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Journal articleHughes S, Troise O, Donaldson H, et al., 2020,
Bacterial and fungal coinfection among hospitalised patients with COVID-19: A retrospective cohort study in a UK secondary care setting
, Clinical Microbiology and Infection, Vol: 26, Pages: 1395-1399, ISSN: 1198-743XObjectivesWe investigate the incidence of bacterial and fungal co-infection of hospitalised patients with confirmed SARS-CoV-2 in this retrospective observational study across two London hospitals during the first UK wave of COVID-19.MethodsA retrospective case-series of hospitalised patients with confirmed SARS-CoV-2 by PCR was analysed across two acute NHS hospitals (February 20–April 20; each isolate reviewed independently in parallel). This was contrasted to a control group of influenza positive patients admitted during 2019/20 flu season. Patient demographics, microbiology, and clinical outcomes were analysed.Results836 patients with confirmed SARS-CoV-2 were included; 27/836(3.2%) had early confirmed bacterial isolates identified (0-5 days post-admission) rising to 51/836(6.1%) throughout admission. Blood cultures, respiratory samples, pneumococcal or legionella urinary antigens, and respiratory viral PCR panels were obtained from 643(77%), 112(13%), 249(30%), 246(29%) and 250(30%) COVID-19 patients, respectively. A positive blood culture was identified in 60(7.1%) patients, of which 39/60 were classified as contaminants. Bacteraemia secondary to respiratory infection was confirmed in two cases (1 community-acquired K. pneumoniae and 1 ventilator-associated E. cloacae). Line-related bacteraemia was identified in six patients (3 candida, 2 Enterococcus spp. and 1 Pseudomonas aeruginosa). All other community acquired bacteraemias(16) were attributed to non-respiratory infection. Zero concomitant pneumococcal, legionella or influenza infection was detected. A low yield of positive respiratory cultures was identified; S. aureus the most common respiratory pathogen isolated in community-acquired coinfection (4/24;16.7%) with pseudomonas and yeast identified in late-onset infection. Invasive fungal infections (n=3) were attributed to line related infections. Comparable rates of positive co-infection were identified in the control group of confirmed influenza i
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Journal articleMoniri A, Miglietta L, Holmes A, et al., 2020,
High-level multiplexing in digital PCR with intercalating dyes by coupling real-time kinetics and melting curve analysis.
, Analytical Chemistry, Vol: 92, Pages: 14181-14188, ISSN: 0003-2700Digital polymerase chain reaction (dPCR) is a mature technique that has enabled scientific breakthroughs in several fields. However, this technology is primarily used in research environments with high-level multiplexing representing a major challenge. Here, we propose a novel method for multiplexing, referred to as amplification and melting curve analysis (AMCA), which leverages the kinetic information in real-time amplification data and the thermodynamic melting profile using an affordable intercalating dye (EvaGreen). The method trains a system comprised of supervised machine learning models for accurate classification, by virtue of the large volume of data from dPCR platforms. As a case study, we develop a new 9-plex assay to detect mobilised colistin resistant (mcr) genes as clinically relevant targets for antimicrobial resistance. Over 100,000 amplification events have been analysed, and for the positive reactions, the AMCA approach reports a classification accuracy of 99.33 ± 0.13%, an increase of 10.0% over using melting curve analysis. This work provides an affordable method of high-level multiplexing without fluorescent probes, extending the benefits of dPCR in research and clinical settings.
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Journal articleMorrell L, Buchanan J, Roope LSJ, et al., 2020,
Delayed antibiotic prescription by general practitioners in the UK: a stated-choice study
, ANTIBIOTICS-BASEL, Vol: 9, Pages: 1-19, ISSN: 2079-6382Delayed antibiotic prescription in primary care has been shown to reduce antibiotic consumption, without increasing risk of complications, yet is not widely used in the UK. We sought to quantify the relative importance of factors affecting the decision to give a delayed prescription, using a stated-choice survey among UK general practitioners. Respondents were asked whether they would provide a delayed or immediate prescription in fifteen hypothetical consultations, described by eight attributes. They were also asked if they would prefer not to prescribe antibiotics. The most important determinants of choice between immediate and delayed prescription were symptoms, duration of illness, and the presence of multiple comorbidities. Respondents were more likely to choose a delayed prescription if the patient preferred not to have antibiotics, but consultation length had little effect. When given the option, respondents chose not to prescribe antibiotics in 51% of cases, with delayed prescription chosen in 21%. Clinical features remained important. Patient preference did not affect the decision to give no antibiotics. We suggest that broader dissemination of the clinical evidence supporting use of delayed prescription for specific presentations may help increase appropriate use. Establishing patient preferences regarding antibiotics may help to overcome concerns about patient acceptance. Increasing consultation length appears unlikely to affect the use of delayed prescription.
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Journal articlePeiffer-Smadja N, Allison R, Jones LF, et al., 2020,
Preventing and Managing Urinary Tract Infections: Enhancing the Role of Community Pharmacists-A Mixed Methods Study
, ANTIBIOTICS-BASEL, Vol: 9, ISSN: 2079-6382- Author Web Link
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- Citations: 5
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Journal articleBorek AJ, Anthierens S, Allison R, et al., 2020,
How did a Quality Premium financial incentive influence antibiotic prescribing in primary care? Views of Clinical Commissioning Group and general practice professionals
, JOURNAL OF ANTIMICROBIAL CHEMOTHERAPY, Vol: 75, Pages: 2681-2688, ISSN: 0305-7453- Author Web Link
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- Citations: 5
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Journal articlePallett SJC, Rayment M, Patel A, et al., 2020,
Point-of-care serological assays for delayed SARS-CoV-2 case identification among health-care workers in the UK: a prospective multicentre cohort study
, LANCET RESPIRATORY MEDICINE, Vol: 8, Pages: 885-894, ISSN: 2213-2600- Author Web Link
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- Citations: 68
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Journal articleBoyd 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: 2213-7165BACKGROUND: 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.
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Journal articlePeiffer-Smadja N, Lescure F-X, Sallard E, et al., 2020,
Anticovid, a comprehensive open-access real-time platform of registered clinical studies for COVID-19
, Journal of Antimicrobial Chemotherapy, Vol: 75, Pages: 2708-2710, ISSN: 0305-7453 -
Journal articleOtter JA, Mookerjee S, Davies F, et al., 2020,
Detecting carbapenemase-producing Enterobacterales (CPE): an evaluation of an enhanced CPE infection control and screening programme in acute care
, JOURNAL OF ANTIMICROBIAL CHEMOTHERAPY, Vol: 75, Pages: 2670-2676, ISSN: 0305-7453- Author Web Link
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- Citations: 3
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Journal articleAbdulaal A, Patel A, Charani E, et al., 2020,
Prognostic modelling of COVID-19 using artificial intelligence in a UK population
, Journal of Medical Internet Research, Vol: 22, Pages: 1-10, ISSN: 1438-8871Background:The current severe acute respiratory syndrome-coronavirus disease (SARS-CoV-2) outbreak is a public health emergency which has had a significant case-fatality in the United Kingdom (UK). Whilst there appear to be several early predictors of outcome, there are no currently validated prognostic models or scoring systems applicable specifically to SARS-CoV-2 positive patients.Objective:To create a point-of-admission, mortality-risk scoring system utilising an artificial neural network (ANN).Methods:We present an ANN which can provide a patient-specific, point-of-admission mortality risk prediction to inform clinical management decisions at the earliest opportunity. The ANN analyses a set of patient features including demographics, comorbidities, smoking history and presenting symptoms and predicts patient-specific mortality risk during the current hospital admission. The model was trained and validated on data extracted from 398 patients admitted to hospital with a positive real-time reverse transcriptase polymerase chain reaction (rt-PCR) test for SARS-CoV-2.Results:Patient-specific mortality was predicted with 86.25% accuracy, with a sensitivity of 87.50% (95% CI: 61.65% to 98.45%) and specificity of 85.94% (95% CI: 74.98% to 93.36%). The positive predictive value was 60.87% (95% CI: 45.23% to 74.56%), and the negative predictive value was 96.49% (95% CI: 88.23% to 99.02%). The (AUROC) was 90.12%.Conclusions:This analysis demonstrates an adaptive ANN trained on data at a single site, which demonstrates the early utility of deep learning approaches in a rapidly evolving pandemic with no established or validated prognostic scoring systems.
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