Browsing by Author "Vujovic, Ankica (57205475784)"

Chronic pulmonary aspergillosis (CPA) is a chronic progressive lung disease associated with a poor prognosis and a 5-year mortality rate of approximately 40–50%. The disease is characterized by slowly progressive destruction of the lung parenchyma, in the form of multiple cavities, nodules, infiltrates or fibrosis. CPA can be challenging to diagnose due to its non-specific symptoms and similarities with other respiratory conditions combined with the poor awareness of the medical community about the disease. This can result in delayed treatment even for years and worsening of the patient’s condition. Serological tests certainly play a significant role in diagnosing CPA but cannot be interpreted without radiological confirmation of CPA. Although many data are published on this hot topic, there is yet no single definitive test for diagnosing CPA, and a multidisciplinary approach which involves a combination of clinical picture, radiological findings, microbiological results and exclusion of other mimicking diseases, is essential for the accurate diagnosis of CPA. © 2023 by the authors.

Chronic pulmonary aspergillosis (CPA) is a chronic progressive lung disease associated with a poor prognosis and a 5-year mortality rate of approximately 40–50%. The disease is characterized by slowly progressive destruction of the lung parenchyma, in the form of multiple cavities, nodules, infiltrates or fibrosis. CPA can be challenging to diagnose due to its non-specific symptoms and similarities with other respiratory conditions combined with the poor awareness of the medical community about the disease. This can result in delayed treatment even for years and worsening of the patient’s condition. Serological tests certainly play a significant role in diagnosing CPA but cannot be interpreted without radiological confirmation of CPA. Although many data are published on this hot topic, there is yet no single definitive test for diagnosing CPA, and a multidisciplinary approach which involves a combination of clinical picture, radiological findings, microbiological results and exclusion of other mimicking diseases, is essential for the accurate diagnosis of CPA. © 2023 by the authors.

Changes in gut microbiota influence both the gut and liver, which are strictly connected by the so-called “gut-liver axis”. The gut microbiota acts as a major determinant of this relationship in the onset and clinical course of liver diseases. According to the results of several studies, gut dysbiosis is linked to viral hepatitis, mainly hepatitis C virus and hepatitis B virus infection. Gut bacteria-derived metabolites and cellular components are key molecules that affect liver function and modulate the pathology of viral hepatitis. Recent studies showed that the gut microbiota produces various molecules, such as peptidoglycans, lipopolysaccharides, DNA, lipoteichoic acid, indole-derivatives, bile acids, and trimethylamine, which are translocated to the liver and interact with liver immune cells causing pathological effects. Therefore, the existence of crosstalk between the gut microbiota and the liver and its implications on host health and pathologic status are essential factors impacting the etiology and therapeutic approach. Concrete mechanisms behind the pathogenic role of gut-derived components on the pathogenesis of viral hepatitis remain unclear and not understood. In this review, we discuss the current findings of research on the bidirectional relationship of the components of gut microbiota and the progression of liver diseases and viral hepatitis and vice versa. Moreover, this paper highlights the current therapeutic and preventive strategies, such as fecal transplantation, used to restore the gut microbiota composition and so improve host health. © The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.

Introduction: The coronavirus disease 2019 (COVID-19) pandemic started in March 2020. Since then, there has been an urgent need for effective therapeutic methods to manage the disease. We aimed to assess the effectiveness of molnupiravir in reducing the need for hospitalization in at-risk, non-hospitalized COVID-19 patients. Methodology: This was a single-center, non-randomized, observational retrospective study of non-hospitalized patients with confirmed COVID-19, treated at the Clinic for Infectious and Tropical Diseases, University Clinical Center in Belgrade, Serbia. Results: The study was conducted between 15 December 2021 and 15 February 2022 and included 320 patients. Of these, 165 (51.6%) received treatment with molnupiravir. The study and control groups were similar in gender and age distribution. The study group had a higher proportion of vaccination (75.2% vs. 51%, p < 0.001). There was no statistically significant difference in presence of comorbidity within the groups. Majority of the patients who received molnupiravir did not require hospitalization; and this was statistically significant in comparison to control group (92.7 vs. 24.5%, p < 0.001). Oxygen supplementation was less frequently required in the study group compared to the control group (0.6% vs. 31%, p < 0.001). During the follow-up period of 12.12 ± 3.5 days, significantly less patients from the study group were admitted to the intensive care unit (p < 0.001). Molnupiravir significantly reduced the risk of hospitalization by 97.9% (HR 0.021; 95% CI 0.005-0.089; p < 0.001). Conclusions: Molnupiravir is an effective therapy in preventing the development of severe forms of COVID-19 and hospitalization. © 2024 Gmizic et al.

Introduction: The coronavirus disease 2019 (COVID-19) pandemic started in March 2020. Since then, there has been an urgent need for effective therapeutic methods to manage the disease. We aimed to assess the effectiveness of molnupiravir in reducing the need for hospitalization in at-risk, non-hospitalized COVID-19 patients. Methodology: This was a single-center, non-randomized, observational retrospective study of non-hospitalized patients with confirmed COVID-19, treated at the Clinic for Infectious and Tropical Diseases, University Clinical Center in Belgrade, Serbia. Results: The study was conducted between 15 December 2021 and 15 February 2022 and included 320 patients. Of these, 165 (51.6%) received treatment with molnupiravir. The study and control groups were similar in gender and age distribution. The study group had a higher proportion of vaccination (75.2% vs. 51%, p < 0.001). There was no statistically significant difference in presence of comorbidity within the groups. Majority of the patients who received molnupiravir did not require hospitalization; and this was statistically significant in comparison to control group (92.7 vs. 24.5%, p < 0.001). Oxygen supplementation was less frequently required in the study group compared to the control group (0.6% vs. 31%, p < 0.001). During the follow-up period of 12.12 ± 3.5 days, significantly less patients from the study group were admitted to the intensive care unit (p < 0.001). Molnupiravir significantly reduced the risk of hospitalization by 97.9% (HR 0.021; 95% CI 0.005-0.089; p < 0.001). Conclusions: Molnupiravir is an effective therapy in preventing the development of severe forms of COVID-19 and hospitalization. © 2024 Gmizic et al.

Introduction: Individual case reports are the main asset in pharmacovigilance signal management. Signal validation is the first stage after signal detection and aims to determine if there is sufficient evidence to justify further assessment. Throughout signal management, a prioritization of signals is continually made. Routinely collected health data can provide relevant contextual information but are primarily used at a later stage in pharmacoepidemiological studies to assess communicated signals. Objective: The aim of this study was to examine the feasibility and utility of analysing routine health data from a multinational distributed network to support signal validation and prioritization and to reflect on key user requirements for these analyses to become an integral part of this process. Methods: Statistical signal detection was performed in VigiBase, the WHO global database of individual case safety reports, targeting generic manufacturer drugs and 16 prespecified adverse events. During a 5-day study-a-thon, signal validation and prioritization were performed using information from VigiBase, regulatory documents and the scientific literature alongside descriptive analyses of routine health data from 10 partners of the European Health Data and Evidence Network (EHDEN). Databases included in the study were from the UK, Spain, Norway, the Netherlands and Serbia, capturing records from primary care and/or hospitals. Results: Ninety-five statistical signals were subjected to signal validation, of which eight were considered for descriptive analyses in the routine health data. Design, execution and interpretation of results from these analyses took up to a few hours for each signal (of which 15–60 minutes were for execution) and informed decisions for five out of eight signals. The impact of insights from the routine health data varied and included possible alternative explanations, potential public health and clinical impact and feasibility of follow-up pharmacoepidemiological studies. Three signals were selected for signal assessment, two of these decisions were supported by insights from the routine health data. Standardization of analytical code, availability of adverse event phenotypes including bridges between different source vocabularies, and governance around the access and use of routine health data were identified as important aspects for future development. Conclusions: Analyses of routine health data from a distributed network to support signal validation and prioritization are feasible in the given time limits and can inform decision making. The cost–benefit of integrating these analyses at this stage of signal management requires further research. © 2023, The Author(s).

Introduction: Individual case reports are the main asset in pharmacovigilance signal management. Signal validation is the first stage after signal detection and aims to determine if there is sufficient evidence to justify further assessment. Throughout signal management, a prioritization of signals is continually made. Routinely collected health data can provide relevant contextual information but are primarily used at a later stage in pharmacoepidemiological studies to assess communicated signals. Objective: The aim of this study was to examine the feasibility and utility of analysing routine health data from a multinational distributed network to support signal validation and prioritization and to reflect on key user requirements for these analyses to become an integral part of this process. Methods: Statistical signal detection was performed in VigiBase, the WHO global database of individual case safety reports, targeting generic manufacturer drugs and 16 prespecified adverse events. During a 5-day study-a-thon, signal validation and prioritization were performed using information from VigiBase, regulatory documents and the scientific literature alongside descriptive analyses of routine health data from 10 partners of the European Health Data and Evidence Network (EHDEN). Databases included in the study were from the UK, Spain, Norway, the Netherlands and Serbia, capturing records from primary care and/or hospitals. Results: Ninety-five statistical signals were subjected to signal validation, of which eight were considered for descriptive analyses in the routine health data. Design, execution and interpretation of results from these analyses took up to a few hours for each signal (of which 15–60 minutes were for execution) and informed decisions for five out of eight signals. The impact of insights from the routine health data varied and included possible alternative explanations, potential public health and clinical impact and feasibility of follow-up pharmacoepidemiological studies. Three signals were selected for signal assessment, two of these decisions were supported by insights from the routine health data. Standardization of analytical code, availability of adverse event phenotypes including bridges between different source vocabularies, and governance around the access and use of routine health data were identified as important aspects for future development. Conclusions: Analyses of routine health data from a distributed network to support signal validation and prioritization are feasible in the given time limits and can inform decision making. The cost–benefit of integrating these analyses at this stage of signal management requires further research. © 2023, The Author(s).

Hospital-acquired infections (HAIs) are a global public health concern. As the COVID-19 pandemic continues, its contribution to mortality and antimicrobial resistance (AMR) grows, particularly in intensive care units (ICUs). A two-year retrospective study from April 2019–April 2021 was conducted in an adult ICU at the Hospital for Infectious and Tropical Diseases, Belgrade, Serbia to assess causative agents of HAIs and AMR rates, with the COVID-19 pandemic ensuing halfway through the study. Resistance rates >80% were observed for the majority of tested antimicrobials. In COVID-19 patients, Acinetobacter spp. was the dominant cause of HAIs and more frequently isolated than in non-COVID-19 patients. (67 vs. 18, p = 0.001). Also, resistance was higher for imipenem (56.8% vs. 24.5%, p < 0.001), meropenem (61.1% vs. 24.3%, p < 0.001) and ciprofloxacin (59.5% vs. 36.9%, p = 0.04). AMR rates were aggregated with findings from our previous study to identify resistance trends and establish empiric treatment recommendations. The increased presence of Acinetobacter spp. and a positive trend in Klebsiella spp. resistance to fluoroquinolones (R2 = 0.980, p = 0.01) and carbapenems (R2 = 0.963, p = 0.02) could have contributed to alarming resistance rates across bloodstream infections (BSIs), pneumonia (PN), and urinary tract infections (UTIs). Exceptions were vancomycin (16.0%) and linezolid (2.6%) in BSIs; tigecycline (14.3%) and colistin (0%) in PNs; and colistin (12.0%) and linezolid (0%) in UTIs. COVID-19 has changed the landscape of HAIs in our ICUs. Approval of new drugs and rigorous surveillance is urgently needed. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Hospital-acquired infections (HAIs) are a global public health concern. As the COVID-19 pandemic continues, its contribution to mortality and antimicrobial resistance (AMR) grows, particularly in intensive care units (ICUs). A two-year retrospective study from April 2019–April 2021 was conducted in an adult ICU at the Hospital for Infectious and Tropical Diseases, Belgrade, Serbia to assess causative agents of HAIs and AMR rates, with the COVID-19 pandemic ensuing halfway through the study. Resistance rates >80% were observed for the majority of tested antimicrobials. In COVID-19 patients, Acinetobacter spp. was the dominant cause of HAIs and more frequently isolated than in non-COVID-19 patients. (67 vs. 18, p = 0.001). Also, resistance was higher for imipenem (56.8% vs. 24.5%, p < 0.001), meropenem (61.1% vs. 24.3%, p < 0.001) and ciprofloxacin (59.5% vs. 36.9%, p = 0.04). AMR rates were aggregated with findings from our previous study to identify resistance trends and establish empiric treatment recommendations. The increased presence of Acinetobacter spp. and a positive trend in Klebsiella spp. resistance to fluoroquinolones (R2 = 0.980, p = 0.01) and carbapenems (R2 = 0.963, p = 0.02) could have contributed to alarming resistance rates across bloodstream infections (BSIs), pneumonia (PN), and urinary tract infections (UTIs). Exceptions were vancomycin (16.0%) and linezolid (2.6%) in BSIs; tigecycline (14.3%) and colistin (0%) in PNs; and colistin (12.0%) and linezolid (0%) in UTIs. COVID-19 has changed the landscape of HAIs in our ICUs. Approval of new drugs and rigorous surveillance is urgently needed. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.