Diagnostic stewardship in infectious diseases: a scoping review.

Introduction. The term 'diagnostic stewardship' is relatively new, with a recent surge in its use within the literature. Despite its increasing popularity, a precise definition remains elusive. Various attempts have been made to define it, with some viewing it as an integral part of antimicrobial stewardship. The World Health Organization offers a broad definition, emphasizing the importance of timely, accurate diagnostics. However, inconsistencies in the use of this term still persist, necessitating further clarification.Gap Statement. There are currently inconsistencies in the definition of diagnostic stewardship used within the academic literature.Aim. This scoping review aims to categorize the use of diagnostic stewardship approaches and define this approach by identifying common characteristics and factors of its use within the literature.Methodology. This scoping review undertook a multi-database search from date of inception until October 2022. Any observational or experimental study where the authors define the intervention to be diagnostic stewardship from any clinical area was included. Screening of all papers was undertaken by a single reviewer with 10% verification by a second reviewer. Data extraction was undertaken by a single reviewer using a pre-piloted form. Given the wide variation in study design and intervention outcomes, a narrative synthesis approach was applied. Studies were clustered around common diagnostic stewardship interventions where appropriate.Results. After duplicate removal, a total of 1310 citations were identified, of which, after full-paper screening, 105 studies were included in this scoping review. The classification of an intervention as taking a diagnostic stewardship approach is a relatively recent development, with the first publication in this field dating back to 2017. The majority of research in this area has been conducted within the USA, with very few studies undertaken outside this region. Visual inspection of the citation map reveals that the current evidence base is interconnected, with frequent references to each other's work. The interventions commonly adopt a restrictive approach, utilizing hard and soft stops within the pre-analytical phase to restrict access to testing. Upon closer examination of the outcomes, it becomes evident that there is a predominant focus on reducing the number of tests rather than enhancing the current test protocol. This is further reflected in the limited number of studies that report on test performance (including protocol improvements, specificity and sensitivity).Conclusion. Diagnostic stewardship seems to have deviated from its intended course, morphing into a rather rudimentary instrument wielded not to enhance but to constrict the scope of testing. Despite the World Health Organization's advocacy for an ideology that promotes a more comprehensive approach to quality improvement, it may be more appropriate to consider alternative regional narratives when categorizing these types of quality improvement interventions.

A smart mask to enforce social contracts based on IOTA Tangle.

In this paper we present the design for a smart-mask to mitigate the impact of an airborne virus such as COVID-19. The design utilises recent results from feedback control theory over a distributed ledger that have been developed to enforce compliance in a pseudo-anonymous manner. The design is based on the use of the IOTA distributed ledger. A hardware-in-the-loop simulation based on indoor positioning, paired with Monte-Carlo simulations, is developed to demonstrate the efficacy of the designed prototype.

Does comorbidity index predict OPAT readmission?

Objectives: To determine if the Charlson comorbidity index (CCI) is an accurate predictor of unplanned readmissions for patients using outpatient parenteral antimicrobial therapy (OPAT) services. Methods: Retrospective analysis of patients >16 years of age who had received OPAT at Lancashire Teaching Hospitals between 2019 and 2021. The number of unplanned hospitalizations was measured and categorized as OPAT related or non-OPAT related. The CCI for each patient group was calculated using an online tool, and logistic regression was used to assess the association between risk factors and risk of being readmitted. Results: The cohort consisted of 741 patients. Unplanned readmission was seen in 112 patients (15.1%). The mean CCI score for patients with OPAT-related readmissions was 4.22, 0.92 higher than the mean for patients who were not readmitted (3.30). The mean CCI score for patients with non-OPAT-related readmissions was higher still at 4.89. The logistic regression showed that increased CCI, age, male gender and home location compared with clinic were associated with increased odds of readmission, although these effects did not meet statistical significance. Conclusions: These results suggest that a higher CCI score is associated with a non-statistically significant increased risk of unplanned hospitalization. We concluded that the CCI may therefore be used in future decision-making regarding the acceptance of patients to OPAT and requires further investigation.

A cyber-physical system to design 3D models using mixed reality technologies and deep learning for additive manufacturing.

I-nteract is a cyber-physical system that enables real-time interaction with both virtual and real artifacts to design 3D models for additive manufacturing by leveraging mixed-reality technologies. This paper presents novel advances in the development of the interaction platform to generate 3D models using both constructive solid geometry and artificial intelligence. In specific, by taking advantage of the generative capabilities of deep neural networks, the system has been automated to generate 3D models inferred from a single 2D image captured by the user. Furthermore, a novel generative neural architecture, SliceGen, has been proposed and integrated with the system to overcome the limitation of single-type genus 3D model generation imposed by differentiable-rendering-based deep neural architectures. The system also enables the user to adjust the dimensions of the 3D models with respect to their physical workspace. The effectiveness of the system is demonstrated by generating 3D models of furniture (e.g., chairs and tables) and fitting them into the physical space in a mixed reality environment. The presented developmental advances provide a novel and immersive form of interaction to facilitate the inclusion of a consumer into the design process for personal fabrication.

Subgroup fairness in two-sided markets.

It is well known that two-sided markets are unfair in a number of ways. For example, female drivers on ride-hailing platforms earn less than their male colleagues per mile driven. Similar observations have been made for other minority subgroups in other two-sided markets. Here, we suggest a novel market-clearing mechanism for two-sided markets, which promotes equalization of the pay per hour worked across multiple subgroups, as well as within each subgroup. In the process, we introduce a novel notion of subgroup fairness (which we call Inter-fairness), which can be combined with other notions of fairness within each subgroup (called Intra-fairness), and the utility for the customers (Customer-Care) in the objective of the market-clearing problem. Although the novel non-linear terms in the objective complicate market clearing by making the problem non-convex, we show that a certain non-convex augmented Lagrangian relaxation can be approximated to any precision in time polynomial in the number of market participants using semidefinite programming, thanks to its "hidden convexity". This makes it possible to implement the market-clearing mechanism efficiently. On the example of driver-ride assignment in an Uber-like system, we demonstrate the efficacy and scalability of the approach and trade-offs between Inter- and Intra-fairness.

Markovian city-scale modelling and mitigation of micro-particles from tires.

The recent uptake in popularity in vehicles with zero tailpipe emissions is a welcome development in the fight against traffic induced airborne pollutants. As vehicle fleets become electrified, and tailpipe emissions become less prevalent, non-tailpipe emissions (from tires and brake disks) will become the dominant source of traffic related emissions, and will in all likelihood become a major concern for human health. This trend is likely to be exacerbated by the heavier weight of electric vehicles, their increased power, and their increased torque capabilities, when compared with traditional vehicles. While the problem of emissions from tire wear is well-known, issues around the process of tire abrasion, its impact on the environment, and modelling and mitigation measures, remain relatively unexplored. Work on this topic has proceeded in several discrete directions including: on-vehicle collection methods; vehicle tire-wear abatement algorithms and controlling the ride characteristics of a vehicle, all with a view to abating tire emissions. Additional approaches include access control mechanisms to manage aggregate tire emissions in a geofenced area with other notable work focussing on understanding the particle size distribution of tire generated PM, the degree to which particles become airborne, and the health impacts of tire emissions. While such efforts are already underway, the problem of developing models to predict the aggregate picture of a network of vehicles at the scale of a city, has yet to be considered. Our objective in this paper is to present one such model, built using ideas from Markov chains. Applications of our modelling approach are given toward the end of this note, both to illustrate the utility of the proposed method, and to illustrate its application as part of a method to collect tire dust particles.

Hysteresis-based supervisory control with application to non-pharmaceutical containment of COVID-19.

The recent COVID-19 outbreak has motivated an extensive development of non-pharmaceutical intervention policies for epidemics containment. While a total lockdown is a viable solution, interesting policies are those allowing some degree of normal functioning of the society, as this allows a continued, albeit reduced, economic activity and lessens the many societal problems associated with a prolonged lockdown. Recent studies have provided evidence that fast periodic alternation of lockdown and normal-functioning days may effectively lead to a good trade-off between outbreak abatement and economic activity. Nevertheless, the correct number of normal days to allocate within each period in such a way to guarantee the desired trade-off is a highly uncertain quantity that cannot be fixed a priori and that must rather be adapted online from measured data. This adaptation task, in turn, is still a largely open problem, and it is the subject of this work. In particular, we study a class of solutions based on hysteresis logic. First, in a rather general setting, we provide general convergence and performance guarantees on the evolution of the decision variable. Then, in a more specific context relevant for epidemic control, we derive a set of results characterizing robustness with respect to uncertainty and giving insight about how a priori knowledge about the controlled process may be used for fine-tuning the control parameters. Finally, we validate the results through numerical simulations tailored on the COVID-19 outbreak.

Seroprevalence of SARS-CoV-2 infection in healthcare workers in a large teaching hospital in the North West of England: a period prevalence survey.

OBJECTIVES: Since its emergence in late 2019, SARS-CoV-2 has caused a global pandemic that has significantly challenged healthcare systems. Healthcare workers have previously been shown to have experienced higher rates of infection than the general population. We aimed to assess the extent of infection in staff working in our healthcare setting. DESIGN: A retrospective analysis of antibody results, compared with staff demographic data, and exposure to patients with COVID-19 infection. SETTING: A large teaching hospital in the North West of England. PARTICIPANTS: 4474 staff in diverse clinical and non-patient facing roles who volunteered for SARS-CoV-2 antibody testing by the Roche Elecsys assay between 29 May and 4 July 2020. RESULTS: Seroprevalence was 17.4%. Higher rates were seen in Asian/Asian British (OR 1.61, 95% CI 1.27 to 2.04) and Black/Black British (OR 2.08, 95% CI 1.25 to 3.45) staff. Staff working in any clinical location were more likely to be seropositive (OR 2.68, 95% 2.27 to 3.15). Staff were at an increased risk of seropositivity as the 'per 100 COVID-19 bed-days change' increased in the clinical area in which they worked (OR 1.12, 95% 1.10 to 1.14). Staff working in critical care were no more likely to have detectable antibodies than staff working in non-clinical areas. Symptoms compatible with COVID-19 were reported in 41.8% and antibodies were detected in 30.7% of these individuals. In staff who reported no symptoms, antibodies were detected in 7.7%. In all staff who had detectable antibodies, 25.2% reported no symptoms. CONCLUSIONS: Staff working in clinical areas where patients with COVID-19 were nursed were more likely to have detectable antibodies. The relationship between seropositivity in healthcare workers and the increase in 'per 100 COVID-19 bed-days' of the area in which they worked, although statistically significant, was weak, suggesting other contributing factors to the risk profile. Of staff with detectable antibodies and therefore evidence of prior infection, a quarter self-reported that they had experienced no compatible symptoms. This has implications for potential unrecorded transmission in both staff and patients.

Post-lockdown abatement of COVID-19 by fast periodic switching.

COVID-19 abatement strategies have risks and uncertainties which could lead to repeating waves of infection. We show-as proof of concept grounded on rigorous mathematical evidence-that periodic, high-frequency alternation of into, and out-of, lockdown effectively mitigates second-wave effects, while allowing continued, albeit reduced, economic activity. Periodicity confers (i) predictability, which is essential for economic sustainability, and (ii) robustness, since lockdown periods are not activated by uncertain measurements over short time scales. In turn-while not eliminating the virus-this fast switching policy is sustainable over time, and it mitigates the infection until a vaccine or treatment becomes available, while alleviating the social costs associated with long lockdowns. Typically, the policy might be in the form of 1-day of work followed by 6-days of lockdown every week (or perhaps 2 days working, 5 days off) and it can be modified at a slow-rate based on measurements filtered over longer time scales. Our results highlight the potential efficacy of high frequency switching interventions in post lockdown mitigation. All code is available on Github at https://github.com/V4p1d/FPSP_Covid19. A software tool has also been developed so that interested parties can explore the proof-of-concept system.

Kemeny-based testing for COVID-19.

Testing, tracking and tracing abilities have been identified as pivotal in helping countries to safely reopen activities after the first wave of the COVID-19 virus. Contact tracing apps give the unprecedented possibility to reconstruct graphs of daily contacts, so the question is: who should be tested? As human contact networks are known to exhibit community structure, in this paper we show that the Kemeny constant of a graph can be used to identify and analyze bridges between communities in a graph. Our 'Kemeny indicator' is the value of the Kemeny constant in the new graph that is obtained when a node is removed from the original graph. We show that testing individuals who are associated with large values of the Kemeny indicator can help in efficiently intercepting new virus outbreaks, when they are still in their early stage. Extensive simulations provide promising results in early identification and in blocking the possible 'super-spreaders' links that transmit disease between different communities.

Q fever-the superstition of avoiding the word "quiet" as a coping mechanism: randomised controlled non-inferiority trial.

OBJECTIVE: To determine the validity of the superstition that utterance of the word "quiet" in a clinical setting increases workload. DESIGN: Prospective randomised controlled non-inferiority study. SETTING: Microbiology department of a large teaching hospital in Lancashire, UK. PARTICIPANTS: Two members of the medical microbiology team carried out the duty work on any given week day and an on-call team member on any weekend day. 29 days were assigned in which staff were to say "Today will be a quiet day" and 32 days were assigned in which staff were to refrain from saying the word "quiet" in any context. INTERVENTIONS: Each day was randomly allocated to either saying "Today will be a quiet day" (intervention group) or refraining from saying the word "quiet" (control group) in any context. MAIN OUTCOME MEASURES: The primary outcome was mean overall workload: a composite of number of clinically related telephone calls, clinically significant results, or validated results processed by the duty medical microbiology team during a 24 hour period referred to collectively as "clinical episodes." A difference of 30 clinical episodes was considered as the margin of non-inferiority. Secondary outcomes included the individual components of the primary outcome. RESULTS: Workload was measured each day over a 61 day period (1 May to 30 June 2019). A mean 139.0 clinical episodes occurred on control days compared with 144.9 on days when the experimental intervention was uttered, a difference of 5.9 (95% confidence interval-12.9 to 24.7). The upper bound was less than the specified margin of 30, providing evidence for non-inferiority. No evidence of a difference in workload was found between interventions with any of the four components, whether considering unadjusted or adjusted analyses, or looking at the subgroups of week days or weekends. CONCLUSIONS: The study findings refute the long held superstition that utterance of the word "quiet" impacts on clinical workload, and therefore it should not be avoided. In the era of considerable staff shortages and increased work related stress, doctors should look to other methods to increase resilience and protect their wellbeing and mental health. TRIAL REGISTRATION: Lancashire Teaching Hospitals NHS Foundation Trust's research department SE-259.

An unusual presentation of Legionella pneumonia in a returning traveller.

A male patient in his mid-60s presented with a severe pneumonia following return to the UK after travel to Crete. He was diagnosed with Legionnaire's disease (caused by an uncommon serogroup of Legionella pneumophila). He was pancytopenic on admission, and during a long stay on critical care he was diagnosed with a disseminated Aspergillus infection. Bone marrow aspiration revealed an underlying hairy cell leukaemia that undoubtedly contributed to his acute presentation and subsequent invasive fungal infection.

A sputum sample processing method for community and mobile tuberculosis diagnosis using the Xpert MTB/RIF assay.

The Xpert MTB/RIF assay can rapidly diagnose tuberculosis, but sputum samples cannot be safely processed unless in a lab. The septum sample pot allows safe handling of sputum and has allowed a mobile TB unit to run the assay in community settings. http://ow.ly/HOA130mS6LG.

Ebola Virus Disease: An Update on Epidemiology, Symptoms, Laboratory Findings, Diagnostic Issues, and Infection Prevention and Control Issues for Laboratory Professionals.

The 2014 to 2016 Ebola virus disease (EVD), through the sheer size of the outbreak and combined experience within both resource-rich and resource-poor settings, allowed for more information to be gained about the clinical and pathologic features of EVD. This review highlights the range of aspects of EVD that the authors find are relevant to laboratory medicine, including the need for robust prediagnostic and laboratory processing algorithms to inform sampling of suspect patients, the vast majority of whom, in resource-rich settings, will have another diagnosis.

Diagnostics in Ebola Virus Disease in Resource-Rich and Resource-Limited Settings.

The Ebola virus disease (EVD) outbreak in West Africa was unprecedented in scale and location. Limited access to both diagnostic and supportive pathology assays in both resource-rich and resource-limited settings had a detrimental effect on the identification and isolation of cases as well as individual patient management. Limited access to such assays in resource-rich settings resulted in delays in differentiating EVD from other illnesses in returning travellers, in turn utilising valuable resources until a diagnosis could be made. This had a much greater impact in West Africa, where it contributed to the initial failure to contain the outbreak. This review explores diagnostic assays of use in EVD in both resource-rich and resource-limited settings, including their respective limitations, and some novel assays and approaches that may be of use in future outbreaks.

Genetic variation in Mycobacterium tuberculosis isolates from a London outbreak associated with isoniazid resistance.

BACKGROUND: The largest outbreak of isoniazid-resistant (INH-R) Mycobacterium tuberculosis in Western Europe is centred in North London, with over 400 cases diagnosed since 1995. In the current study, we evaluated the genetic variation in a subset of clinical samples from the outbreak with the hypothesis that these isolates have unique biological characteristics that have served to prolong the outbreak. METHODS: Fitness assays, mutation rate estimation, and whole-genome sequencing were performed to test for selective advantage and compensatory mutations. RESULTS: This detailed analysis of the genetic variation of these INH-R samples suggests that this outbreak consists of successful, closely related, circulating strains with heterogeneous resistance profiles and little or no associated fitness cost or impact on their mutation rate. CONCLUSIONS: Specific deletions and SNPs could be a peculiar feature of these INH-R M. tuberculosis isolates, and could potentially explain their persistence over the years.

Therapeutic drug monitoring of antimicrobials.

As pathology services become more centralized and automated, the measurement of therapeutic antimicrobial drugs concentrations is increasingly performed in clinical biochemistry or 'blood science' laboratories. This review outlines key groups of antimicrobial agents: aminoglycosides, glycopeptides, antifungal agents and antituberculosis agents, their role in managing infectious diseases, and the reasons why serum concentration measurement is important.

Person-Specific Standardized Vulnerability Assessment in Health and Social Care.

We describe an integrated person-specific standardized vulnerability assessment model designed to facilitate patient management in health and social care. Such a system is not meant to replace existing health and social assessment models but rather to complement them by providing a holistic picture of the vulnerabilities faced by a given patient. In fact, it should be seen as a screening tool for health and social care workers. One key aspect of the modeling framework is the ability to provide personalized yet standardized multi-dimensional assessments of risk based on incomplete information about the patient status, as is the case in screening situations. Specifically, we integrate a Markov chain model describing the evolution of patients in and out of vulnerable states over time with a Bayesian network that serves to customize the dynamic model. We present an application in the context of elder care.

Rapid Whole-Genome Sequencing of Mycobacterium tuberculosis Isolates Directly from Clinical Samples.

The rapid identification of antimicrobial resistance is essential for effective treatment of highly resistant Mycobacterium tuberculosis. Whole-genome sequencing provides comprehensive data on resistance mutations and strain typing for monitoring transmission, but unlike for conventional molecular tests, this has previously been achievable only from cultures of M. tuberculosis. Here we describe a method utilizing biotinylated RNA baits designed specifically for M. tuberculosis DNA to capture full M. tuberculosis genomes directly from infected sputum samples, allowing whole-genome sequencing without the requirement of culture. This was carried out on 24 smear-positive sputum samples, collected from the United Kingdom and Lithuania where a matched culture sample was available, and 2 samples that had failed to grow in culture. M. tuberculosis sequencing data were obtained directly from all 24 smear-positive culture-positive sputa, of which 20 were of high quality (>20× depth and >90% of the genome covered). Results were compared with those of conventional molecular and culture-based methods, and high levels of concordance between phenotypical resistance and predicted resistance based on genotype were observed. High-quality sequence data were obtained from one smear-positive culture-negative case. This study demonstrated for the first time the successful and accurate sequencing of M. tuberculosis genomes directly from uncultured sputa. Identification of known resistance mutations within a week of sample receipt offers the prospect for personalized rather than empirical treatment of drug-resistant tuberculosis, including the use of antimicrobial-sparing regimens, leading to improved outcomes.