Patient engagement with antibiotic messaging in secondary care: a qualitative feasibility study of the ‘review & revise’ experience

Background: We aimed to investigate and optimise the acceptability and usefulness of a patient leaflet about antibiotic prescribing decisions made during hospitalisation, and to explore individual patient experiences and preferences regarding the process of antibiotic prescription ‘review & revise’ which is a key strategy to minimise antibiotic overuse in hospitals. Methods: In this qualitative study, run within the feasibility study of a large, cluster-randomised stepped wedge trial of 36 hospital organisations, a series of semi-structured, think-aloud telephone interviews were conducted and data were analysed using thematic analysis. Fifteen adult patients who had experienced a recent acute medical hospital admission during which they had been prescribed antimicrobials and offered a patient leaflet about antibiotic prescribing were recruited to the study. Results: Participants reacted positively to the leaflet, reporting that it was both an accessible and important source of information which struck the appropriate balance between informing and reassuring. Participants all valued open communication with clinicians, and were keen to be involved in antibiotic prescribing decisions, with individuals reporting positive experiences regarding antibiotic prescription changes or stopping. Many participants had prior experience or knowledge of antibiotics and resistance, and generally welcomed efforts to reduce antibiotic usage. Overall, there was a feeling that healthcare professionals (HCPs) are trusted experts providing the most appropriate treatment for individual patient conditions. Conclusions: This study offers novel insights into how patients within secondary care are likely to respond to messages advocating a reduction in the use of antibiotics through the ‘review & revise’ approach. Due to the level of trust that patients place in their care provider, encouraging HCPs within secondary care to engage patients with greater communication and information provision could provide great advantages in the drive to reduce antibiotic use. It may also be beneficial for HCPs to view patient experiences as cumulative events that have the potential to impact future behaviour around antibiotic use. Finally, pre-testing messages about antibiotic prescribing and resistance is vital to dispelling any misconceptions either around effectiveness of treatment for patients, or perceptions of how messages may be received

Macro-, meso- and microstructural characterization of metallic lattice structures manufactured by additive manufacturing assisted investment casting

Cellular materials are recognized for their high specific mechanical properties, making them desirable in ultra-lightweight applications. Periodic lattices have tunable properties and may be manufactured by metallic additive manufacturing (AM) techniques. However, AM can lead to issues with un-melted powder, macro/micro porosity, dimensional control and heterogeneous microstructures. This study overcomes these problems through a novel technique, combining additive manufacturing and investment casting to produce detailed investment cast lattice structures. Fused filament fabrication is used to fabricate a pattern used as the mold for the investment casting of aluminium A356 alloy into high-conformity thin-ribbed (~ 0.6 mm thickness) scaffolds. X-ray micro-computed tomography (CT) is used to characterize macro- and meso-scale defects. Optical and scanning electron (SEM) microscopies are used to characterize the microstructure of the cast structures. Slight dimensional (macroscale) variations originate from the 3D printing of the pattern. At the mesoscale, the casting process introduces very fine (~ 3 µm) porosity, along with small numbers of (~ 25 µm) gas entrapment defects in the horizontal struts. At a microstructural level, both the (~ 70 μm) globular/dendritic grains and secondary phases show no significant variations across the lattices. This method is a promising alternative means for producing highly detailed non-stochastic metallic cellular lattices and offers scope for further improvement through refinement of filament fabrication.This work was supported by Portuguese FCT, under the reference project UIDB/04436/2020. We are grateful to the funding from the European Research Council through the ERC grant CORREL-CT, number 695638 to enable VHC to visit the Henry Royce Institute to undertake the X-ray CT studies. Tis work was supported by the Henry Royce Institute for Advanced Materials, funded through EPSRC grants EP/R00661X/1, EP/S019367/1, EP/P025021/1 and EP/P025498/1 and the Henry Moseley X-ray Imaging Facility funded by EP/T02593X/1

Machine learning and synthetic outcome estimation for individualised antimicrobial cessation

The decision on when it is appropriate to stop antimicrobial treatment in an individual patient is complex and under-researched. Ceasing too early can drive treatment failure, while excessive treatment risks adverse events. Under- and over-treatment can promote the development of antimicrobial resistance (AMR). We extracted routinely collected electronic health record data from the MIMIC-IV database for 18,988 patients (22,845 unique stays) who received intravenous antibiotic treatment during an intensive care unit (ICU) admission. A model was developed that utilises a recurrent neural network autoencoder and a synthetic control-based approach to estimate patients’ ICU length of stay (LOS) and mortality outcomes for any given day, under the alternative scenarios of if they were to stop vs. continue antibiotic treatment. Control days where our model should reproduce labels demonstrated minimal difference for both stopping and continuing scenarios indicating estimations are reliable (LOS results of 0.24 and 0.42 days mean delta, 1.93 and 3.76 root mean squared error, respectively). Meanwhile, impact days where we assess the potential effect of the unobserved scenario showed that stopping antibiotic therapy earlier had a statistically significant shorter LOS (mean reduction 2.71 days, p-value <0.01). No impact on mortality was observed. In summary, we have developed a model to reliably estimate patient outcomes under the contrasting scenarios of stopping or continuing antibiotic treatment. Retrospective results are in line with previous clinical studies that demonstrate shorter antibiotic treatment durations are often non-inferior. With additional development into a clinical decision support system, this could be used to support individualised antimicrobial cessation decision-making, reduce the excessive use of antibiotics, and address the problem of AMR

The changing landscape of membrane protein structural biology through developments in electron microscopy

Membrane proteins are ubiquitous in biology and are key targets for therapeutic development. Despite this, our structural understanding has lagged behind that of their soluble counterparts. This review provides an overview of this important field, focusing in particular on the recent resurgence of electron microscopy (EM) and the increasing role it has to play in the structural studies of membrane proteins, and illustrating this through several case studies. In addition we examine some of the challenges remaining in structural determination, and what steps are underway to enhance our knowledge of these enigmatic proteins

Delivering precision antimicrobial therapy through closed-loop control systems

Sub-optimal exposure to antimicrobial therapy is associated with poor patient outcomes and the development of antimicrobial resistance. Mechanisms for optimizing the concentration of a drug within the individual patient are under development. However, several barriers remain in realizing true individualization of therapy. These include problems with plasma drug sampling, availability of appropriate assays, and current mechanisms for dose adjustment. Biosensor technology offers a means of providing real-time monitoring of antimicrobials in a minimally invasive fashion. We report the potential for using microneedle biosensor technology as part of closed-loop control systems for the optimization of antimicrobial therapy in individual patients

A SANS and APT study of precipitate evolution and strengthening in a maraging steel

In this work a combination of the characterisation techniques small angle neutron scattering (SANS) and atom probe tomography (APT) are used to study the precipitation in a maraging steel. Three similar maraging steel alloys were aged at different temperatures and ageing times, and then characterised using SANS, APT and microhardness. The alloys consist of two types of precipitates, namely Laves phase and β-NiAl, the precipitates have different composition and hence precipitate ageing, which makes it complicated to model. The SANS experimental set-up was relatively simple and allowed the precipitate size and fraction of a large number of samples to be measured in a single experiment. The APT results were used for constraining the SANS modelling, particularly the composition, shape and distribution of phases. The characterisation led to the following description of precipitation: NiAl phase reaches coarsening at early stages of ageing and shifts its strength mechanisms from shearing to Orowan looping, which cause the characteristic peak strength; the Laves phase is in growth throughout and its strength contribution increases with ageing time. These observations were shown to be consistent with precipitate evolution and strengthening models, and the work of others. Although, there are some issues with the combination of SANS and APT approach, which are discussed, the methodology provides a valuable tool to understand complex precipitation behaviours

System Dynamics modelling to formulate policy interventions to optimise antibiotic prescribing in hospitals

© 2020 The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Multiple strategies have been used in the National Health System (NHS) in England to reduce inappropriate antibiotic prescribing and consumption in order to tackle antimicrobial resistance. These strategies have included, among others, restricting dispensing, introduction of prescribing guidelines, use of clinical audit, and performance reviews as well as strategies aimed at changing the prescribing behaviour of clinicians. However, behavioural interventions have had limited effect in optimising doctors’ antibiotic prescribing practices. This study examines the determinants of decision-making for antibiotic prescribing in hospitals in the NHS. A system dynamics model was constructed to capture structural and behavioural influences to simulate doctors’ prescribing practices. Data from the literature, patient records, healthcare professional interviews and survey responses were used to parameterise the model. The scenario simulation shows maximum improvements in guideline compliance are achieved when compliance among senior staff is increased, combined with fast laboratory turnaround of blood cultures, and microbiologist review. Improving guideline compliance of junior staff alone has limited impact. This first use of system dynamics modelling to study antibiotic prescribing decision-making demonstrates the applicability of the methodology for design and evaluation of future policies and interventions.Peer reviewe

Management strategies to minimize the dredging impacts of coastal development on fish and fisheries

Accelerating coastal development and shipping activities dictate that dredging operations will intensify, increasing potential impacts to fishes. Coastal fishes have high economic, ecological, and conservation significance and there is a need for evidencebased, quantitative guidelines on how to mitigate the impacts of dredging activities. We assess the potential risk from dredging to coastal fish and fisheries on a global scale.We then develop quantitative guidelines for two management strategies: threshold reference values and seasonal restrictions. Globally, threatened species and nearshore fisheries occur within close proximity to ports. We find that maintaining suspended sediment concentrations below 44 mg/L (15–121 bootstrapped CI) and for less than 24 hours would protect 95% of fishes from dredging-induced mortality. Implementation of seasonal restrictions during peak periods of reproduction and recruitment could further protect species from dredging impacts. This study details the first evidence-based defensible approach to minimize impacts to coastal fishes from dredging activities

Qualitative theory testing as mixed-method research

While the concept of mixed-methods research is more usually associated with combining quantitative and qualitative approaches, this paper outlines a study that mixed methods by undertaking qualitative theory testing and derivation when examining the relationship between health promotion theory and hospital nursing practice. Thus, it is concerned with relating the metatheoretical aspects of the debate and not with the pragmatic aspects of the research and concomitant methods. A deductive–inductive–deductive design, based on the theory–research–theory strategy of Meleis (1985), tested, revised and developed for nursing established health promotion theory using theory-testing criteria. To complement the methodological mix, the study also used the theory (i.e. a health-promotion taxonomy) as a framework to contextualise the findings rather than generate theory in the way associated with interpretative inquiry. While inconsistent with the traditional view linking theory testing with quantitative, objective epistemology, the process enabled a theoretically robust health-promotion taxonomy to be synthesised and advanced for use in nursing in relation to a paradigm of social thought