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Summary: At the 2015 World Health Assembly, UN member states adopted a resolution that committed to the development of national action plans (NAPs) for antimicrobial resistance (AMR). The political determination to commit to NAPs and the availability of robust governance structures to assure sustainable translation of the identified NAP objectives from policy to practice remain major barriers to progress. Inter-country variability in economic and political resilience and resource constraints could be fundamental barriers to progressing AMR NAPs. Although there have been regional and global analyses of NAPs from a One Health and policy perspective, a global assessment of the NAP objectives targeting antimicrobial use in human populations is needed. In this Health Policy, we report a systematic evidence synthesis of existing NAPs that are aimed at tackling AMR in human populations. We find marked gaps and variability in maturity of NAP development and operationalisation across the domains of: (1) policy and strategic planning; (2) medicines management and prescribing systems; (3) technology for optimised antimicrobial prescribing; (4) context, culture, and behaviours; (5) operational delivery and monitoring; and (6) patient and public engagement and involvement. The gaps identified in these domains highlight opportunities to facilitate sustainable delivery and operationalisation of NAPs. The findings from this analysis can be used at country, regional, and global levels to identify AMR-related priorities that are relevant to infrastructure needs and contexts.

BackgroundIdentifying patients at risk of dengue shock syndrome (DSS) is vital for effective healthcare delivery. This can be challenging in endemic settings because of high caseloads and limited resources. Machine learning models trained using clinical data could support decision-making in this context.MethodsWe developed supervised machine learning prediction models using pooled data from adult and paediatric patients hospitalised with dengue. Individuals from 5 prospective clinical studies in Ho Chi Minh City, Vietnam conducted between 12th April 2001 and 30th January 2018 were included. The outcome was onset of dengue shock syndrome during hospitalisation. Data underwent random stratified splitting at 80:20 ratio with the former used only for model development. Ten-fold cross-validation was used for hyperparameter optimisation and confidence intervals derived from percentile bootstrapping. Optimised models were evaluated against the hold-out set.FindingsThe final dataset included 4,131 patients (477 adults and 3,654 children). DSS was experienced by 222 (5.4%) of individuals. Predictors were age, sex, weight, day of illness at hospitalisation, indices of haematocrit and platelets over first 48 hours of admission and before the onset of DSS. An artificial neural network model (ANN) model had best performance with an area under receiver operator curve (AUROC) of 0.83 (95% confidence interval [CI], 0.76-0.85) in predicting DSS. When evaluated against the independent hold-out set this calibrated model exhibited an AUROC of 0.82, specificity of 0.84, sensitivity of 0.66, positive predictive value of 0.18 and negative predictive value of 0.98.InterpretationThe study demonstrates additional insights can be obtained from basic healthcare data, when applied through a machine learning framework. The high negative predictive value could support interventions such as early discharge or ambulatory patient management in this population. Work is underway to incorporate these findings into an electronic clinical decision support system to guide individual patient management.

Summary: Background: Enhanced methods of drug monitoring are required to support the individualisation of antibiotic dosing. We report the first-in-human evaluation of real-time phenoxymethylpenicillin monitoring using a minimally invasive microneedle-based β-lactam biosensor in healthy volunteers. Methods: This first-in-human, proof-of-concept study was done at the National Institute of Health Research/Wellcome Trust Imperial Clinical Research Facility (Imperial College London, London, UK). The study was approved by London-Harrow Regional Ethics Committee. Volunteers were identified through emails sent to a healthy volunteer database from the Imperial College Clinical Research Facility. Volunteers, who had to be older than 18 years, were excluded if they had evidence of active infection, allergies to penicillin, were at high risk of skin infection, or presented with anaemia during screening. Participants wore a solid microneedle β-lactam biosensor for up to 6 h while being dosed at steady state with oral phenoxymethylpenicillin (five 500 mg doses every 6 h). On arrival at the study centre, two microneedle sensors were applied to the participant's forearm. Blood samples (via cannula, at −30, 0, 10, 20, 30, 45, 60, 90, 120, 150, 180, 210, 240 min) and extracellular fluid (ECF; via microdialysis, every 15 min) pharmacokinetic (PK) samples were taken during one dosing interval. Phenoxymethylpenicillin concentration data obtained from the microneedles were calibrated using locally estimated scatter plot smoothing and compared with free-blood and microdialysis (gold standard) data. Phenoxymethylpenicillin PK for each method was evaluated using non-compartmental analysis. Area under the concentration–time curve (AUC), maximum concentration, and time to maximum concentration were compared. Bias and limits of agreement were investigated with Bland–Altman plots. Microneedle biosensor limits of detection were estimated. The study was registered with ClinicalTrials.gov, number NCT03847610. Findings: Ten healthy volunteers participated in the study. Mean age was 42 years (SD 14). Seven (70%) were men. Microdialysis and microneedle results were similar for phenoxymethylpenicillin ECF maximum concentration (0·74 mg/L vs 0·64 mg/L; 95% CI −0·24 to 0·44; p=0·53), time to maximum concentration (1·18 h vs 1·10 h; −0·52 to 0·67; p=0·79), and AUC (1·54 mg × h/L vs 1·67 mg × h/L; −1·10 to 0·85; p=0·79). In total, 440 time points were compared with mean difference between measurements −0·16 mg/L (95% CI −1·30 to 0·82). Mean phenoxymethylpenicillin AUCs for free serum and microneedle PK were similar (1·77 mg × h/L [SD 0·59] vs 1·67 mg × h/L [1·00]; −0·77 to 0·97; p=0·81). Median coefficient of variation between sensors within individuals was 7% (IQR 4–17). Limit of detection for the microneedles was estimated at 0·17 mg/L. Interpretation: This study is proof-of-concept of real-time, microneedle sensing of penicillin in vivo. Future work will explore microneedle use in patient populations, their role in data generation to inform dosing recommendations, and their incorporation into closed-loop control systems for automated drug delivery. Funding: National Institute for Health Research Imperial Biomedical Research Centre, Mérieux Foundation.

IntroductionThe development and implementation of national strategic plans is a critical component towards successfully addressing antimicrobial resistance (AMR). This study aimed to review the scope and analytical depth of situation analyses conducted to address AMR in human health to inform the development and implementation of national strategic plans.MethodsA systematic search of the literature was conducted to identify all studies since 2000, that have employed a situation analysis to address AMR. The included studies are analysed against frameworks for strategic analysis, primarily the PESTELI (Political, Economic, Sociological, Technological, Ecological, Legislative, Industry) framework, to understand the depth, scope and utility of current published approaches.Results10 studies were included in the final review ranging from single country (6) to regional-level multicountry studies (4). 8 studies carried out documentary review, and 3 of these also included stakeholder interviews. 2 studies were based on expert opinion with no data collection. No study employed the PESTELI framework. Most studies (9) included analysis of the political domain and 1 study included 6 domains of the framework. Technological and industry analyses is a notable gap. Facilitators and inhibitors within the political and legislative domains were the most frequently reported. No facilitators were reported in the economic or industry domains but featured inhibiting factors including: lack of ring-fenced funding for surveillance, perverse financial incentives, cost-shifting to patients; joint-stock drug company ownership complicating regulations.ConclusionThe PESTELI framework provides further opportunities to combat AMR using a systematic, strategic management approach, rather than a retrospective view. Future analysis of existing quantitative data with interviews of key strategic and operational stakeholders is needed to provide critical insights about where implementation efforts should be focussed, and also how to build contingency at the strategic level for agile responses to macro-level environmental influences.

Abstract In the face of increasing antimicrobial tolerance and resistance there is a global obligation to optimise oral antimicrobial dosing strategies including narrow spectrum penicillins, such as penicillin-V. We conducted a randomised, crossover study in healthy volunteers to characterise the influence of probenecid on penicillin-V pharmacokinetics and estimate the pharmacodynamics against Streptococcus pneumoniae. Twenty participants took six doses of penicillin-V (250 mg, 500 mg or 750 mg four times daily) with and without probenecid. Total and free concentrations of penicillin-V and probenecid were measured at two timepoints. A pharmacokinetic model was developed, and the probability of target attainment (PTA) calculated. The mean difference (95% CI) between penicillin-V alone and in combination with probenecid for serum total and free penicillin-V concentrations was significantly different at both timepoints (total: 45 min 4.32 (3.20–5.32) mg/L p

OBJECTIVES:To investigate the teaching of antimicrobial stewardship (AS) in undergraduate healthcare educational degree programmes in the United Kingdom (UK). PARTICIPANTS AND METHODS:Cross-sectional survey of undergraduate programmes in human and veterinary medicine, dentistry, pharmacy and nursing in the UK. The main outcome measures included prevalence of AS teaching; stewardship principles taught; estimated hours apportioned; mode of content delivery and teaching strategies; evaluation methodologies; and frequency of multidisciplinary learning. RESULTS:80% (112/140) of programmes responded adequately. The majority of programmes teach AS principles (88/109, 80.7%). 'Adopting necessary infection prevention and control precautions' was the most frequently taught principle (83/88, 94.3%), followed by 'timely collection of microbiological samples for microscopy, culture and sensitivity' (73/88, 82.9%) and 'minimisation of unnecessary antimicrobial prescribing' (72/88, 81.8%). The 'use of intravenous administration only to patients who are severely ill, or unable to tolerate oral treatment' was reported in ~50% of courses. Only 32/88 (36.3%) programmes included all recommended principles. DISCUSSION:Antimicrobial stewardship principles are included in most undergraduate healthcare and veterinary degree programmes in the UK. However, future professionals responsible for using antimicrobials receive disparate education. Education may be boosted by standardisation and strengthening of less frequently discussed principles.

The use of peripheral vascular catheters (PVCs) is an extremely common and necessary clinical intervention, but inappropriate PVC care poses a major patient safety risk in terms of infection. Quality improvement initiatives have been proposed to reduce the likelihood of adverse events, but a lack of understanding about factors that influence behaviours of healthcare professionals limits the efficacy of such interventions. We undertook qualitative interviews with clinical staff from a large group of hospitals in order to understand influences on PVC care behaviors and subsequent patient safety.Ten doctors, ten clinical pharmacists, 18 nurses and one midwife at a National Health Service hospital group in London (United Kingdom) were interviewed between December 2010 and July 2011 using qualitative methods. Responses were analysed using a thematic framework.FOUR KEY THEMES EMERGED: 1) Fragmentation of management and care, demonstrated with a lack of general overview and insufficient knowledge about expected standards of care or responsibility of different professionals; 2) feelings of resentment and frustration as a result of tensions in the workplace, due to the ambiguity about professional responsibilities; 3) disregard for existing hospital policy due to perceptions of flaws in the evidence used to support it; and 4) low-risk perception for the impact of PVC use on patient safety.Fragmentation of practice resulted in ill-defined responsibilities and interdisciplinary resentment, which coupled with a generally low perception of risk of catheter use, appeared to result in lack of maintaining policy PVC standards which could reduced patient safety. Resolution of these issues through clearly defining handover practice, teaching interdisciplinary duties and increasing awareness of PVC risks could result in preventing thousands of BSIs and other PVC-related infections annually.

Summary: Background: Dengue epidemics impose considerable strain on healthcare resources. Real-time continuous and non-invasive monitoring of patients admitted to the hospital could lead to improved care and outcomes. We evaluated the performance of a commercially available wearable (SmartCare) utilising photoplethysmography (PPG) to stratify clinical risk for a cohort of hospitalised patients with dengue in Vietnam. Methods: We performed a prospective observational study for adult and paediatric patients with a clinical diagnosis of dengue at the Hospital for Tropical Disease, Ho Chi Minh City, Vietnam. Patients underwent PPG monitoring early during admission alongside standard clinical care. PPG waveforms were analysed using machine learning models. Adult patients were classified between 3 severity classes: i) uncomplicated (ward-based), ii) moderate-severe (emergency department-based), and iii) severe (ICU-based). Data from paediatric patients were split into 2 classes: i) severe (during ICU stay) and ii) follow-up (14–21 days after the illness onset). Model performances were evaluated using standard classification metrics and 5-fold stratified cross-validation. Findings: We included PPG and clinical data from 132 adults and 15 paediatric patients with a median age of 28 (IQR, 21–35) and 12 (IQR, 9–13) years respectively. 1781 h of PPG data were available for analysis. The best performing convolutional neural network models (CNN) achieved a precision of 0.785 and recall of 0.771 in classifying adult patients according to severity class and a precision of 0.891 and recall of 0.891 in classifying between disease and post-disease state in paediatric patients. Interpretation: We demonstrate that the use of a low-cost wearable provided clinically actionable data to differentiate between patients with dengue of varying severity. Continuous monitoring and connectivity to early warning systems could significantly benefit clinical care in dengue, particularly within an endemic setting. Work is currently underway to implement these models for dynamic risk predictions and assist in individualised patient care. Funding: EPSRC Centre for Doctoral Training in High-Performance Embedded and Distributed Systems (HiPEDS) (Grant: EP/L016796/1) and the Wellcome Trust (Grants: 215010/Z/18/Z and 215688/Z/19/Z).

BackgroundIncreased data availability has prompted the creation of clinical decision support systems. These systems utilise clinical information to enhance health care provision, both to predict the likelihood of specific clinical outcomes or evaluate the risk of further complications. However, their adoption remains low due to concerns regarding the quality of recommendations, and a lack of clarity on how results are best obtained and presented.MethodsWe used autoencoders capable of reducing the dimensionality of complex datasets in order to produce a 2D representation denoted as latent space to support understanding of complex clinical data. In this output, meaningful representations of individual patient profiles are spatially mapped in an unsupervised manner according to their input clinical parameters. This technique was then applied to a large real-world clinical dataset of over 12,000 patients with an illness compatible with dengue infection in Ho Chi Minh City, Vietnam between 1999 and 2021. Dengue is a systemic viral disease which exerts significant health and economic burden worldwide, and up to 5% of hospitalised patients develop life-threatening complications.ResultsThe latent space produced by the selected autoencoder aligns with established clinical characteristics exhibited by patients with dengue infection, as well as features of disease progression. Similar clinical phenotypes are represented close to each other in the latent space and clustered according to outcomes broadly described by the World Health Organisation dengue guidelines. Balancing distance metrics and density metrics produced results covering most of the latent space, and improved visualisation whilst preserving utility, with similar patients grouped closer together. In this case, this balance is achieved by using the sigmoid activation function and one hidden layer with three neurons, in addition to the latent dimension layer, which produces the output (Pearson, 0.840; Spearman, 0.830; Procrustes, 0.301; GMM 0.321).ConclusionThis study demonstrates that when adequately configured, autoencoders can produce two-dimensional representations of a complex dataset that conserve the distance relationship between points. The output visualisation groups patients with clinically relevant features closely together and inherently supports user interpretability. Work is underway to incorporate these findings into an electronic clinical decision support system to guide individual patient management.

Abstract Background To characterise the longitudinal dynamics of C-reactive protein (CRP) and Procalcitonin (PCT) in a cohort of hospitalised patients with COVID-19 and support antimicrobial decision-making. Methods Longitudinal CRP and PCT concentrations and trajectories of 237 hospitalised patients with COVID-19 were modelled. The dataset comprised of 2,021 data points for CRP and 284 points for PCT. Pairwise comparisons were performed between: (i) those with or without significant bacterial growth from cultures, and (ii) those who survived or died in hospital. Results CRP concentrations were higher over time in COVID-19 patients with positive microbiology (day 9: 236 vs 123 mg/L, p

BackgroundSymptomatic dengue infection can result in a life-threatening shock syndrome and timely diagnosis is essential. Point-of-care tests for non-structural protein 1 and IgM are used widely but performance can be limited. We developed a supervised machine learning model to predict whether patients with acute febrile illnesses had a diagnosis of dengue or other febrile illnesses (OFI). The impact of seasonality on model performance over time was examined.MethodsWe analysed data from a prospective observational clinical study in Vietnam. Enrolled patients presented with an acute febrile illness of 90%).ConclusionSupervised machine learning models are able to discriminate between dengue and OFI diagnoses in patients presenting with an early undifferentiated febrile illness. These models could be of clinical utility in supporting healthcare decision-making and provide passive surveillance across dengue endemic regions. Effects of seasonality and changing disease prevalence must however be taken into account—this is of significant importance given unpredictable effects of human-induced climate change and the impact on health.

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 24 h 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

Optimal management of infectious diseases is guided by up-to-date information at the individual and public health levels. For infections of global importance, including emerging pandemics such as COVID-19 or prevalent endemic diseases such as dengue, identifying patients at risk of severe disease and clinical deterioration can be challenging, considering that the majority present with a mild illness. In our article, we describe the use of wearable technology for continuous physiological monitoring in healthcare settings. Deployment of wearables in hospital settings for the management of infectious diseases, or in the community to support syndromic surveillance during outbreaks, could provide significant, cost-effective advantages and improve healthcare delivery. We highlight a range of promising technologies employed by wearable devices and discuss the technical and ethical issues relating to implementation in the clinic, focusing on low- and middle- income countries. Finally, we propose a set of essential criteria for the rollout of wearable technology for clinical use.

Abstract Background Health care services must engage all relevant healthcare workers, including nurses, in optimal antimicrobial use to address the global threat of drug-resistant infections. Reflecting upon the variety of antimicrobial stewardship (AMS) nursing models already implemented in the UK could facilitate policymaking and decisions in other settings about context-sensitive, pragmatic nurse roles. Methods We describe purposefully selected cases drawn from the UK network of public sector nurses in AMS exploring their characteristics, influence, relations with clinical and financial structures, and role content. Results AMS nursing has been deployed in the UK within ‘vertical’, ‘horizontal’ or ‘hybrid’ models. The ‘vertical’ model refers to a novel, often unique consultant-type role ideally suited to transform organisational practice by legitimising nurse participation in antimicrobial decisions. Such organisational improvements may not be straightforward, though, due to scalability issues. The ‘horizontal’ model can foster coordinated efforts to increase optimal AMS behaviours in all nurses around a narrative of patient safety and quality. Such model may be unable to address tensions between the required institutional response to sepsis and the inappropriate use of antibiotics. Finally, the ‘hybrid’ model would increase AMS responsibilities for all nurses whilst allocating some expanded AMS skills to existing teams of specialists such as sepsis or vascular access nurses. This model can generate economies of scale, yet it may be threatened by a lack of clarity about a nurse-relevant vision. Conclusions A variety of models articulating the participation of nurses in antimicrobial stewardship efforts have already been implemented in public sector organisations in the UK. The strengths and weaknesses of each model need considering before implementation in other settings and healthcare systems, including precise metrics of success and careful consideration of context-sensitive, resource dependent and pragmatic solutions.

Abstract Background Antibiotic stewardship interventions are being implemented across different healthcare settings. We report the findings of a global survey of healthcare professionals on the implementation of antibiotic stewardship programmes. Methods Learners of a Massive Online Open Course (MOOC) on antibiotic stewardship were invited to complete an online survey on the core available organisational resources for stewardship. The categorical variables were analysed using chi-squared test, and Likert questions were analysed using an ordinal regression model. The p-values were considered as two-tailed. Significance was set at p-value of

Abstract Background Poverty increases the risk of contracting infectious diseases and therefore exposure to antibiotics. Yet there is lacking evidence on the relationship between income and non-income dimensions of poverty and antimicrobial resistance. Investigating such relationship would strengthen antimicrobial stewardship interventions. Methods A systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed, Ovid, MEDLINE, EMBASE, Scopus, CINAHL, PsychINFO, EBSCO, HMIC, and Web of Science databases were searched in October 2016. Prospective and retrospective studies reporting on income or non-income dimensions of poverty and their influence on colonisation or infection with antimicrobial-resistant organisms were retrieved. Study quality was assessed with the Integrated quality criteria for review of multiple study designs (ICROMS) tool. Results Nineteen articles were reviewed. Crowding and homelessness were associated with antimicrobial resistance in community and hospital patients. In high-income countries, low income was associated with Streptococcus pneumoniae and Acinetobacter baumannii resistance and a seven-fold higher infection rate. In low-income countries the findings on this relation were contradictory. Lack of education was linked to resistant S. pneumoniae and Escherichia coli. Two papers explored the relation between water and sanitation and antimicrobial resistance in low-income settings. Conclusions Despite methodological limitations, the results suggest that addressing social determinants of poverty worldwide remains a crucial yet neglected step towards preventing antimicrobial resistance.

Abstract Background Antibiotic-resistant bacteria (ARB) are selected by the use of antibiotics. The rational design of interventions to reduce levels of antibiotic resistance requires a greater understanding of how and where ARB are acquired. Our aim was to determine whether acquisition of ARB occurs more often in the community or hospital setting. Methods We used a mathematical model of the natural history of ARB to estimate how many ARB were acquired in each of these two environments, as well as to determine key parameters for further investigation. To do this, we explored a range of realistic parameter combinations and considered a case study of parameters for an important subset of resistant strains in England. Results If we consider all people with ARB in the total population (community and hospital), the majority, under most clinically derived parameter combinations, acquired their resistance in the community, despite higher levels of antibiotic use and transmission of ARB in the hospital. However, if we focus on just the hospital population, under most parameter combinations a greater proportion of this population acquired ARB in the hospital. Conclusions It is likely that the majority of ARB are being acquired in the community, suggesting that efforts to reduce overall ARB carriage should focus on reducing antibiotic usage and transmission in the community setting. However, our framework highlights the need for better pathogen-specific data on antibiotic exposure, ARB clearance and transmission parameters, as well as the link between carriage of ARB and health impact. This is important to determine whether interventions should target total ARB carriage or hospital-acquired ARB carriage, as the latter often dominated in hospital populations.

Abstract Background Enterobacteriaceae are a common cause of hospital infections. Carbapenems are a clinically effective treatment of such infections. However, resistance is on the rise. In particular, carbapenemase-producing carbapenem-resistant Enterobacteriaceae (CP-CRE) are increasingly common. In order to limit spread in clinical settings, screening and isolation is being recommended, but many different screening methods are available. We aimed to compare the impact and costs of three algorithms for detecting CP-CRE carriage. Methods We developed an individual-based simulation model to compare three screening algorithms using data from a UK National Health Service (NHS) trust. The first algorithm, “Direct PCR”, was highly sensitive/specific and quick (half a day), but expensive. The second, “Culture + PCR”, was relatively sensitive/specific but slower, requiring 2.5 days. A third algorithm, “PHE”, repeated the “Culture + PCR” three times with an additional PCR. Scenario analysis was used to compare several levels of CP-CRE prevalence and coverage of screening, different specialities as well as isolation strategies. Our outcomes were (1) days that a patient with CP-CRE was not detected and hence not isolated (“days at risk”), (2) isolation bed days, (3) total costs and (4) mean cost per CP-CRE risk day averted per year. We also explored limited isolation bed day capacity. Results We found that although a Direct PCR algorithm would reduce the number of CP-CRE days at risk, the mean cost per CP-CRE risk day averted per year was substantially higher than for a Culture + PCR algorithm. For example, in our model of an intensive care unit, during a year with a 1.6% CP-CRE prevalence and 63% screening coverage, there were 508 (standard deviation 15), 642 (14) and 655 (14) days at risk under screening algorithms Direct PCR, Culture + PCR and PHE respectively, with mean costs per risk day averted of £192, £61 and £79. These results were robust to sensitivity analyses. Conclusions Our results indicate that a Culture + PCR algorithm provides the optimal balance of cost and risk days averted, at varying isolation, prevalence and screening coverage scenarios. Findings from this study will help clinical organisations determine the optimal screening approach for CP-CRE, balancing risk and resources.

Abstract Background We developed a personalised antimicrobial information module co-designed with patients. This study aimed to evaluate the potential impact of this patient-centred intervention on short-term knowledge and understanding of antimicrobial therapy in secondary care. Methods Thirty previous patients who had received antibiotics in hospital within 12 months were recruited to co-design an intervention to promote patient engagement with infection management. Two workshops, containing five focus-groups were held. These were audio-recorded. Data were analysed using a thematic framework developed deductively based on previous work. Line-by-line coding was performed with new themes added to the framework by two researchers. This was used to inform the development of a patient information module, embedded within an electronic decision support tool (CDSS). The intervention was piloted over a four-week period at Imperial College Healthcare NHS Trust on 30 in-patients. Pre- and post-intervention questionnaires were developed and implemented to assess short term changes in patient knowledge and understanding and provide feedback on the intervention. Data were analysed using SPSS and NVIVO software. Results Within the workshops, there was consistency in identified themes. The participants agreed upon and co-designed a personalised PDF document that could be integrated into an electronic CDSS to be used by healthcare professionals at the point-of-care. Their aim for the tool was to provide individualised practical information, signpost to reputable information sources, and enhance communication between patients and healthcare professionals. Eighteen out of thirty in-patients consented to participant in the pilot evaluation with 15/18(83%) completing the study. Median (range) age was 66(22–85) years. The majority were male (10/15;66%). Pre-intervention, patients reported desiring further information regarding their infections and antibiotic therapy, including side effects of treatment. Deployment of the intervention improved short term knowledge and understanding of individuals infections and antibiotic management with median (IQR) scores improving from 3(2–5)/13 to 10(6–11)/13. 13/15(87%) reported that they would use the intervention again. Conclusion A personalised, patient-centred intervention improved understanding and short-term knowledge of infections and antibiotic therapy in participating patients’. Long term impact on attitudes and behaviours post discharge will be further investigated.

Abstract Background Antimicrobial Resistance is threatening our ability to treat common infectious diseases and overuse of antimicrobials to treat human infections in hospitals is accelerating this process. Clinical Decision Support Systems (CDSSs) have been proven to enhance quality of care by promoting change in prescription practices through antimicrobial selection advice. However, bypassing an initial assessment to determine the existence of an underlying disease that justifies the need of antimicrobial therapy might lead to indiscriminate and often unnecessary prescriptions. Methods From pathology laboratory tests, six biochemical markers were selected and combined with microbiology outcomes from susceptibility tests to create a unique dataset with over one and a half million daily profiles to perform infection risk inference. Outliers were discarded using the inter-quartile range rule and several sampling techniques were studied to tackle the class imbalance problem. The first phase selects the most effective and robust model during training using ten-fold stratified cross-validation. The second phase evaluates the final model after isotonic calibration in scenarios with missing inputs and imbalanced class distributions. Results More than 50% of infected profiles have daily requested laboratory tests for the six biochemical markers with very promising infection inference results: area under the receiver operating characteristic curve (0.80-0.83), sensitivity (0.64-0.75) and specificity (0.92-0.97). Standardization consistently outperforms normalization and sensitivity is enhanced by using the SMOTE sampling technique. Furthermore, models operated without noticeable loss in performance if at least four biomarkers were available. Conclusion The selected biomarkers comprise enough information to perform infection risk inference with a high degree of confidence even in the presence of incomplete and imbalanced data. Since they are commonly available in hospitals, Clinical Decision Support Systems could benefit from these findings to assist clinicians in deciding whether or not to initiate antimicrobial therapy to improve prescription practices.

In the last years, there has been an increase of antimicrobial resistance rates around the world with the misuse and overuse of antimicrobials as one of the main leading drivers. In response to this threat, a variety of initiatives have arisen to promote the efficient use of antimicrobials. These initiatives rely on antimicrobial surveillance systems to promote appropriate prescription practices and are provided by national or global health care institutions with limited consideration of the variations within hospitals. As a consequence, physicians’ adherence to these generic guidelines is still limited. To fill this gap, this work presents an automated approach to performing local antimicrobial surveillance from microbiology data. Moreover, in addition to the commonly reported resistance rates, this work estimates secular resistance trends through regression analysis to provide a single value that effectively communicates the resistance trend to a wider audience. The methods considered for trend estimation were ordinary least squares regression, weighted least squares regression with weights inversely proportional to the number of microbiology records available and autoregressive integrated moving average. Among these, weighted least squares regression was found to be the most robust against changes in the granularity of the time series and presented the best performance. To validate the results, three case studies have been thoroughly compared with the existing literature: (i) Escherichia coli in urine cultures; (ii) Escherichia coli in blood cultures; and (iii) Staphylococcus aureus in wound cultures. The benefits of providing local rather than general antimicrobial surveillance data of a higher quality is two fold. Firstly, it has the potential to stimulate engagement among physicians to strengthen their knowledge and awareness on antimicrobial resistance which might encourage prescribers to change their prescription habits more willingly. Moreover, it provides fundamental knowledge to the wide range of stakeholders to revise and potentially tailor existing guidelines to the specific needs of each hospital.

We describe the trend of antibiotic prescribing in out-of-hours (OOH) general practices (GP) before and during England’s first wave of the COVID-19 pandemic. We analysed practice-level prescribing records between January 2016 to June 2020 to report the trends for the total prescribing volume, prescribing of broad-spectrum antibiotics and key agents included in the national Quality Premium. We performed a time-series analysis to detect measurable changes in the prescribing volume associated with COVID-19. Before COVID-19, the total prescribing volume and the percentage of broad-spectrum antibiotics continued to decrease in-hours (IH). The prescribing of broad-spectrum antibiotics was higher in OOH (OOH: 10.1%, IH: 8.7%), but a consistent decrease in the trimethoprim-to-nitrofurantoin ratio was observed OOH. The OOH antibiotic prescribing volume diverged from the historical trend in March 2020 and started to decrease by 5088 items per month. Broad-spectrum antibiotic prescribing started to increase in OOH and IH. In OOH, co-amoxiclav and doxycycline peaked in March to May in 2020, which was out of sync with seasonality peaks (Winter) in previous years. While this increase might be explained by the implementation of the national guideline to use co-amoxiclav and doxycycline to manage pneumonia in the community during COVID-19, further investigation is required to see whether the observed reduction in OOH antibiotic prescribing persists and how this reduction might influence antimicrobial resistance and patient outcomes.

Abstract Background The uptake of improvement initiatives in infection prevention and control (IPC) has often proven challenging. Innovative interventions such as ‘serious games’ have been proposed in other areas to educate and help clinicians adopt optimal behaviours. There is limited evidence about the application and evaluation of serious games in IPC. The purposes of the study were: a) to synthesise research evidence on the use of serious games in IPC to support healthcare workers’ behaviour change and best practice learning; and b) to identify gaps across the formulation and evaluation of serious games in IPC. Methods A scoping study was conducted using the methodological framework developed by Arksey and O’Malley. We interrogated electronic databases (Ovid MEDLINE, Embase Classic + Embase, PsycINFO, Scopus, Cochrane, Google Scholar) in December 2015. Evidence from these studies was assessed against an analytic framework of intervention formulation and evaluation. Results Nine hundred sixty five unique papers were initially identified, 23 included for full-text review, and four finally selected. Studies focused on intervention inception and development rather than implementation. Expert involvement in game design was reported in 2/4 studies. Potential game users were not included in needs assessment and game development. Outcome variables such as fidelity or sustainability were scarcely reported. Conclusions The growing interest in serious games for health has not been coupled with adequate evaluation of processes, outcomes and contexts involved. Explanations about the mechanisms by which game components may facilitate behaviour change are lacking, further hindering adoption.

Objectives: Multifactorial interventions are crucial to arrest the threat posed by infectious diseases. Public involvement requires adequate information, but determinants such as health literacy can impact on the effective use of such knowledge. The influence of health literacy on infectious diseases is examined in this paper. Methods: Databases were searched from January 1999 through July 2015 seeking studies reporting on health literacy and infections such tuberculosis, malaria, and influenza, and infection-related behaviours such as vaccination and hand hygiene. HIV was excluded, as comprehensive reviews have already been published. Results: Studies were found on antibiotic knowledge and use, the adoption of influenza and MMR immunizations, and screening for sexually transmitted and viral hepatitis infections. There was a lack of investigations on areas such as tuberculosis, malaria, hand hygiene, and diarrhoeal diseases. Conclusions: Limited or insufficient health literacy was associated with reduced adoption of protective behaviours such as immunization, and an inadequate understanding of antibiotics, although the relationship was not consistent. Large gaps remain in relation to infectious diseases with a high clinical and societal impact, such as tuberculosis and malaria.