A low-volume LC/MS method for highly sensitive monitoring of phenoxymethylpenicillin, benzylpenicillin, and probenecid in human serum

Background: The optimization of antimicrobial dosing plays a crucial role in improving the likelihood of achieving therapeutic success while reducing the risks associated with toxicity and antimicrobial resistance. Probenecid has shown significant potential in enhancing the serum exposure of phenoxymethylpenicillin, thereby allowing for lower doses of phenoxymethylpenicillin to achieve similar pharmacokinetic/pharmacodynamic (PK/PD) targets. We developed a triple quadrupole liquid chromatography mass spectrometry (TQ LC/MS) analysis of, phenoxymethylpenicillin, benzylpenicillin and probenecid using benzylpenicillin-d7 and probenecid-d14 as IS in single low-volumes of human serum, with improved limit of quantification to support therapeutic drug monitoring. Methods: Sample clean-up was performed by protein precipitation using acetonitrile. Reverse phase chromatography was performed using TQ LC/MS. The mobile phase consisted of 55% methanol in water + 0.1% formic acid, with a flow rate of 0.4 mL min-1. Antibiotic stability was assessed at different temperatures. Results: Chromatographic separation was achieved within 2 minutes, allowing simultaneous measurement of phenoxymethylpenicillin, benzylpenicillin and probenecid in a single 15 μL blood sample. Validation indicated linearity over the range 0.0015-10 mg L-1, with accuracy of 96-102% and a LLOQ of 0.01 mg L-1. All drugs demonstrated good stability under different storage conditions. Conclusion: The developed method is simple, rapid, accurate and clinically applicable for the quantification of phenoxymethylpenicillin, benzylpenicillin and probenecid in tandem.

Personalising intravenous to oral antibiotic switch decision making through fair interpretable machine learning

Antimicrobial resistance (AMR) and healthcare associated infections pose a significant threat globally. One key prevention strategy is to follow antimicrobial stewardship practices, in particular, to maximise targeted oral therapy and reduce the use of indwelling vascular devices for intravenous (IV) administration. Appreciating when an individual patient can switch from IV to oral antibiotic treatment is often non-trivial and not standardised. To tackle this problem we created a machine learning model to predict when a patient could switch based on routinely collected clinical parameters. 10,362 unique intensive care unit stays were extracted and two informative feature sets identified. Our best model achieved a mean AUROC of 0.80 (SD 0.01) on the hold-out set while not being biased to individuals protected characteristics. Interpretability methodologies were employed to create clinically useful visual explanations. In summary, our model provides individualised, fair, and interpretable predictions for when a patient could switch from IV-to-oral antibiotic treatment. Prospectively evaluation of safety and efficacy is needed before such technology can be applied clinically.

Coronavirus disease 2019 (COVID-19) impact on central-line-associated bloodstream infections (CLABSI): a systematic review

Introduction

Central line-associated bloodstream infections (CLABSI) are an important clinical and public health issue, impacted by the purported increase in healthcare-associated infections (including CLABSI) during the COVID-19 pandemic. This review evaluates the impact of COVID-19 on CLABSI at a global level, to determine risk factors, effective preventive measures and microbiological epidemiology.

Methods

A systematic literature review was performed using a PECO framework, with COVID-19 infection as the exposure measure and CLABSI rates as the main outcome of interest, pre- and during the pandemic.

Results

Overall, most studies (17 of N=21) found a significant increase in CLABSI incidence/rates during the pandemic. Four studies showed a reduction (N=1) or no increase (N=3). High workload, redeployment, and 'overwhelmed' healthcare staff were recurrent risk-factor themes, likely to have negatively influenced basic infection control practices, including compliance with hand hygiene and line care bundles. Microbiological epidemiology was also impacted, with an increase in enterococcal infections and other pathogens.

Conclusion

The COVID-19 pandemic significantly impacted CLABSI incidence/rates. Observations from the different studies highlight significant gaps in healthcare associated infections (HCAI) knowledge and practice during the pandemic, and the importance of identifying preventive measures effective in reducing CLABSI, essential to health system resilience for future pandemics. Central to this are changes to CLABSI surveillance, as reporting is not mandatory in many healthcare systems. An audit tool combined with regular assessments of the compliance with infection control measures and line care bundles also remains an essential step in the prevention of CLABSI.

Rapid Diagnostic Test Value and Implementation in Antimicrobial Stewardship Across Low-to-Middle and High-Income Countries: A Mixed-Methods Review

Despite technological advancements in infectious disease rapid diagnostic tests (RDTs) and use to direct therapy at the per-patient level, RDT utilisation in antimicrobial stewardship programmes (ASPs) is variable across low-to-middle income and high-income countries. Key insights from a panel of seven infectious disease experts from Colombia, Japan, Nigeria, Thailand, the UK, and the USA, combined with evidence from a literature review, were used to assess the value of RDTs in ASPs. From this, a value framework is proposed which aims to define the benefits of RDT use in ASPs, separate from per-patient benefits. Expert insights highlight that, to realise the value of RDTs within ASPs, effective implementation is key; actionable advice for choosing an RDT is proposed. Experts advocate the inclusion of RDTs in the World Health Organization Model List of essential in vitro diagnostics and in iterative development of national action plans.

Towards optimizing carbapenem selection in stewardship strategies: a prospective propensity score-matched study of ertapenem versus class 2 carbapenems for empirical treatment of third-generation cephalosporin-resistant Enterobacterales bacteraemia

BACKGROUND

Third-generation cephalosporin-resistant Enterobacterales (3GCRE) are increasing in prevalence, leading to greater carbapenem consumption. Selecting ertapenem has been proposed as a strategy to reduce carbapenem resistance development. However, there are limited data for the efficacy of empirical ertapenem for 3GCRE bacteraemia.

OBJECTIVES

To compare the efficacy of empirical ertapenem and class 2 carbapenems for the treatment of 3GCRE bacteraemia.

METHODS

A prospective non-inferiority observational cohort study was performed from May 2019 to December 2021. Adult patients with monomicrobial 3GCRE bacteraemia receiving carbapenems within 24 h were included at two hospitals in Thailand. Propensity scores were used to control for confounding, and sensitivity analyses were performed in several subgroups. The primary outcome was 30 day mortality. This study is registered with clinicaltrials.gov (NCT03925402).

RESULTS

Empirical carbapenems were prescribed in 427/1032 (41%) patients with 3GCRE bacteraemia, of whom 221 received ertapenem and 206 received class 2 carbapenems. One-to-one propensity score matching resulted in 94 pairs. Escherichia coli was identified in 151 (80%) of cases. All patients had underlying comorbidities. Septic shock and respiratory failure were the presenting syndromes in 46 (24%) and 33 (18%) patients, respectively. The overall 30 day mortality rate was 26/188 (13.8%). Ertapenem was non-inferior to class 2 carbapenems in 30 day mortality (12.8% versus 14.9%; mean difference -0.02; 95% CI: -0.12 to 0.08). Sensitivity analyses were consistent regardless of aetiological pathogens, septic shock, source of infection, nosocomial acquisition, lactate levels or albumin levels.

CONCLUSIONS

Ertapenem may be of comparable efficacy to class 2 carbapenems in the empirical treatment of 3GCRE bacteraemia.

Can precision antibiotic prescribing help prevent the spread of carbapenem-resistant organisms in the hospital setting?

The emergence of carbapenem-resistant organisms (CROs) is a significant global threat. Reduction of carbapenem consumption can decrease CROs. In the global endemic era of ESBL-producing bacteria, carbapenems are considered the treatment of choice, leading to challenge in limiting carbapenem use. This review describes the role of precision prescribing for prevention of CROs. This involves improving antibiotic selection, dosing and shortening duration. The effect of different antibiotics, dosing and duration on CRO development are explored. Available options for precision prescribing, gaps in the scientific evidence, and areas for future research are also presented.

A rapid, simple, high-performance liquid chromatography method for the clinical measurement of beta-lactam antibiotics in serum and interstitial fluid

Background: enhanced methods of therapeutic drug monitoring are required to support the individualisation of antibiotic dosing based on pharmacokinetics (PK) parameters. PK studies can be hampered by limited total serum volume, especially in neonates, or by sensitivity in the case of critically ill patients. We aimed to develop a liquid chromatography-mass spectrometry (LC/MS) analysis of benzylpenicillin, phenoxymethylpenicillin and amoxicillin in single low volumes of human serum and interstitial fluid (ISF) samples, with an improved limit of detection (LOD) and limit of quantification (LOQ), compared with previously published assays. Methods: sample clean-up was performed by protein precipitation using acetonitrile. Reverse phase chromatography was performed using triple quadrupole LC/MS. The mobile phase consisted of 55% methanol in water + 0.1% formic acid, with a flow rate of 0.4 mL min^-1. Antibiotics stability was assessed at different temperatures. Results: chromatographic separation was achieved within 3 minutes for all analytes. Three common penicillins can now be measured in a single low-volume blood and ISF sample (15 μL) for the first time. Validation has demonstrated the method to be linear over the range 0.0015-10 mg L^-1, with an accuracy of 93-104% and high sensitivity, with LOD ≈ 0.003 mg L^-1 and LOQ ≈ 0.01 mg L^-1 for all three analytes, which is critical for use in dose optimisation/individualisation. All evaluated penicillins indicated good stability at room temperature over 4 h, at (4 °C) over 24 h and at -80 °C for 6 months. Conclusion: the developed method is simple, rapid, accurate and clinically applicable for the quantification of three penicillin classes.

Triple quadrupole LC/MS method for the simultaneous quantitative measurement of cefiderocol and meropenem in serum

Background: therapeutic drug monitoring is a crucial aspect of the management of hospitalized patients. The correct dosage of antibiotics is imperative to ensure their adequate exposure specially in critically ill patients. The aim of this study is to establish and validate a robust and fast liquid chromatography-tandem mass spectrometry (LC/MS) method for the simultaneous quantification of two important antibiotics in critically ill patients, cefiderocol and meropenem in human plasma. Methods: sample clean-up was performed by protein precipitation using acetonitrile. Reverse phase chromatography was performed using triple quadrupole LC/MS. The mobile phase was consisted of 55% methanol in water +0.1% formic acid, with flow rate of 0.4 ml min^-1. Antibiotics stability was assessed at different temperatures. Serum protein binding was assessed using ultrafiltration devices. Results: chromatographic separation was achieved within 1.5 minutes for all analytes. Validation has demonstrated the method to be linear over the range 0.0025-50 mg L^-1 for cefiderocol and 0.00028-50 mg L^-1 for meropenem, with accuracy of 94-101% and highly sensitive, with LLOQ ≈ 0.02 mg L^-1 and 0.003 mg L^-1 for cefiderocol and meropenem, respectively. Both cefiderocol and meropenem showed a good stability at room temperature over 6 h, and at (4 °C) over 24 h. Cefiderocol and meropenem demonstrated a protein binding of 49-60% and 98%, respectively in human plasma. Conclusion: the developed method is simple, rapid, accurate and clinically applicable for the quantification of cefiderocol and meropenem.

Management of Bacterial and Fungal Infections in the ICU: Diagnosis, Treatment, and Prevention Recommendations

Bacterial and fungal infections are common issues for patients in the intensive care unit (ICU). Large, multinational point prevalence surveys have identified that up to 50% of ICU patients have a diagnosis of bacterial or fungal infection at any one time. Infection in the ICU is associated with its own challenges. Causative organisms often harbour intrinsic and acquired mechanisms of drug-resistance, making empiric and targeted antimicrobial selection challenging. Infection in the ICU is associated with worse clinical outcomes for patients. We review the epidemiology of bacterial and fungal infection in the ICU. We discuss risk factors for acquisition, approaches to diagnosis and management, and common strategies for the prevention of infection.

Haematological and hepatic adverse effects of ceftriaxone in ambulatory care: a dual-centre retrospective observational analysis of standard vs high dose

BACKGROUND

European Committee on Antimicrobial Susceptibility Testing (EUCAST) breakpoint criteria for methicillin-susceptible Staphylococcus aureus (MSSA) treatment with ceftriaxone are based upon high dose (4 g/day) rather than standard dose (2 g/day) posology. This is particularly relevant for invasive infections, and for patients managed via Outpatient Parenteral Antimicrobial Therapy (OPAT), but may result in increased drug toxicity. We quantified the incidence of neutropenia, thrombocytopenia and raised liver enzymes between standard and high dose ceftriaxone in adult patients.

METHOD

Adult outpatients prescribed ≥ 7 days of ceftriaxone therapy were identified, and clinical, pharmacological, and laboratory parameters extracted from electronic health records between May 2021 and December 2021. Incidence and median time to haematological and hepto-toxicity were analysed. Univariate odds ratios were calculated for neutrophil count and ALT levels with 95% confidence level and Chi squared/Fisher's exact test used to identify statistical significance.

RESULTS

Incidence of neutropenia was comparable between both groups; 8/47 (17%) in the 2 g group vs 6/39 (15.4%) in the 4 g group (OR 0.89 (95% CI 0.26-2.63), p > 0.999). Median time to neutropenia was 12 and 17 days in the 2 g and 4 g groups respectively. Thrombocytopenia was observed in 0/47 in the 2 g group compared with 3/39 (7.7%) in the 4 g group (p 0.089). Median time to thrombocytopenia was 7 days in the 4 g group. Elevated liver enzymes did not clearly correlate with ceftriaxone dosing; present in 5/47 (10.6%) and 2/39 (5.1%) for 2 g and 4 g respectively (OR 0.45 (95% CI 0.87-2.36), p 0.448). Treatment cessation due to any adverse effect was similar between both groups 2/47 (4.3%) for 2 g and 3/39 (7.7%) for 4 g (OR 1.86 (95% CI 0.36-10.92), p 0.655).

CONCLUSIONS

Increased adverse effects with 4 g (over 2 g) daily dosing of ceftriaxone was not observed in an OPAT population. However absolute development of haematological and liver dyscrasias was appreciable-monitoring of liver function and full blood count in patients receiving prolonged ceftriaxone is indicated irrespective of dosing.

Machine learning and synthetic outcome estimation for individualised antimicrobial cessation

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

Closed-loop control of continuous piperacillin delivery: An in silico study

Background and objective: Sub-therapeutic dosing of piperacillin-tazobactam in critically-ill patients is associated with poor clinical outcomes and may promote the emergence of drug-resistant infections. In this paper, an in silico investigation of whether closed-loop control can improve pharmacokinetic-pharmacodynamic (PK-PD) target attainment is described.

Method: An in silico platform was developed using PK data from 20 critically-ill patients receiving piperacillin-tazobactam where serum and tissue interstitial fluid (ISF) PK were defined. Intra-day variability on renal clearance, ISF sensor error, and infusion constraints were taken into account. Proportional-integral-derivative (PID) control was selected for drug delivery modulation. Dose adjustment was made based on ISF sensor data with a 30-min sampling period, targeting a serum piperacillin concentration between 32 and 64 mg/L. A single tuning parameter set was employed across the virtual population. The PID controller was compared to standard therapy, including bolus and continuous infusion of piperacillin-tazobactam.

Results: Despite significant inter-subject and simulated intra-day PK variability and sensor error, PID demonstrated a significant improvement in target attainment compared to traditional bolus and continuous infusion approaches.

Conclusion: A PID controller driven by ISF drug concentration measurements has the potential to precisely deliver piperacillin-tazobactam in critically-ill patients undergoing treatment for sepsis.

Application of therapeutic drug monitoring to the treatment of bacterial central nervous system infection: a scoping review

BACKGROUND

Bacterial central nervous system (CNS) infection is challenging to treat and carries high risk of recurrence, morbidity, and mortality. Low CNS penetration of antibiotics may contribute to poor clinical outcomes from bacterial CNS infections. The current application of therapeutic drug monitoring (TDM) to management of bacterial CNS infection was reviewed.

METHODS

Studies were included if they described adults treated for a suspected/confirmed bacterial CNS infection and had antibiotic drug concentration(s) determined that affected individual treatment.

RESULTS

One-hundred-and-thirty-six citations were retrieved. Seventeen manuscripts were included describing management of 68 patients. TDM for vancomycin (58/68) and the beta-lactams (29/68) was most common. Timing of clinical sampling varied widely between studies and across different antibiotics. Methods for setting individual PK-PD targets, determining parameters and making treatment changes varied widely and were sometimes unclear.

DISCUSSION

Despite increasing observational data showing low CNS penetration of various antibiotics, there are few clinical studies describing practical implementation of TDM in management of CNS infection. Lack of consensus around clinically relevant CSF PK-PD targets and protocols for dose-adjustment may contribute. Standardised investigation of TDM as a tool to improve treatment is required, especially as innovative drug concentration-sensing and PK-PD modelling technologies are emerging. Data generated at different centres offering TDM should be open access and aggregated to enrich understanding and optimize application.

Addition of probenecid to oral β-lactam antibiotics: a systematic review and meta-analysis

OBJECTIVES

To explore the literature comparing the pharmacokinetic and clinical outcomes from adding probenecid to oral β-lactams.

METHODS

Medline and EMBASE were searched from inception to December 2021 for all English language studies comparing the addition of probenecid (intervention) with an oral β-lactam [flucloxacillin, penicillin V, amoxicillin (± clavulanate), cefalexin, cefuroxime axetil] alone (comparator). ROBINS-I and ROB-2 tools were used. Data on antibiotic therapy, infection diagnosis, primary and secondary outcomes relating to pharmacokinetics and clinical outcomes, plus adverse events were extracted and reported descriptively. For a subset of studies comparing treatment failure between probenecid and control groups, meta-analysis was performed.

RESULTS

Overall, 18/295 (6%) screened abstracts were included. Populations, methodology and outcome data were heterogeneous. Common populations included healthy volunteers (9/18; 50%) and those with gonococcal infection (6/18; 33%). Most studies were crossover trials (11/18; 61%) or parallel-arm randomized trials (4/18; 22%). Where pharmacokinetic analyses were performed, addition of probenecid to oral β-lactams increased total AUC (7/7; 100%), Cmax (5/8; 63%) and serum t½ (6/8; 75%). Probenecid improved PTA (2/2; 100%). Meta-analysis of 3105 (2258 intervention, 847 control) patients treated for gonococcal disease demonstrated a relative risk of treatment failure in the random-effects model of 0.33 (95% CI 0.20-0.55; I2 = 7%), favouring probenecid.

CONCLUSIONS

Probenecid-boosted β-lactam therapy is associated with improved outcomes in gonococcal disease. Pharmacokinetic data suggest that probenecid-boosted oral β-lactam therapy may have a broader application, but appropriately powered mechanistic and efficacy studies are required.

Antiviral metabolite 3'-deoxy-3',4'-didehydro-cytidine is detectable in serum and identifies acute viral infections including COVID-19

BACKGROUND

There is a critical need for rapid viral infection diagnostics to enable prompt case identification in pandemic settings and support targeted antimicrobial prescribing.

METHODS

Using untargeted high-resolution liquid chromatography coupled with mass spectrometry, we compared the admission serum metabolome of emergency department patients with viral infections (including COVID-19), bacterial infections, inflammatory conditions, and healthy controls. Sera from an independent cohort of emergency department patients admitted with viral or bacterial infections underwent profiling to validate findings. Associations between whole-blood gene expression and the identified metabolite of interest were examined.

FINDINGS

3'-Deoxy-3',4'-didehydro-cytidine (ddhC), a free base of the only known human antiviral small molecule ddhC-triphosphate (ddhCTP), was detected for the first time in serum. When comparing 60 viral with 101 non-viral cases in the discovery cohort, ddhC was the most significantly differentially abundant metabolite, generating an area under the receiver operating characteristic curve (AUC) of 0.954 (95% CI: 0.923-0.986). In the validation cohort, ddhC was again the most significantly differentially abundant metabolite when comparing 40 viral with 40 bacterial cases, generating an AUC of 0.81 (95% CI 0.708-0.915). Transcripts of viperin and CMPK2, enzymes responsible for ddhCTP synthesis, were among the five genes most highly correlated with ddhC abundance.

CONCLUSIONS

The antiviral precursor molecule ddhC is detectable in serum and an accurate marker for acute viral infection. Interferon-inducible genes viperin and CMPK2 are implicated in ddhC production in vivo. These findings highlight a future diagnostic role for ddhC in viral diagnosis, pandemic preparedness, and acute infection management.

FUNDING

NIHR Imperial BRC; UKRI.

Exploring the Pharmacokinetics of Phenoxymethylpenicillin (Penicillin-V) in Adults: A Healthy Volunteer Study

This healthy volunteer study aimed to explore phenoxymethylpenicillin (penicillin-V) pharmacokinetics (PK) to support the planning of large dosing studies in adults. Volunteers were dosed with penicillin-V at steady state. Total and unbound penicillin-V serum concentrations were determined, and a base population PK model was fitted to the data.

COVID-19 Prognostic Models: A Pro-con Debate for Machine Learning vs. Traditional Statistics

The SARS-CoV-2 virus, which causes the COVID-19 pandemic, has had an unprecedented impact on healthcare requiring multidisciplinary innovation and novel thinking to minimize impact and improve outcomes. Wide-ranging disciplines have collaborated including diverse clinicians (radiology, microbiology, and critical care), who are working increasingly closely with data-science. This has been leveraged through the democratization of data-science with the increasing availability of easy to access open datasets, tutorials, programming languages, and hardware which makes it significantly easier to create mathematical models. To address the COVID-19 pandemic, such data-science has enabled modeling of the impact of the virus on the population and individuals for diagnostic, prognostic, and epidemiological ends. This has led to two large systematic reviews on this topic that have highlighted the two different ways in which this feat has been attempted: one using classical statistics and the other using more novel machine learning techniques. In this review, we debate the relative strengths and weaknesses of each method toward the specific task of predicting COVID-19 outcomes.

Resistance Trend Estimation Using Regression Analysis to Enhance Antimicrobial Surveillance: A Multi-Centre Study in London 2009-2016

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.

Changing Patterns of Bloodstream Infections in the Community and Acute Care Across 2 Coronavirus Disease 2019 Epidemic Waves: A Retrospective Analysis Using Data Linkage

BACKGROUND

We examined community- and hospital-acquired bloodstream infections (BSIs) in coronavirus disease 2019 (COVID-19) and non-COVID-19 patients across 2 epidemic waves.

METHODS

We analyzed blood cultures of patients presenting to a London hospital group between January 2020 and February 2021. We reported BSI incidence, changes in sampling, case mix, healthcare capacity, and COVID-19 variants.

RESULTS

We identified 1047 BSIs from 34 044 blood cultures, including 653 (62.4%) community-acquired and 394 (37.6%) hospital-acquired. Important pattern changes were seen. Community-acquired Escherichia coli BSIs remained below prepandemic level during COVID-19 waves, but peaked following lockdown easing in May 2020, deviating from the historical trend of peaking in August. The hospital-acquired BSI rate was 100.4 per 100 000 patient-days across the pandemic, increasing to 132.3 during the first wave and 190.9 during the second, with significant increase in elective inpatients. Patients with a hospital-acquired BSI, including those without COVID-19, experienced 20.2 excess days of hospital stay and 26.7% higher mortality, higher than reported in prepandemic literature. In intensive care, the BSI rate was 421.0 per 100 000 intensive care unit patient-days during the second wave, compared to 101.3 pre-COVID-19. The BSI incidence in those infected with the severe acute respiratory syndrome coronavirus 2 Alpha variant was similar to that seen with earlier variants.

CONCLUSIONS

The pandemic have impacted the patterns of community- and hospital-acquired BSIs, in COVID-19 and non-COVID-19 patients. Factors driving the patterns are complex. Infection surveillance needs to consider key aspects of pandemic response and changes in healthcare practice.

Informing antimicrobial management in the context of COVID-19: understanding the longitudinal dynamics of C-reactive protein and procalcitonin

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 < 0.0001) and in those who died (day 8: 226 vs 152 mg/L, p < 0.0001) but only after day 7 of COVID-related symptom onset. Failure for CRP to reduce in the first week of hospital admission was associated with significantly higher odds of death. PCT concentrations were higher in patients with COVID-19 and positive microbiology or in those who died, although these differences were not statistically significant.

Conclusions

Both the absolute CRP concentration and the trajectory during the first week of hospital admission are important factors predicting microbiology culture positivity and outcome in patients hospitalised with COVID-19. Further work is needed to describe the role of PCT for co-infection. Understanding relationships of these biomarkers can support development of risk models and inform optimal antimicrobial strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06621-7.

Optimising antimicrobial use in humans - review of current evidence and an interdisciplinary consensus on key priorities for research

Addressing the silent pandemic of antimicrobial resistance (AMR) is a focus of the 2021 G7 meeting. A major driver of AMR and poor clinical outcomes is suboptimal antimicrobial use. Current research in AMR is inequitably focused on new drug development. To achieve antimicrobial security we need to balance AMR research efforts between development of new agents and strategies to preserve the efficacy and maximise effectiveness of existing agents. Combining a review of current evidence and multistage engagement with diverse international stakeholders (including those in healthcare, public health, research, patient advocacy and policy) we identified research priorities for optimising antimicrobial use in humans across four broad themes: policy and strategic planning; medicines management and prescribing systems; technology to optimise prescribing; and context, culture and behaviours. Sustainable progress depends on: developing economic and contextually appropriate interventions; facilitating better use of data and prescribing systems across healthcare settings; supporting appropriate and scalable technological innovation. Implementing this strategy for AMR research on the optimisation of antimicrobial use in humans could contribute to equitable global health security.

Bacteraemia variation during the COVID-19 pandemic; a multi-centre UK secondary care ecological analysis

Background

We investigated for change in blood stream infections (BSI) with Enterobacterales, coagulase negative staphylococci (CoNS), Streptococcus pneumoniae, and Staphylococcus aureus during the first UK wave of SARS-CoV-2 across five London hospitals.

Methods

A retrospective multicentre ecological analysis was undertaken evaluating all blood cultures taken from adults from 01 April 2017 to 30 April 2020 across five acute hospitals in London. Linear trend analysis and ARIMA models allowing for seasonality were used to look for significant variation.

Results

One hundred nineteen thousand five hundred eighty-four blood cultures were included. At the height of the UK SARS-CoV-2 first wave in April 2020, Enterobacterales bacteraemias were at an historic low across two London trusts (63/3814, 1.65%), whilst all CoNS BSI were at an historic high (173/3814, 4.25%). This differed significantly for both Enterobacterales (p = 0.013), CoNS central line associated BSIs (CLABSI) (p < 0.01) and CoNS non-CLABSI (p < 0.01), when compared with prior periods, even allowing for seasonal variation. S. pneumoniae (p = 0.631) and S. aureus (p = 0.617) BSI did not vary significant throughout the study period.

Conclusions

Significantly fewer than expected Enterobacterales BSI occurred during the UK peak of the COVID-19 pandemic; identifying potential causes, including potential unintended consequences of national self-isolation public health messaging, is essential. High rates of CoNS BSI, with evidence of increased CLABSI, but also likely contamination associated with increased use of personal protective equipment, may result in inappropriate antimicrobial use and indicates a clear area for intervention during further waves.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06159-8.

Supervised machine learning to support the diagnosis of bacterial infection in the context of COVID-19

Background

Bacterial infection has been challenging to diagnose in patients with COVID-19. We developed and evaluated supervised machine learning algorithms to support the diagnosis of secondary bacterial infection in hospitalized patients during the COVID-19 pandemic.

Methods

Inpatient data at three London hospitals for the first COVD-19 wave in March and April 2020 were extracted. Demographic, blood test and microbiology data for individuals with and without SARS-CoV-2-positive PCR were obtained. A Gaussian Naive Bayes, Support Vector Machine (SVM) and Artificial Neural Network were trained and compared using the area under the receiver operating characteristic curve (AUCROC). The best performing algorithm (SVM with 21 blood test variables) was prospectively piloted in July 2020. AUCROC was calculated for the prediction of a positive microbiological sample within 48 h of admission.

Results

A total of 15 599 daily blood profiles for 1186 individual patients were identified to train the algorithms; 771/1186 (65%) individuals were SARS-CoV-2 PCR positive. Clinically significant microbiology results were present for 166/1186 (14%) patients during admission. An SVM algorithm trained with 21 routine blood test variables and over 8000 individual profiles had the best performance. AUCROC was 0.913, sensitivity 0.801 and specificity 0.890. Prospective testing on 54 patients on admission (28/54, 52% SARS-CoV-2 PCR positive) demonstrated an AUCROC of 0.960 (95% CI: 0.90–1.00).

Conclusions

An SVM using 21 routine blood test variables had excellent performance at inferring the likelihood of positive microbiology. Further prospective evaluation of the algorithms ability to support decision making for the diagnosis of bacterial infection in COVID-19 cohorts is underway.

Bacterial and Fungal Coinfection in Individuals With Coronavirus: A Rapid Review To Support COVID-19 Antimicrobial Prescribing

BACKGROUND

To explore and describe the current literature surrounding bacterial/fungal coinfection in patients with coronavirus infection.

METHODS

MEDLINE, EMBASE, and Web of Science were searched using broad-based search criteria relating to coronavirus and bacterial coinfection. Articles presenting clinical data for patients with coronavirus infection (defined as SARS-1, MERS, SARS-CoV-2, and other coronavirus) and bacterial/fungal coinfection reported in English, Mandarin, or Italian were included. Data describing bacterial/fungal coinfections, treatments, and outcomes were extracted. Secondary analysis of studies reporting antimicrobial prescribing in SARS-CoV-2 even in absence of coinfection was performed.

RESULTS

1007 abstracts were identified. Eighteen full texts reporting bacterial/fungal coinfection were included. Most studies did not identify or report bacterial/fungal coinfection (85/140; 61%). Nine of 18 (50%) studies reported on COVID-19, 5/18 (28%) on SARS-1, 1/18 (6%) on MERS, and 3/18 (17%) on other coronaviruses. For COVID-19, 62/806 (8%) patients were reported as experiencing bacterial/fungal coinfection during hospital admission. Secondary analysis demonstrated wide use of broad-spectrum antibacterials, despite a paucity of evidence for bacterial coinfection. On secondary analysis, 1450/2010 (72%) of patients reported received antimicrobial therapy. No antimicrobial stewardship interventions were described. For non-COVID-19 cases, bacterial/fungal coinfection was reported in 89/815 (11%) of patients. Broad-spectrum antibiotic use was reported.

CONCLUSIONS

Despite frequent prescription of broad-spectrum empirical antimicrobials in patients with coronavirus-associated respiratory infections, there is a paucity of data to support the association with respiratory bacterial/fungal coinfection. Generation of prospective evidence to support development of antimicrobial policy and appropriate stewardship interventions specific for the COVID-19 pandemic is urgently required.

The Differences in Antibiotic Decision-making Between Acute Surgical and Acute Medical Teams: An Ethnographic Study of Culture and Team Dynamics

BACKGROUND

Cultural and social determinants influence antibiotic decision-making in hospitals. We investigated and compared cultural determinants of antibiotic decision-making in acute medical and surgical specialties.

METHODS

An ethnographic observational study of antibiotic decision-making in acute medical and surgical teams at a London teaching hospital was conducted (August 2015-May 2017). Data collection included 500 hours of direct observations, and face-to-face interviews with 23 key informants. A grounded theory approach, aided by Nvivo 11 software, analyzed the emerging themes. An iterative and recursive process of analysis ensured saturation of the themes. The multiple modes of enquiry enabled cross-validation and triangulation of the findings.

RESULTS

In medicine, accepted norms of the decision-making process are characterized as collectivist (input from pharmacists, infectious disease, and medical microbiology teams), rationalized, and policy-informed, with emphasis on de-escalation of therapy. The gaps in antibiotic decision-making in acute medicine occur chiefly in the transition between the emergency department and inpatient teams, where ownership of the antibiotic prescription is lost. In surgery, team priorities are split between 3 settings: operating room, outpatient clinic, and ward. Senior surgeons are often absent from the ward, leaving junior staff to make complex medical decisions. This results in defensive antibiotic decision-making, leading to prolonged and inappropriate antibiotic use.

CONCLUSIONS

In medicine, the legacy of infection diagnosis made in the emergency department determines antibiotic decision-making. In surgery, antibiotic decision-making is perceived as a nonsurgical intervention that can be delegated to junior staff or other specialties. Different, bespoke approaches to optimize antibiotic prescribing are therefore needed to address these specific challenges.

Antimicrobial use, drug-resistant infections and COVID-19

Coronavirus disease 2019 may have a complex long-term impact on antimicrobial resistance (AMR). Coordinated strategies at the individual, health-care and policy levels are urgently required to inform necessary actions to reduce the potential longer-term impact on AMR and on access to effective antimicrobials.

COVID-19 and the potential long-term impact on antimicrobial resistance

The emergence of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) has required an unprecedented response to control the spread of the infection and protect the most vulnerable within society. Whilst the pandemic has focused society on the threat of emerging infections and hand hygiene, certain infection control and antimicrobial stewardship policies may have to be relaxed. It is unclear whether the unintended consequences of these changes will have a net-positive or -negative impact on rates of antimicrobial resistance. Whilst the urgent focus must be on controlling this pandemic, sustained efforts to address the longer-term global threat of antimicrobial resistance should not be overlooked.

Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study

BACKGROUND

Infection diagnosis can be challenging, relying on clinical judgement and non-specific markers of infection. We evaluated a supervised machine learning (SML) algorithm for diagnosing bacterial infection using routinely available blood parameters on presentation to hospital.

METHODS

An SML algorithm was developed to classify cases into infection versus no infection using microbiology records and six available blood parameters (C-reactive protein, white cell count, bilirubin, creatinine, ALT and alkaline phosphatase) from 160203 individuals. A cohort of patients admitted to hospital over a 6 month period had their admission blood parameters prospectively inputted into the SML algorithm. They were prospectively followed up from admission to classify those who fulfilled clinical case criteria for a community-acquired bacterial infection within 72 h of admission using a pre-determined definition. Predictive ability was assessed using receiver operating characteristics (ROC) with cut-off values for optimal sensitivity and specificity explored.

RESULTS

One hundred and four individuals were included prospectively. The median (range) cohort age was 65 (21-98)  years. The majority were female (56/104; 54%). Thirty-six (35%) were diagnosed with infection in the first 72 h of admission. Overall, 44/104 (42%) individuals had microbiological investigations performed. Treatment was prescribed for 33/36 (92%) of infected individuals and 4/68 (6%) of those with no identifiable bacterial infection. Mean (SD) likelihood estimates for those with and without infection were significantly different. The infection group had a likelihood of 0.80 (0.09) and the non-infection group 0.50 (0.29) (P < 0.01; 95% CI: 0.20-0.40). ROC AUC was 0.84 (95% CI: 0.76-0.91).

CONCLUSIONS

An SML algorithm was able to diagnose infection in individuals presenting to hospital using routinely available blood parameters.

A Real-world Evaluation of a Case-based Reasoning Algorithm to Support Antimicrobial Prescribing Decisions in Acute Care

Background

A locally developed case-based reasoning (CBR) algorithm, designed to augment antimicrobial prescribing in secondary care was evaluated.

Methods

Prescribing recommendations made by a CBR algorithm were compared to decisions made by physicians in clinical practice. Comparisons were examined in 2 patient populations: first, in patients with confirmed Escherichia coli blood stream infections (“E. coli patients”), and second in ward-based patients presenting with a range of potential infections (“ward patients”). Prescribing recommendations were compared against the Antimicrobial Spectrum Index (ASI) and the World Health Organization Essential Medicine List Access, Watch, Reserve (AWaRe) classification system. Appropriateness of a prescription was defined as the spectrum of the prescription covering the known or most-likely organism antimicrobial sensitivity profile.

Results

In total, 224 patients (145 E. coli patients and 79 ward patients) were included. Mean (standard deviation) age was 66 (18) years with 108/224 (48%) female sex. The CBR recommendations were appropriate in 202/224 (90%) compared to 186/224 (83%) in practice (odds ratio [OR]: 1.24 95% confidence interval [CI]: .392–3.936; P = .71). CBR recommendations had a smaller ASI compared to practice with a median (range) of 6 (0–13) compared to 8 (0–12) (P &lt; .01). CBR recommendations were more likely to be classified as Access class antimicrobials compared to physicians’ prescriptions at 110/224 (49%) vs. 79/224 (35%) (OR: 1.77; 95% CI: 1.212–2.588; P &lt; .01). Results were similar for E. coli and ward patients on subgroup analysis.

Conclusions

A CBR-driven decision support system provided appropriate recommendations within a narrower spectrum compared to current clinical practice. Future work must investigate the impact of this intervention on prescribing behaviors more broadly and patient outcomes.

Machine learning for clinical decision support in infectious diseases: a narrative review of current applications

BACKGROUND

Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID).

OBJECTIVES

We aim to inform clinicians about the use of ML for diagnosis, classification, outcome prediction and antimicrobial management in ID.

SOURCES

References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, ACM Digital Library, arXiV and IEEE Xplore Digital Library up to July 2019.

CONTENT

We found 60 unique ML-clinical decision support systems (ML-CDSS) aiming to assist ID clinicians. Overall, 37 (62%) focused on bacterial infections, 10 (17%) on viral infections, nine (15%) on tuberculosis and four (7%) on any kind of infection. Among them, 20 (33%) addressed the diagnosis of infection, 18 (30%) the prediction, early detection or stratification of sepsis, 13 (22%) the prediction of treatment response, four (7%) the prediction of antibiotic resistance, three (5%) the choice of antibiotic regimen and two (3%) the choice of a combination antiretroviral therapy. The ML-CDSS were developed for intensive care units (n = 24, 40%), ID consultation (n = 15, 25%), medical or surgical wards (n = 13, 20%), emergency department (n = 4, 7%), primary care (n = 3, 5%) and antimicrobial stewardship (n = 1, 2%). Fifty-three ML-CDSS (88%) were developed using data from high-income countries and seven (12%) with data from low- and middle-income countries (LMIC). The evaluation of ML-CDSS was limited to measures of performance (e.g. sensitivity, specificity) for 57 ML-CDSS (95%) and included data in clinical practice for three (5%).

IMPLICATIONS

Considering comprehensive patient data from socioeconomically diverse healthcare settings, including primary care and LMICs, may improve the ability of ML-CDSS to suggest decisions adapted to various clinical contexts. Currents gaps identified in the evaluation of ML-CDSS must also be addressed in order to know the potential impact of such tools for clinicians and patients.

Antibiotic prescribing in general medical and surgical specialties: a prospective cohort study

Background

Qualitative work has described the differences in prescribing practice across medical and surgical specialties. This study aimed to understand if specialty impacts quantitative measures of prescribing practice.

Methods

We prospectively analysed the antibiotic prescribing across general medical and surgical teams for acutely admitted patients. Over a 12-month period (June 2016 – May 2017) 659 patients (362 medical, 297 surgical) were followed for the duration of their hospital stay. Antibiotic prescribing across these cohorts was assessed using Chi-squared or Wilcoxon rank-sum, depending on normality of data. The t-test was used to compare age and length of stay. A logistic regression model was used to predict escalation of antibiotic therapy.

Results

Surgical patients were younger (p < 0.001) with lower Charlson Comorbidity Index scores (p < 0.001). Antibiotics were prescribed for 45% (162/362) medical and 55% (164/297) surgical patients. Microbiological results were available for 26% (42/164) medical and 29% (48/162) surgical patients, of which 55% (23/42) and 48% (23/48) were positive respectively. There was no difference in the spectrum of antibiotics prescribed between surgery and medicine (p = 0.507). In surgery antibiotics were 1) prescribed more frequently (p = 0.001); 2) for longer (p = 0.016); 3) more likely to be escalated (p = 0.004); 4) less likely to be compliant with local policy (p < 0.001) than medicine.

Conclusions

Across both specialties, microbiology investigation results are not adequately used to diagnose infections and optimise their management. There is significant variation in antibiotic decision-making (including escalation patterns) between general surgical and medical teams. Antibiotic stewardship interventions targeting surgical specialties need to go beyond surgical prophylaxis. It is critical to focus on of review the patients initiated on therapeutic antibiotics in surgical specialties to ensure that escalation and continuation of therapy is justified.

Antibacterial resistance in ophthalmic infections: a multi-centre analysis across UK care settings

BACKGROUND

Bacterial ophthalmic infections are common. Empirical treatment with topical broad-spectrum antibiotics is recommended for severe cases. Antimicrobial resistance (AMR) to agents used for bacterial ophthalmic infections make it increasingly important to consider changing resistance patterns when prescribing, however UK data in this area are lacking. We evaluate the epidemiology and antimicrobial susceptibilities of ophthalmic pathogens across care settings and compare these with local and national antimicrobial prescribing guidelines.

METHODS

A retrospective, multi-centre observational analysis was undertaken of ophthalmic microbiology isolates between 2009 and 2015 at a centralised North-West London laboratory (incorporating data from primary care and five London teaching hospitals). Data were analysed using descriptive statistics with respect to patient demographics, pathogen distribution (across age-groups and care setting), seasonality, and susceptibility to topical chloramphenicol, moxifloxacin, and fusidic acid.

RESULTS

Two thousand six hundred eighty-one isolates (n = 2168 patients) were identified. The commonest pathogen in adults was Staphylococcus spp. across primary, secondary, and tertiary care (51.7%; 43.4%; 33.6% respectively) and in children was Haemophilus spp. (34.6%;28.2%;36.6%). AMR was high and increased across care settings for chloramphenicol (11.8%;15.1%;33.8%); moxifloxacin (5.5%;7.6%;25.5%); and fusidic acid (49.6%;53.4%; 58.7%). Pseudomonas spp. was the commonest chloramphenicol-resistant pathogen across all care settings, whilst Haemophilus spp. was the commonest fusidic acid-resistant pathogen across primary and secondary care. More isolates were recorded in spring (31.6%) than any other season, mostly due to a significant rise in Haemophilus spp.

CONCLUSIONS

We find UK national and local antimicrobial prescribing policies for ophthalmic infections may not be concordant with the organisms and antimicrobial susceptibilities found in clinical samples. We also find variations in microbial incidence related to patient age, clinical setting, and season. Such variations may have further important implications for prescribing practices and modification of antimicrobial guidelines.

Development of a Minimally Invasive Microneedle-Based Sensor for Continuous Monitoring of β-Lactam Antibiotic Concentrations in Vivo

Antimicrobial resistance poses a global threat to patient health. Improving the use and effectiveness of antimicrobials is critical in addressing this issue. This includes optimizing the dose of antibiotic delivered to each individual. New sensing approaches that track antimicrobial concentration for each patient in real time could allow individualized drug dosing. This work presents a potentiometric microneedle-based biosensor to detect levels of β-lactam antibiotics in vivo in a healthy human volunteer. The biosensor is coated with a pH-sensitive iridium oxide layer, which detects changes in local pH as a result of β-lactam hydrolysis by β-lactamase immobilized on the electrode surface. Development and optimization of the biosensor coatings are presented, giving a limit of detection of 6.8 μM in 10 mM PBS solution. Biosensors were found to be stable for up to 2 weeks at -20 °C and to withstand sterilization. Sensitivity was retained after application for 6 h in vivo. Proof-of-concept results are presented showing that penicillin concentrations measured using the microneedle-based biosensor track those measured using both discrete blood and microdialysis sampling in vivo. These preliminary results show the potential of this microneedle-based biosensor to provide a minimally invasive means to measure real-time β-lactam concentrations in vivo, representing an important first step toward a closed-loop therapeutic drug monitoring system.

Surveillance for Azole-Resistant Aspergillus fumigatus in a Centralized Diagnostic Mycology Service, London, United Kingdom, 1998-2017

Background/Objectives: Aspergillus fumigatus is the leading cause of invasive aspergillosis. Treatment is hindered by the emergence of resistance to triazole antimycotic agents. Here, we present the prevalence of triazole resistance among clinical isolates at a major centralized medical mycology laboratory in London, United Kingdom, in the period 1998-2017. Methods: A large number (n = 1469) of clinical A. fumigatus isolates from unselected clinical specimens were identified and their susceptibility against three triazoles, amphotericin B and three echinocandin agents was carried out. All isolates were identified phenotypically and antifungal susceptibility testing was carried out by using a standard broth microdilution method. Results: Retrospective surveillance (1998-2011) shows 5/1151 (0.43%) isolates were resistant to at least one of the clinically used triazole antifungal agents. Prospective surveillance (2015-2017) shows 7/356 (2.2%) isolates were resistant to at least one triazole antifungals demonstrating an increase in incidence of triazole-resistant A. fumigatus in our laboratory. Among five isolates collected from 2015 to 2017 and available for molecular testing, three harbored TR34/L98H alteration in the cyp51A gene that are associated with the acquisition of resistance in the non-patient environment. Conclusion: These data show that historically low prevalence of azole resistance may be increasing, warranting further surveillance of susceptible patients.

Investigating the impact of poverty on colonization and infection with drug-resistant organisms in humans: a systematic review

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.

Public acceptability of computer-controlled antibiotic management: An exploration of automated dosing and opportunities for implementation

A paucity of data describing citizen perceptions of novel technologies, including those containing unsupervised computer-controlled systems is currently available. We explored citizen perceptions of using a microneedle biosensor and automated dose control system at a university public festival. Groups of citizens (from 2-6 people per group) attended a short demonstration of a microneedle biosensor and automated dosing system versus a traditional phlebotomy approach over a two-day public festival. Individual groups discussed and reached consensus on a number of short questions regarding their perceptions on the acceptability of such technology. Over the two days, 100 groups participated (56/100 day 1 and 44/100 day 2). The majority of individuals reported high acceptability of microneedle technology (median Likert score 9/10), but the majority believed that doctors should decide what dose of antibiotic is delivered (75/100; 75%). Groups concurred with the acceptability of microneedles to reduce blood tests and pain associated with them. However, concerns were reported over unsupervised computer-controlled programmes making decision about antibiotic dosing. This was driven by concerns over computer error and the inability of systems to contextualise decision making to the human and social context. Future work must consider the greater role of citizen engagement in the development of such technologies, to ensure their acceptability upon implementation in clinical practice.

Development of a web-based tool for undergraduate engagement in medical research; the ProjectPal experience

BACKGROUND

We report the development and evaluation of a web-based tool designed to facilitate student extra-curricular engagement in medical research through project matching students with academic supervisors. UK based university students were surveyed to explore their perceptions of undergraduate research, barriers and facilitators to current engagement. Following this, an online web-based intervention ( www.ProjectPal.org ) was developed to support access of students to research projects and supervisors. A pilot intervention was undertaken across a London-based university in January 2013 to February 2016. In March 2016, anonymised data were extracted from the prospective data log for analysis of website engagement and usage. Supervisors were surveyed to evaluate the website and student outputs.

RESULTS

Fifty-one students responded to the electronic survey. Twenty-four (47%) reported frustration at a perceived lack of opportunities to carry out extra-curricular academic projects. Major barriers to engaging in undergraduate research reported were difficulties in identifying suitable supervisors (33/51; 65%) and time pressures (36/51; 71%) associated with this. Students reported being opportunistic in their engagement with undergraduate research. Following implementation of the website, 438 students signed up to ProjectPal and the website was accessed 1357 times. Access increased on a yearly basis. Overall, 70 projects were advertised by 35 supervisors. There were 86 applications made by students for these projects. By February 2016, the 70 projects had generated 5 peer-review publications with a further 7 manuscripts under peer-review, 14 national presentations, and 1 national prize.

CONCLUSION

The use of an online platform to promote undergraduate engagement with extra-curricular research appears to facilitate extra-curricular engagement with research. Further work to understand the impact compared to normal opportunistic practices in enhancing student engagement is now underway.

A summary and appraisal of existing evidence of antimicrobial resistance in the Syrian conflict

Antimicrobial resistance (AMR) in populations experiencing war has yet to be addressed, despite the abundance of contemporary conflicts and the protracted nature of twenty-first century wars, in combination with growing global concern over conflict-associated bacterial pathogens. The example of the Syrian conflict is used to explore the feasibility of using existing global policies on AMR in conditions of extreme conflict. The available literature on AMR and prescribing behaviour in Syria before and since the onset of the conflict in March 2011 was identified. Overall, there is a paucity of rigorous data before and since the onset of conflict in Syria to contextualize the burden of AMR. However, post onset of the conflict, an increasing number of studies conducted in neighbouring countries and Europe have reported AMR in Syrian refugees. High rates of multidrug resistance, particularly Gram-negative organisms, have been noted amongst Syrian refugees when compared with local populations. Conflict impedes many of the safeguards against AMR, creates new drivers, and exacerbates existing ones. Given the apparently high rates of AMR in Syria, in neighbouring countries hosting refugees, and in European countries providing asylum, this requires the World Health Organization and other global health institutions to address the causes, costs, and future considerations of conflict-related AMR as an issue of global governance.

Exploring the Use of C-Reactive Protein to Estimate the Pharmacodynamics of Vancomycin

BACKGROUND

C-reactive protein (CRP) pharmacodynamic (PD) models have the potential to provide adjunctive methods for predicting the individual exposure response to antimicrobial therapy. We investigated CRP PD linked to a vancomycin pharmacokinetic (PK) model using routinely collected data from noncritical care adults in secondary care.

METHODS

Patients receiving intermittent intravenous vancomycin therapy in secondary care were identified. A 2-compartment vancomycin PK model was linked to a previously described PD model describing CRP response. PK and PD parameters were estimated using a Non-Parametric Adaptive Grid technique. Exposure-response relationships were explored with vancomycin area-under-the-concentration-time-curve (AUC) and EC50 (concentration of drug that causes a half maximal effect) using the index, AUC:EC50, fitted to CRP data using a sigmoidal Emax model.

RESULTS

Twenty-nine individuals were included. Median age was 62 (21-97) years. Fifteen (52%) patients were microbiology confirmed. PK and PD models were adequately fitted (r 0.83 and 0.82, respectively). There was a wide variation observed in individual Bayesian posterior EC50 estimates (6.95-48.55 mg/L), with mean (SD) AUC:EC50 of 31.46 (29.22). AUC:EC50 was fitted to terminal CRP with AUC:EC50 >19 associated with lower CRP value at 96-120 hours of therapy (100 mg/L versus 44 mg/L; P < 0.01).

CONCLUSIONS

The use of AUC:EC50 has the potential to provide in vivo organism and host response data as an adjunct for in vitro minimum inhibitory concentration data, which is currently used as the gold standard PD index for vancomycin therapy. This index can be estimated using routinely collected clinical data. Future work must investigate the role of AUC:EC50 in a prospective cohort and explore linkage with direct patient outcomes.

Antimicrobial resistance in the context of the Syrian conflict: Drivers before and after the onset of conflict and key recommendations

Current evidence describing antimicrobial resistance (AMR) in the context of the Syrian conflict is of poor quality and sparse in nature. This paper explores and reports the major drivers of AMR that were present in Syria pre-conflict and those that have emerged since its onset in March 2011. Drivers that existed before the conflict included a lack of enforcement of existing legislation to regulate over-the-counter antibiotics and notification of communicable diseases. This contributed to a number of drivers of AMR after the onset of conflict, and these were also compounded by the exodus of trained staff, the increase in overcrowding and unsanitary conditions, the increase in injuries, and economic sanctions limiting the availability of required laboratory medical materials and equipment. Addressing AMR in this context requires pragmatic, multifaceted action at the local, regional, and international levels to detect and manage potentially high rates of multidrug-resistant infections. Priorities are (1) the development of a competent surveillance system for hospital-acquired infections, (2) antimicrobial stewardship, and (3) the creation of cost-effective and implementable infection control policies. However, it is only by addressing the conflict and immediate cessation of the targeting of health facilities that the rehabilitation of the health system, which is key to addressing AMR in this context, can progress.