Browsing by Author "Veljkovic, Nevena (8737352200)"

Coronary artery disease (CAD) and myocardial infarction (MI) are recognized as leading causes of mortality in developed countries. Although typically associated with behavioral risk factors, such as smoking, sedentary lifestyle, and poor dietary habits, such vascular phenotypes have also long been recognized as being related to genetic background. We review the currently available data concerning genetic markers for CAD in English and non-English articles with English abstracts published between 2003 and 2018. As genetic testing is increasingly available, it may be possible to identify adequate genetic markers representing the risk profile and to use them in a clinical setting. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.

Introduction: The analysis of cell-free DNA (cfDNA) for genetic abnormalities is a promising new approach for the diagnosis and prognosis of pancreatic cancer patients. Insights into the molecular characteristics of pancreatic cancer may provide valuable information, leading to its earlier detection and the development of targeted therapies. Material and Methods: We conducted a systematic review and a meta-analysis of studies that reported cfDNA in pancreatic ductal adenocarcinoma (PDAC). The studies were considered eligible if they included patients with PDAC, if they had blood tests for cfDNA/ctDNA, and if they analyzed the prognostic value of cfDNA/ctDNA for patients’ survival. The studies published before 22 October 2020 were identified through the PubMED, EM-BASE, Web of Science and Cochrane Library databases. The assessed outcomes were the overall (OS) and progression-free survival (PFS), expressed as the log hazard ratio (HR) and standard error (SE). The summary of the HR effect size was estimated by pooling the individual trial results using the Review Manager, version 5.3, Cochrane Collaboration. The heterogeneity was assessed using the Cochran Q test and I2 statistic. Results: In total, 48 studies were included in the qualitative review, while 44 were assessed in the quantitative synthesis, with the total number of patients included being 3524. Overall negative impacts of cfDNA and KRAS mutations on OS and PFS in PDAC (HR = 2.42, 95% CI 1.95–2.99 and HR = 2.46, 95% CI: 2.01–3.00, respectively) were found. The subgroup analysis of the locally advanced and metastatic disease presented similar results (HR = 2.51, 95% CI: 1.90–3.31). In the studies assessing the pre-treatment presence of KRAS, there was a moderate to high degree of heterogeneity (I2 = 87% and I2 = 48%, for OS and PFS, respectively), which was remarkably decreased in the analysis of the studies measuring post-treatment KRAS (I2 = 24% and I2 = 0%, for OS and PFS, respectively). The patients who were KRAS positive before but KRAS negative after treatment had a better prognosis than the persistently KRAS-positive patients (HR = 5.30, 95% CI: 1.02–27.63). Conclusion: The assessment of KRAS mutation by liquid biopsy can be considered as an additional tool for the estimation of the disease course and outcome in PDAC patients. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Introduction: The analysis of cell-free DNA (cfDNA) for genetic abnormalities is a promising new approach for the diagnosis and prognosis of pancreatic cancer patients. Insights into the molecular characteristics of pancreatic cancer may provide valuable information, leading to its earlier detection and the development of targeted therapies. Material and Methods: We conducted a systematic review and a meta-analysis of studies that reported cfDNA in pancreatic ductal adenocarcinoma (PDAC). The studies were considered eligible if they included patients with PDAC, if they had blood tests for cfDNA/ctDNA, and if they analyzed the prognostic value of cfDNA/ctDNA for patients’ survival. The studies published before 22 October 2020 were identified through the PubMED, EM-BASE, Web of Science and Cochrane Library databases. The assessed outcomes were the overall (OS) and progression-free survival (PFS), expressed as the log hazard ratio (HR) and standard error (SE). The summary of the HR effect size was estimated by pooling the individual trial results using the Review Manager, version 5.3, Cochrane Collaboration. The heterogeneity was assessed using the Cochran Q test and I2 statistic. Results: In total, 48 studies were included in the qualitative review, while 44 were assessed in the quantitative synthesis, with the total number of patients included being 3524. Overall negative impacts of cfDNA and KRAS mutations on OS and PFS in PDAC (HR = 2.42, 95% CI 1.95–2.99 and HR = 2.46, 95% CI: 2.01–3.00, respectively) were found. The subgroup analysis of the locally advanced and metastatic disease presented similar results (HR = 2.51, 95% CI: 1.90–3.31). In the studies assessing the pre-treatment presence of KRAS, there was a moderate to high degree of heterogeneity (I2 = 87% and I2 = 48%, for OS and PFS, respectively), which was remarkably decreased in the analysis of the studies measuring post-treatment KRAS (I2 = 24% and I2 = 0%, for OS and PFS, respectively). The patients who were KRAS positive before but KRAS negative after treatment had a better prognosis than the persistently KRAS-positive patients (HR = 5.30, 95% CI: 1.02–27.63). Conclusion: The assessment of KRAS mutation by liquid biopsy can be considered as an additional tool for the estimation of the disease course and outcome in PDAC patients. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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).