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Abstract Aims Surgical Antibiotic Prophylaxis (SAP) in Nigeria is often not evidence based. The aim of this study is to test if the GADSA application can change prescription behaviour of surgeons in Nigeria. In addition, the study aims to identify AMS strategies and policies for the future. Methods The GADSA gamified decision support app uses WHO and Sanford prescribing guidelines to deliver real-time persuasive technology feedback to surgeons through an interactive mentor. The app can advise on whether clinician’s decisions align with SAP recommendations and provides the opportunity for clinicians to make adjustments. Twenty surgeons actively participated in a 6-month pilot study in three hospitals in Nigeria. The surgeons determined the risk of infection of a surgical procedure, and the need, type and duration of SAP. The study used a longitudinal approach to test whether the GADSA app significantly changed prescribing behaviour of participating surgeons by analysing the reported prescription decisions within the app. Results 321 SAP prescriptions were recorded. Concerning the surgical risk decision, 12% of surgeons changed their decision to be in line with guidelines after app feedback (p

Abstract Background The COVID-19 pandemic disproportionately affected people living and working in UK care homes causing high mortality rates. Vaccinating staff members and residents is considered the most effective intervention to reduce infection and its transmission rates. However, uptake of the first dose of the COVID-19 vaccine in care homes was variable. We sought to investigate factors influencing uptake of COVID-19 vaccination in care home staff to inform strategies to increase vaccination uptake and inform future preparedness. Methods Twenty care home staff including managerial and administrative staff, nurses, healthcare practitioners and support staff from nine care homes across England participated in semi-structured telephone interviews (March-June 2021) exploring attitudes towards the COVID-19 vaccine and factors influencing uptake. We used thematic analysis to generate themes which were subsequently deductively mapped to the Capability, Opportunity, Motivation-Behaviour (COM-B) model. The Behavioural Change Wheel (BCW) was used to identify potential intervention strategies to address identified influences. Results Enablers to vaccine uptake included the willingness to protect care home residents, staff and family/friends from infection and the belief that vaccination provided a way back to normality (reflective motivation); convenience of vaccination and access to accurate information (physical opportunity); and a supporting social environment around them favouring vaccination (social opportunity). Barriers included fears about side-effects (automatic motivation); a lack of trust due to the quick release of the vaccine (reflective motivation); and feeling pressurised to accept vaccination if mandatory (automatic motivation). Conclusions We identified influences on COVID-19 vaccine uptake by care home staff that can inform the implementation of future vaccination programmes. Strategies likely to support uptake include information campaigns and facilitating communication between staff and managers to openly discuss concerns regarding possible vaccination side effects. Freedom of choice played an important role in the decision to be vaccinated suggesting that the decision to mandate vaccination may have unintended behavioural consequences.

Abstract Background Antimicrobial resistance (AMR) is a major global health threat caused by the inappropriate use of antimicrobials in healthcare and other settings. Antimicrobial stewardship (AMS) is a broad multi-component health services intervention that promotes and monitors the judicious use of antimicrobials to preserve their future effectiveness. A main component of AMS is education and training (E&T). However, there are often discrepancies in how such interventions are implemented and delivered in hospital-based care. The aim of this study was to explore the factors influencing the implementation of AMS E&T in UK hospitals. Methods Semi-structured interviews were carried out with AMS E&T trainers in UK hospitals. The interview schedule was developed using the Capability, Opportunity, Motivation = Behaviour (COM-B) model. Participants were identified via professional networks and social media. Interviews were analysed using inductive thematic analysis, followed by deductive analysis using the COM-B model as a framework. Results A total of 34 participants (26 antimicrobial pharmacists, 3 nurses, 1 advanced clinical practitioner, 2 infectious disease consultants, 1 microbiologist and 1 clinical scientist). responsible for designing, implementing and evaluating AMS E&T in UK hospitals (five from Northern Ireland, four from Wales, two from Scotland and 23 from England) took part in virtual interviews. Key themes were: (1) The organisational context, including system-level barriers to AMS included competing organisational targets (Reflective motivation and physical opportunity) and the impact of the COVID-19 pandemic on activity (Physical opportunity); (2) Healthcare professionals’ roles and the wider multi-disciplinary team, such that AMS roles were defined and addressed poorly in E&T (Social opportunity); and (3) The individual perception of the need for AMS E&T in hospital-based care, manifest in a perceived lack of conviction of the wider threat of AMR and the resulting need for AMS E&T (Reflective motivation). Conclusion This study has identified factors influencing implementation of AMS E&T in UK hospitals and further identified where implemented, AMS E&T did not address real-world challenges. Current AMS E&T needs to be optimised to elicit practice change, with recommendations including training and engaging the wider work-force and drawing upon theoretically-informed intervention development frameworks to inform AMS E&T to better target AMS behaviour change.

Abstract Background Urinary tract infection (UTI) is a leading cause of hospital admissions and is diagnosed based on urinary symptoms and microbiological cultures. Due to lags in the availability of culture results of up to 72 h, and the limitations of routine diagnostics, many patients with suspected UTI are started on antibiotic treatment unnecessarily. Predictive models based on routinely collected clinical information may help clinicians to rule out a diagnosis of bacterial UTI in low-risk patients shortly after hospital admission, providing additional evidence to guide antibiotic treatment decisions. Methods Using electronic hospital records from Queen Elizabeth Hospital Birmingham (QEHB) collected between 2011 and 2017, we aim to develop a series of models that estimate the probability of bacterial UTI at presentation in the emergency department (ED) among individuals with suspected UTI syndromes. Predictions will be made during ED attendance and at different time points after hospital admission to assess whether predictive performance may be improved over time as more information becomes available about patient status. All models will be externally validated for expected future performance using QEHB data from 2018/2019. Discussion Risk prediction models using electronic health records offer a new approach to improve antibiotic prescribing decisions, integrating clinical and demographic data with test results to stratify patients according to their probability of bacterial infection. Used in conjunction with expert opinion, they may help clinicians to identify patients that benefit the most from early antibiotic cessation.

Abstract Background Overuse of antibiotics has contributed to antimicrobial resistance; a growing public health threat. In long-term care facilities, levels of inappropriate prescribing are as high as 75%. Numerous interventions targeting long-term care facilities’ antimicrobial stewardship have been reported with varying, and largely unexplained, effects. Therefore, this review aimed to apply behavioural science frameworks to specify the component behaviour change techniques of stewardship interventions in long-term care facilities and identify those components associated with improved outcomes. Method A systematic review (CRD42018103803) was conducted through electronic database searches. Two behavioural science frameworks, the Behaviour Change Wheel and Behaviour Change Technique Taxonomy were used to classify intervention descriptions into intervention types and component behaviour change techniques used. Study design and outcome heterogeneity prevented meta-analysis and meta-regression. Interventions were categorised as ‘very promising’ (all outcomes statistically significant), ‘quite promising’ (some outcomes statistically significant), or ‘not promising’ (no outcomes statistically significant). ‘Promise ratios’ (PR) were calculated for identified intervention types and behaviour change techniques by dividing the number of (very or quite) promising interventions featuring the intervention type or behaviour change technique by the number of interventions featuring the intervention type or behaviour change technique that were not promising. Promising intervention types and behaviour change techniques were defined as those with a PR ≥ 2. Results Twenty studies (of19 interventions) were included. Seven interventions (37%) were ‘very promising’, eight ‘quite promising’ (42%) and four ‘not promising’ (21%). Most promising intervention types were ‘persuasion’ (n = 12; promise ratio (PR) = 5.0), ‘enablement’ (n = 16; PR = 4.33) and ‘education’ (n = 19; PR = 3.75). Most promising behaviour change techniques were ‘feedback on behaviour’ (n = 9; PR = 8.0) and ‘restructuring the social environment’ (e.g. staff role changes; n = 8; PR = 7.0). Conclusion Systematic identification of the active ingredients of antimicrobial stewardship in long-term care facilities was facilitated through the application of behavioural science frameworks. Incorporating environmental restructuring and performance feedback may be promising intervention strategies for antimicrobial stewardship interventions within long-term care facilities.

Abstract Background There is an urgent need to develop biomarkers that stratify risk of bacterial infection in order to support antimicrobial stewardship in emergency hospital admissions. Methods We used computational machine learning to derive a rule-out blood transcriptomic signature of bacterial infection (SeptiCyte™ TRIAGE) from eight published case-control studies. We then validated this signature by itself in independent case-control data from more than 1500 samples in total, and in combination with our previously published signature for viral infections (SeptiCyte™ VIRUS) using pooled data from a further 1088 samples. Finally, we tested the performance of these signatures in a prospective observational cohort of emergency department (ED) patients with fever, and we used the combined SeptiCyte™ signature in a mixture modelling approach to estimate the prevalence of bacterial and viral infections in febrile ED patients without microbiological diagnoses. Results The combination of SeptiCyte™ TRIAGE with our published signature for viral infections (SeptiCyte™ VIRUS) discriminated bacterial and viral infections in febrile ED patients, with a receiver operating characteristic area under the curve of 0.95 (95% confidence interval 0.90–1), compared to 0.79 (0.68–0.91) for WCC and 0.73 (0.61–0.86) for CRP. At pre-test probabilities 0.35 and 0.72, the combined SeptiCyte™ score achieved a negative predictive value for bacterial infection of 0.97 (0.90–0.99) and 0.86 (0.64–0.96), compared to 0.90 (0.80–0.94) and 0.66 (0.48–0.79) for WCC and 0.88 (0.69–0.95) and 0.60 (0.31–0.72) for CRP. In a mixture modelling approach, the combined SeptiCyte™ score estimated that 24% of febrile ED cases receiving antibacterials without a microbiological diagnosis were due to viral infections. Our analysis also suggested that a proportion of patients with bacterial infection recovered without antibacterials. Conclusions Blood transcriptional biomarkers offer exciting opportunities to support precision antibacterial prescribing in ED and improve diagnostic classification of patients without microbiologically confirmed infections.

Abstract Background Driven by alcohol consumption and obesity, the prevalence of non-viral liver disease in the UK is increasing. Due to its silent and slow nature, the progression of liver disease is currently unpredictable and challenging to monitor. The latest National Institute for Health Care Excellence cirrhosis guidelines call for a validated risk tool that would allow general practitioners to identify patients that are at high risk of developing cirrhosis. Methods Using linked electronic health records from the Clinical Practice Research Datalink (a database of > 10 million patients in England), we aim to develop and validate a prediction model to estimate 2-, 5- and 10-year risk of cirrhosis. The model will provide individualised cirrhosis risk predictions for adult primary care patients, free from underlying liver disease or viral hepatitis infection, whose liver blood test results come back abnormal. We will externally validate the model in patients from 30 further Clinical Practice Research Datalink general practices in England. Discussion The prediction model will provide estimates of cirrhosis risk in primary care patients with abnormal liver blood test results to guide referral to secondary care, to identify patients who are in serious need of preventative health interventions and to help reassure patients at low risk of cirrhosis in the long term.

An amendment to this paper has been published and can be accessed via the original article.