1,721,052 research outputs found
Editorial: Antimicrobial Stewardship in Low- and Middle-Income Countries
No full textGyssens, IC (corresponding author), Dept Internal Med, Nijmegen, Netherlands.
Radboud Ctr Infect Dis, Nijmegen, Netherlands.
Hasselt Univ, Fac Med & Hlth Sci, Hasselt, Belgium.
[email protected]
Antibiotic policy
No full textThere is a clear association between antibiotic use and resistance both on individual and population levels. In the European Union, countries with large antibiotic consumption have higher resistance rates. Antibiotic resistance leads to failed treatments, prolonged hospitalisations, increased costs and deaths. With few new antibiotics in the Research & Development pipeline, prudent antibiotic use is the only option to delay the development of resistance. Antibiotic policy consists of prescribing strategies to optimise the indication, selection, dosing, route of administration, duration and timing of antibiotic therapy to maximise clinical cure or prevention of infection whilst limiting the unintended consequences of antibiotic use, including toxicity and selection of resistant microorganisms. A secondary goal is to reduce healthcare costs without adversely affecting the quality of care. The purpose of this paper is to provide the evidence base of prudent antibiotic policy. Special emphasis is placed on urinary tract infections. The value and support of antibiotic committees, guidelines, ID consultants and/or antimicrobial stewardship teams to prolong the efficacy of available antibiotics will be discussed. (C) 2011 Elsevier B. V. and the International Society of Chemotherapy. All rights reserved
Persisterende artalgiëen na een verblijf in Bolivië: de grote chikungunyavirusepidemie op het westelijk halfrond.
No full textPersistent arthralgia after a stay in Bolivia: the large chikungunya virus epidemic on the Western hemisphere
In this case report, a 20-year-old Belgian patient suffered persisting arthralgias. She was diagnosed with chikungunya acquired in Bolivia, which was confirmed serologically. The clinical features, pathogenesis, diagnosis, and treatment options are discussed. In addition, an overview of the current epidemic on the Western hemisphere is given
Risk Factors for Mortality, Intensive Care Unit Admission, and Bacteremia in Patients Suspected of Sepsis at the Emergency Department: A Prospective Cohort Study
No full textContains fulltext :
230544.pdf (Publisher’s version ) (Open Access
Differentiating influenza from COVID-19 in patients presenting with suspected sepsis
No full textThere is a need for a quick assessment of severely ill patients presenting to the hospital. The objectives of this study were to identify clinical, laboratory and imaging parameters that could differentiate between influenza and COVID-19 and to assess the frequency and impact of early bacterial co-infection. A prospective observational cohort study was performed between February 2019 and April 2020. A retrospective cohort was studied early in the COVID-19 pandemic. Patients suspected of sepsis with PCR-confirmed influenza or SARS-CoV-2 were included. A multivariable logistic regression model was built to differentiate COVID-19 from influenza. In total, 103 patients tested positive for influenza and 110 patients for SARS-CoV-2, respectively. Hypertension (OR 6.550), both unilateral (OR 4.764) and bilateral (OR 7.916), chest X-ray abnormalities, lower temperature (OR 0.535), lower absolute leukocyte count (OR 0.857), lower AST levels (OR 0.946), higher LDH (OR 1.008), higher ALT (OR 1.044) and higher ferritin (OR 1.001) were predictive of COVID-19. Early bacterial co-infection was more frequent in patients with influenza (10.7% vs. 2.7%). Empiric antibiotic usage was high (76.7% vs. 84.5%). Several factors determined at presentation to the hospital can differentiate between influenza and COVID-19. In the future, this could help in triage, diagnosis and early management. Clinicaltrial.gov Identifier: NCT03841162
Analysis of Targeted Proteomics data across different datasets with reference characteristics
No full textBackground: An early diagnosis or the detection of signals, markers, and patterns that indicate harmful events, before the disease can develop is crucial in many diseases. For infectious diseases the characterization of the inflammatory response can assist in making a rapid clinical assessment of early infection diagnosis For chronic diseases the characterization of disease-associated parameters can assist in representing a disease as an individual specific health continuum. The objective of this study is to develop an approach for the analyses of high-dimensional multi-batch proteomics datasets and to develop a pipeline to analyze multi-dimensional data keeping the control dataset as the reference for parameter range estimations. This to discover the relationships across and within these datatypes, and to identify relevant patterns.
Methods: In a pilot project (I AM Frontier), Cross-omics data (+1000 proteins, +250 metabolites) were provided by VITO health. In an attempt to test the central hypotheses of precision medicine In I AM Frontier (IAF), VITO collected blood, urine, stool, activity measurements and anthropometric information from a small cohort of 30 healthy but “at risk” (45-60 years old) individuals, on a longitudinal basis for 13 months. External datasets were included to complement the reference data with robust disease endpoints and to validate our findings with a sepsis dataset consisting of patients with infections of different aetiology. For the targeted proteomics samples were analyzed using 92-plex proteomics panels (including an inflammation panel) based on a proximity extension assay (PEA) with oligonucleotide-labelled antibody probe pairs (OLINK, Uppsala Sweden). Unsupervised differential expression analysis using hierarchical clustering t, k-means clustering and PCA were performed in R.
Supervised differential expression analysis using Welch’s t test and elastic net regression analyses where performed to confirm unsupervised analyses. A complete normalization and bridging workflow for multi-batch proteomics experiments across cohorts was applied.
Results: We establish a workflow to use IAF data as a reference dataset to analyze targeted proteomics datasets. Inverse normalized rank based transformation of the data followed by cosine similarity calculations of the OLINK pooled plasma samples showed to be a robust method for comparing this type of cross-omics multi batch data across cohorts.
A 92-plex proteomics dataset with 406 sepsis patients unsupervised, hierarchical clustering revealed that inflammatory response is more strongly related to disease severity than to aetiology or site of infection. A subgroup of influenza showed to result in clearly distinct inflammatory protein profiles compared to other infections causing sepsis.
Conclusions: We built a cross-study integration workflow for targeted proteomics (OLINK) utilizing a uniquely available timeseries dataset from IAF as the reference for determining individual variations per parameter. The proposed workflow is validated in an independent sepsis dataset. Several differentially expressed inflammatory proteins were identified that could be used as biomarkers for sepsis. A promising methodology and data availability is in place to analyze disease profiles of additional (chronic) diseases across cohorts in a search for biomolecular markers. The ranges of the proteins established by our workflow can be of value for outcome prediction, patient monitoring, and directing further diagnostics
Analysis of Targeted Proteomics data across different datasets with reference characteristics
No full textBackground: An early diagnosis or the detection of signals, markers, and patterns that indicate harmful events, before the disease can develop is crucial in many diseases. For infectious diseases the characterization of the inflammatory response can assist in making a rapid clinical assessment of early infection diagnosis For chronic diseases the characterization of disease-associated parameters can assist in representing a disease as an individual specific health continuum. The objective of this study is to develop an approach for the analyses of high-dimensional multi-batch proteomics datasets and to develop a pipeline to analyze multi-dimensional data keeping the control dataset as the reference for parameter range estimations. This to discover the relationships across and within these datatypes, and to identify relevant patterns.
Methods: In a pilot project (I AM Frontier), Cross-omics data (+1000 proteins, +250 metabolites) were provided by VITO health. In an attempt to test the central hypotheses of precision medicine In I AM Frontier (IAF), VITO collected blood, urine, stool, activity measurements and anthropometric information from a small cohort of 30 healthy but “at risk” (45-60 years old) individuals, on a longitudinal basis for 13 months. External datasets were included to complement the reference data with robust disease endpoints and to validate our findings with a sepsis dataset consisting of patients with infections of different aetiology. For the targeted proteomics samples were analyzed using 92-plex proteomics panels (including an inflammation panel) based on a proximity extension assay (PEA) with oligonucleotide-labelled antibody probe pairs (OLINK, Uppsala Sweden). Unsupervised differential expression analysis using hierarchical clustering t, k-means clustering and PCA were performed in R.
Supervised differential expression analysis using Welch’s t test and elastic net regression analyses where performed to confirm unsupervised analyses. A complete normalization and bridging workflow for multi-batch proteomics experiments across cohorts was applied.
Results: We establish a workflow to use IAF data as a reference dataset to analyze targeted proteomics datasets. Inverse normalized rank based transformation of the data followed by cosine similarity calculations of the OLINK pooled plasma samples showed to be a robust method for comparing this type of cross-omics multi batch data across cohorts.
A 92-plex proteomics dataset with 406 sepsis patients unsupervised, hierarchical clustering revealed that inflammatory response is more strongly related to disease severity than to aetiology or site of infection. A subgroup of influenza showed to result in clearly distinct inflammatory protein profiles compared to other infections causing sepsis.
Conclusions: We built a cross-study integration workflow for targeted proteomics (OLINK) utilizing a uniquely available timeseries dataset from IAF as the reference for determining individual variations per parameter. The proposed workflow is validated in an independent sepsis dataset. Several differentially expressed inflammatory proteins were identified that could be used as biomarkers for sepsis. A promising methodology and data availability is in place to analyze disease profiles of additional (chronic) diseases across cohorts in a search for biomolecular markers. The ranges of the proteins established by our workflow can be of value for outcome prediction, patient monitoring, and directing further diagnostics
A case study on Staphylococcus aureus bacteraemia: available treatment options, antibiotic R&D and responsible antibiotic-use strategies
No full textThis case study addresses: (i) antibiotic treatment options for Staphylococcus aureus bacteraemia (SAB), for both empirical and targeted therapy; (ii) the current status of and priorities for the antibiotic pipeline to ensure access of effective antibiotics for SAB; and (iii) strategies for responsible antibiotic use relevant to the clinical management of SAB
The Clinical Impact of Rapid Molecular Microbiological Diagnostics for Pathogen and Resistance Gene Identification in Patients With Sepsis: A Systematic Review
No full textContains fulltext :
229512.pdf (Publisher’s version ) (Open Access)Fast microbiological diagnostics (MDx) are needed to ensure early targeted antimicrobial treatment in sepsis. This systematic review focuses on the impact on antimicrobial management and patient outcomes of MDx for pathogen and resistance gene identification compared with blood cultures. PubMed was searched for clinical studies using either whole blood directly or after short-term incubation. Twenty-five articles were retrieved describing the outcomes of 8 different MDx. Three interventional studies showed a significant increase in appropriateness of antimicrobial therapy and a nonsignificant change in time to appropriate therapy. Impact on mortality was conflicting. Length of stay was significantly lower in 2 studies. A significant decrease in antimicrobial cost was demonstrated in 6 studies. The limitations of this systematic review include the low number and observed heterogeneity of clinical studies. In conclusion, potential benefits of MDx regarding antimicrobial management and some patient outcomes were reported. More rigorous intervention studies are needed focusing on the direct benefits for patients
Screening to select patients carrying extended-spectrum β-lactamase-producing Enterobacteriaceae for isolation in Flemish intensive care units: a Swiss cheese strategy?
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