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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 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 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: 6
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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: 6
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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: 11
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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: 75
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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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Journal articlePeiffer-Smadja N, Poda A, Ouedraogo A-S, et al., 2020,
Paving the Way for the Implementation of a Decision Support System for Antibiotic Prescribing in Primary Care in West Africa: Preimplementation and Co-Design Workshop With Physicians.
, J Med Internet Res, Vol: 22BACKGROUND: Suboptimal use of antibiotics is a driver of antimicrobial resistance (AMR). Clinical decision support systems (CDSS) can assist prescribers with rapid access to up-to-date information. In low- and middle-income countries (LMIC), the introduction of CDSS for antibiotic prescribing could have a measurable impact. However, interventions to implement them are challenging because of cultural and structural constraints, and their adoption and sustainability in routine clinical care are often limited. Preimplementation research is needed to ensure relevant adaptation and fit within the context of primary care in West Africa. OBJECTIVE: This study examined the requirements for a CDSS adapted to the context of primary care in West Africa, to analyze the barriers and facilitators of its implementation and adaptation, and to ensure co-designed solutions for its adaptation and sustainable use. METHODS: We organized a workshop in Burkina Faso in June 2019 with 47 health care professionals representing 9 West African countries and 6 medical specialties. The workshop began with a presentation of Antibioclic, a publicly funded CDSS for antibiotic prescribing in primary care that provides personalized antibiotic recommendations for 37 infectious diseases. Antibioclic is freely available on the web and as a smartphone app (iOS, Android). The presentation was followed by a roundtable discussion and completion of a questionnaire with open-ended questions by participants. Qualitative data were analyzed using thematic analysis. RESULTS: Most of the participants had access to a smartphone during their clinical consultations (35/47, 74%), but only 49% (23/47) had access to a computer and none used CDSS for antibiotic prescribing. The participants considered that CDSS could have a number of benefits including updating the knowledge of practitioners on antibiotic prescribing, improving clinical care and reducing AMR, encouraging the establishment of national guidelines, and deve
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Journal articleZhou J, Otter JA, Price JR, et al., 2020,
Investigating SARS-CoV-2 surface and air contamination in an acute healthcare setting during the peak of the COVID-19 pandemic in London
, Clinical Infectious Diseases, Vol: 2020, Pages: 1-1, ISSN: 1058-4838BACKGROUND: Evaluation of SARS-CoV-2 surface and air contamination during the COVID-19 pandemic in London. METHODS: We performed this prospective cross-sectional observational study in a multi-site London hospital. Air and surface samples were collected from seven clinical areas, occupied by patients with COVID-19, and a public area of the hospital. Three or four 1.0 m3 air samples were collected in each area using an active air sampler. Surface samples were collected by swabbing items in the immediate vicinity of each air sample. SARS-CoV-2 was detected by RT-qPCR and viral culture; the limit of detection for culturing SARS-CoV-2 from surfaces was determined. RESULTS: Viral RNA was detected on 114/218 (52.3%) of surfaces and 14/31 (38.7%) air samples but no virus was cultured. The proportion of surface samples contaminated with viral RNA varied by item sampled and by clinical area. Viral RNA was detected on surfaces and in air in public areas of the hospital but was more likely to be found in areas immediately occupied by COVID-19 patients than in other areas (67/105 (63.8%) vs. 29/64 (45.3%) (odds ratio 0.5, 95% confidence interval 0.2-0.9, p=0.025, Chi squared test)). The high PCR Ct value for all samples (>30) indicated that the virus would not be culturable. CONCLUSIONS: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines the potential risk from environmental contamination in managing COVID-19, and the need for effective use of PPE, physical distancing, and hand/surface hygiene.
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