10 results found
Boyd SE, Livermore DM, Hooper DC, et al., 2020, Metallo-β-Lactamases: Structure, Function, Epidemiology, Treatment Options, and the Development Pipeline, Antimicrobial Agents and Chemotherapy, Vol: 64, ISSN: 0066-4804
<jats:p> Modern medicine is threatened by the global rise of antibiotic resistance, especially among Gram-negative bacteria. Metallo-β-lactamase (MBL) enzymes are a particular concern and are increasingly disseminated worldwide, though particularly in Asia. Many MBL producers have multiple further drug resistances, leaving few obvious treatment options. Nonetheless, and more encouragingly, MBLs may be less effective agents of carbapenem resistance <jats:italic>in vivo</jats:italic> , under zinc limitation, than <jats:italic>in vitro</jats:italic> . </jats:p>
Boyd SE, Vasudevan A, Moore LSP, et al., 2020, Validating a prediction tool to determine the risk of nosocomial multidrug-resistant Gram-negative bacilli infection in critically ill patients: A retrospective case-control study, Journal of Global Antimicrobial Resistance, Vol: 22, Pages: 826-831, ISSN: 2213-7165
BACKGROUND: The Singapore GSDCS score was developed to enable clinicians predict the risk of nosocomial multidrug-resistant Gram-negative bacilli (RGNB) infection in critically ill patients. We aimed to validate this score in a UK setting. METHOD: A retrospective case-control study was conducted including patients who stayed for more than 24h in intensive care units (ICUs) across two tertiary National Health Service hospitals in London, UK (April 2011-April 2016). Cases with RGNB and controls with sensitive Gram-negative bacilli (SGNB) infection were identified. RESULTS: The derived GSDCS score was calculated from when there was a step change in antimicrobial therapy in response to clinical suspicion of infection as follows: prior Gram-negative organism, Surgery, Dialysis with end-stage renal disease, prior Carbapenem use and intensive care Stay of more than 5 days. A total of 110 patients with RGNB infection (cases) were matched 1:1 to 110 geotemporally chosen patients with SGNB infection (controls). The discriminatory ability of the prediction tool by receiver operating characteristic curve analysis in our validation cohort was 0.75 (95% confidence interval 0.65-0.81), which is comparable with the area under the curve of the derivation cohort (0.77). The GSDCS score differentiated between low- (0-1.3), medium- (1.4-2.3) and high-risk (2.4-4.3) patients for RGNB infection (P<0.001) in a UK setting. CONCLUSION: A simple bedside clinical prediction tool may be used to identify and differentiate patients at low, medium and high risk of RGNB infection prior to initiation of prompt empirical antimicrobial therapy in the intensive care setting.
Chatterjee A, Modarai M, Naylor N, et al., 2018, Quantifying drivers of antibiotic resistance in humans: a systematic review, The Lancet Infectious Diseases, Vol: 18, Pages: e368-e378, ISSN: 1473-3099
Mitigating the risks of antibiotic resistance requires a horizon scan linking the quality with the quantity of data reported on drivers of antibiotic resistance in humans, arising from the human, animal, and environmental reservoirs. We did a systematic review using a One Health approach to survey the key drivers of antibiotic resistance in humans. Two sets of reviewers selected 565 studies from a total of 2819 titles and abstracts identified in Embase, MEDLINE, and Scopus (2005–18), and the European Centre for Disease Prevention and Control, the US Centers for Disease Control and Prevention, and WHO (One Health data). Study quality was assessed in accordance with Cochrane recommendations. Previous antibiotic exposure, underlying disease, and invasive procedures were the risk factors with most supporting evidence identified from the 88 risk factors retrieved. The odds ratios of antibiotic resistance were primarily reported to be between 2 and 4 for these risk factors when compared with their respective controls or baseline risk groups. Food-related transmission from the animal reservoir and water-related transmission from the environmental reservoir were frequently quantified. Uniformly quantifying relationships between risk factors will help researchers to better understand the process by which antibiotic resistance arises in human infections.
Rodvold KA, Hope WW, Boyd SE, 2017, Considerations for effect site pharmacokinetics to estimate drug exposure: concentrations of antibiotics in the lung, Current Opinion in Pharmacology, Vol: 36, Pages: 114-123, ISSN: 1471-4892
Boyd S, moore LSP, Rawson TM, et al., 2017, Combination therapy for carbapenemase-producing Entero-bacteriaceae: INCREMENT-al effect on resistance remains unclear, The Lancet Infectious Diseases, Vol: 17, Pages: 899-900, ISSN: 1473-3099
Holmes AH, Boyd SE, Moore LSP, et al., 2017, Obtaining antibiotics online from within the UK: a cross-sectional study, Journal of Antimicrobial Chemotherapy, ISSN: 1460-2091
Boyd S, Charani E, Lyons T, et al., 2016, Information provision for antibacterial dosing in the obese patient: a sizeable absence?, Journal of Antimicrobial Chemotherapy, ISSN: 1460-2091
Boyd S, Rawson T, Moore L, et al., 2016, Preventing bloodstream infection in children: What's the CATCH?, The Lancet, Vol: 388, Pages: 462-463, ISSN: 0140-6736
Law S, Boyd S, MacDonald J, et al., 2014, Predictors of survival in patients with chronic obstructive pulmonary disease receiving long-term oxygen therapy, BMJ Supportive & Palliative Care, Vol: 4, Pages: 140-145, ISSN: 2045-435X
Boyd S, Aggarwal I, Davey P, et al., 2011, Peripheral intravenous catheters: the road to quality improvement and safer patient care, Journal of Hospital Infection, Vol: 77, Pages: 37-41, ISSN: 0195-6701
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