Forecasting Implementation, Adoption, and Evaluation Challenges for an Electronic Game-Based Antimicrobial Stewardship Intervention: Co-Design Workshop With Multidisciplinary Stakeholders.

BACKGROUND: Serious games have been proposed to address the lack of engagement and sustainability traditionally affecting interventions aiming to improve optimal antibiotic use among hospital prescribers. OBJECTIVE: The goal of the research was to forecast gaps in implementation, adoption and evaluation of game-based interventions, and co-design solutions with antimicrobial clinicians and digital and behavioral researchers. METHODS: A co-development workshop with clinicians and academics in serious games, antimicrobials, and behavioral sciences was organized to open the International Summit on Serious Health Games in London, United Kingdom, in March 2018. The workshop was announced on social media and online platforms. Attendees were asked to work in small groups provided with a laptop/tablet and the latest version of the game On call: Antibiotics. A workshop leader guided open group discussions around implementation, adoption, and evaluation threats and potential solutions. Workshop summary notes were collated by an observer. RESULTS: There were 29 participants attending the workshop. Anticipated challenges to resolve reflected implementation threats such as an inadequate organizational arrangement to scale and sustain the use of the game, requiring sufficient technical and educational support and a streamlined feedback mechanism that made best use of data arriving from the game. Adoption threats included collective perceptions that a game would be a ludic rather than professional tool and demanding efforts to integrate all available educational solutions so none are seen as inferior. Evaluation threats included the need to combine game metrics with organizational indicators such as antibiotic use, which may be difficult to enable. CONCLUSIONS: As with other technology-based interventions, deploying game-based solutions requires careful planning on how to engage and support clinicians in their use and how best to integrate the game and game outputs onto existing workflows. The ludic characteristics of the game may foster perceptions of unprofessionalism among gamers, which would need buffering from the organization.

Towards a minimally invasive device for beta-lactam monitoring in humans.

Antimicrobial resistance is a leading patient safety issue. There is a need to develop novel mechanisms for monitoring and subsequently improving the precision of how we use antibiotics. A surface modified microneedle array was developed for monitoring beta-lactam antibiotic levels in human interstitial fluid. The sensor was fabricated by anodically electrodepositing iridium oxide (AEIROF) onto a platinum surface on the microneedle followed by fixation of beta-lactamase enzyme within a hydrogel. Calibration of the sensor was performed to penicillin-G in buffer solution (PBS) and artificial interstitial fluid (ISF). Further calibration of a platinum disc electrode was undertaken using amoxicillin and ceftriaxone. Open-circuit potentials were performed and data analysed using the Hill equation and log(concentration [M]) plots. The microneedle sensor demonstrated high reproducibility between penicillin-G runs in PBS with mean Km (±1SD) = 0.0044 ± 0.0013 M and mean slope function of log(concentration plots) 29 ± 1.80 mV/decade (r2=0.933). Response was reproducible after 28 days storage at 4°C. In artificial ISF, the sensors response was Km (±1SD) = 0.0077 ± 0.0187 M and a slope function of 34 ± 1.85 mv/decade (r2=0.995). Our results suggest that microneedle array based beta-lactam sensing may be a future application of this AEIROF based enzymatic sensor.

Supervised learning for infection risk inference using pathology data.

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.

Mapping the decision pathways of acute infection management in secondary care among UK medical physicians: a qualitative study.

BACKGROUND: The inappropriate use of antimicrobials drives antimicrobial resistance. We conducted a study to map physician decision-making processes for acute infection management in secondary care to identify potential targets for quality improvement interventions. METHODS: Physicians newly qualified to consultant level participated in semi-structured interviews. Interviews were audio recorded and transcribed verbatim for analysis using NVIVO11.0 software. Grounded theory methodology was applied. Analytical categories were created using constant comparison approach to the data and participants were recruited to the study until thematic saturation was reached. RESULTS: Twenty physicians were interviewed. The decision pathway for the management of acute infections follows a Bayesian-like step-wise approach, with information processed and systematically added to prior assumptions to guide management. The main emerging themes identified as determinants of the decision-making of individual physicians were (1) perceptions of providing 'optimal' care for the patient with infection by providing rapid and often intravenous therapy; (2) perceptions that stopping/de-escalating therapy was a senior doctor decision with junior trainees not expected to contribute; and (3) expectation of interactions with local guidelines and microbiology service advice. Feedback on review of junior doctor prescribing decisions was often lacking, causing frustration and confusion on appropriate practice within this cohort. CONCLUSION: Interventions to improve infection management must incorporate mechanisms to promote distribution of responsibility for decisions made. The disparity between expectations of prescribers to start but not review/stop therapy must be urgently addressed with mechanisms to improve communication and feedback to junior prescribers to facilitate their continued development as prudent antimicrobial prescribers.

Leprosy and lymphatic filariasis comorbidity: the case for an integrated functional limitation grading system.

Worldwide, both lymphatic filariasis (LF) and leprosy are major causes of morbidity and functional limitation. Despite an abundance of data on the impairment caused by these infections in isolation, there is little data on disability in patients unfortunate enough to be affected by both infections. We present two cases of patients with LF and leprosy comorbidity. Both cases suffer from Grade 2 disability and chronic lymphedema in the same limb (left and right leg, respectively). These morbidities cause significant functional limitation in the patients' activities of daily living. Both LF and leprosy are endemic in India and their comorbid conditions contribute to functional limitation. However, there is still currently no integrated scale for assessment of interventions for functional limitation in these patients. Investigating and managing impairment and functional limitations individually is the least cost effective and sustainable strategy. Considering the similarities of caring for functional limitations in both diseases and assuming its efficacy, augmentation of LF care services with leprosy referral centres may be beneficial. Integrated approaches have been pilot tested at district level in states in India under the title 'Integrated Prevention of Deformity project' (IPoD). Further work is required to identify a reliable, holistic, functional impairment scoring system, which allows for comparison between diseases. This may help promote greater understanding of impairments and functional limitations these cause and allow effective monitoring and evaluation of all aspects, activities and stages of similar programmes.

Leprosy and tuberculosis concomitant infection: a poorly understood, age-old relationship.

Historically, archaeological evidence, post-mortem findings and retrospective analysis of leprosy institutions' data demonstrates a high observed incidence of concomitant infection with leprosy and tuberculosis (TB). However, reports of concomitant infection in the modern literature remain scarce, with estimates of annual new case detection rates of concomitant infection at approximately 0.02 cases per 100,000 population. Whilst the mechanism for this apparent decline in concomitant infections remains unclear, further research on this topic has remained relatively neglected. Modelling of the interaction of the two organisms has suggested that the apparent decline in observations of concomitant infection may be due to the protective effects of cross immunity, whilst more recently others have questioned whether it is a more harmful relationship, predisposing towards increased host mortality. We review recent evidence, comparing it to previously held understanding on the epidemiological relationship and our own experience of concomitant infection. From this discussion, we highlight several under-investigated areas, which may lead to improvements in the future delivery of leprosy management and services, as well as enhance understanding in other fields of infection management. These include, a) highlighting the need for greater understanding of host immunogenetics involved in concomitant infection, b) whether prolonged courses of high dose steroids pre-dispose to TB infection? and, c) whether there is a risk of rifampicin resistance developing in leprosy patients treated in the face of undiagnosed TB and other infections? Longitudinal work is still required to characterise these temporal relationships further and add to the current paucity of literature on this subject matter.

Innovative approaches in phenotypic beta-lactamase detection for personalised infection management.

Beta-lactamase-producing Enterobacteriaceae present a significant therapeutic challenge. Current developments in phenotypic diagnostics focus primarily on rapid minimum inhibitory concentration (MIC) determination. There is a requirement for rapid phenotypic diagnostics to improve antimicrobial susceptibility tests (AST) and aid prescribing decisions. Phenotypic AST are limited in their ability to characterise beta-lactamase-producing Enterobacteriaceae in detail. Despite advances in rapid AST, gaps and opportunities remain for developing additional diagnostic approaches that facilitate personalised antimicrobial prescribing. In this perspective, we highlight the state-of-the-art in beta-lactamase detection, identify gaps in current practice, and discuss barriers for innovation within this field.

Machine learning to predict antimicrobial resistance: future applications in clinical practice?

INTRODUCTION: Machine learning (ML) is increasingly being used to predict antimicrobial resistance (AMR). This review aims to provide physicians with an overview of the literature on ML as a means of AMR prediction. METHODS: References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, ACM Digital Library, and IEEE Xplore Digital Library up to December 2023. RESULTS: Thirty-six studies were included in this review. Thirty-two studies (32/36, 89 %) were based on hospital data and four (4/36, 11 %) on outpatient data. The vast majority of them were conducted in high-resource settings (33/36, 92 %). Twenty-four (24/36, 67 %) studies developed systems to predict drug resistance in infected patients, eight (8/36, 22 %) tested the performances of ML-assisted antibiotic prescription, two (2/36, 6 %) assessed ML performances in predicting colonization with carbapenem-resistant bacteria and, finally, two assessed national and international AMR trends. The most common inputs were demographic characteristics (25/36, 70 %), previous antibiotic susceptibility testing (19/36, 53 %) and prior antibiotic exposure (15/36, 42 %). Thirty-three (92 %) studies targeted prediction of Gram-negative bacteria (GNB) resistance as an output (92 %). The studies included showed moderate to high performances, with AUROC ranging from 0.56 to 0.93. CONCLUSION: ML can potentially provide valuable assistance in AMR prediction. Although the literature on this topic is growing, future studies are needed to design, implement, and evaluate the use and impact of ML decision support systems.

A practical laboratory method to determine ceftazidime-avibactam-aztreonam synergy in patients with New Delhi metallo-beta-lactamase (NDM)-producing Enterobacterales infection.

OBJECTIVES: In response to infection with New Delhi metallo-beta-lactamase (NDM)-producing Enterobacterales, combination antimicrobial therapy with ceftazidime/avibactam (CAZ/AVI) plus aztreonam (ATM) has been explored. This study evaluated a practical laboratory method of testing for clinically significant synergy between CAZ/AVI+ATM in NDM-producing Enterobacterales. METHODS: Minimum inhibitory concentrations (MICs) of clinical NDM-producing isolates were determined for ATM alone and CAZ/AVI+ATM using broth dilution. Restoration of the ATM breakpoint after the addition of CAZ/AVI was explored. A CAZ/AVI Etest/ATM disc method was compared with broth dilution. RESULTS: Of 43 isolates, 33 (77%) were ATM resistant (median [range] MIC = 56 [16-512] mg/L). Addition of CAZ/AVI restored the ATM breakpoint (MIC <4 mg/L) in 29 of 33 resistant isolates (89%). Overall, the Etest/disc method correlated with the findings from broth dilution in 35 of 43 cases (81%). Etest/disc sensitivity was 77% and specificity 85%. Positive predictive value was 92% and negative predictive value 61%. CONCLUSION: CAZ/AVI+ATM demonstrated significant synergy in most ATM-resistant NDM-producing Enterobacterales. The Etest/disc method is a quick, reproducible, and reliable method of testing for clinically relevant synergy in the microbiology laboratory.