19.