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.

Integrated analysis of patient networks and plasmid genomes reveals a regional, multi-species outbreak of carbapenemase-producing Enterobacterales carrying both blaIMP and mcr-9 genes

BACKGROUND

Carbapenemase-producing Enterobacterales (CPE) are challenging in healthcare, with resistance to multiple classes of antibiotics. This study describes the emergence of IMP-encoding CPE amongst diverse Enterobacterales species between 2016 and 2019 across a London regional network.

METHODS

We performed a network analysis of patient pathways, using electronic health records, to identify contacts between IMP-encoding CPE positive patients. Genomes of IMP-encoding CPE isolates were overlayed with patient contacts to imply potential transmission events.

RESULTS

Genomic analysis of 84 Enterobacterales isolates revealed diverse species (predominantly Klebsiella spp, Enterobacter spp, E. coli); 86% (72/84) harboured an IncHI2 plasmid carrying blaIMP and colistin resistance gene mcr-9 (68/72). Phylogenetic analysis of IncHI2 plasmids identified three lineages showing significant association with patient contacts and movements between four hospital sites and across medical specialities, which was missed on initial investigations.

CONCLUSIONS

Combined, our patient network and plasmid analyses demonstrate an interspecies, plasmid-mediated outbreak of blaIMPCPE, which remained unidentified during standard investigations. With DNA sequencing and multi-modal data incorporation, the outbreak investigation approach proposed here provides a framework for real-time identification of key factors causing pathogen spread. Plasmid-level outbreak analysis reveals that resistance spread may be wider than suspected, allowing more interventions to stop transmission within hospital networks.

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.

A Multi-Site, Multi-Wavelength PPG Platform for Continuous Non-Invasive Health Monitoring in Hospital Settings

This article presents a novel PPG acquisition platform capable of synchronous multi-wavelength signal acquisition from two measurement locations with up to 4 independent wavelengths from each in parallel. The platform is fully configurable and operates at 1ksps, accommodating a wide variety of transmitters and detectors to serve as both a research tool for experimentation and a clinical tool for disease monitoring. The sensing probes presented in this work acquire 4 PPG channels from the wrist and 4 PPG channels from the fingertip, with wavelengths such that surrogates for pulse wave velocity and haematocrit can be extracted. For conventional PPG sensing, we have achieved the mean error of 4.08 ± 3.72 bpm for heart-rate and a mean error of 1.54 ± 1.04% for SpO ₂ measurement, with the latter lying within the FDA limits for commercial pulse oximeters. We have further evaluated over 700 individual peak-to-peak time differences between wrist and fingertip signals, achieving a normalized weighted average PWV of 5.80 ± 1.58 m/s, matching with values of PWV found for this age group in literature. Lastly, we introduced and computed a haematocrit ratio ( R_hct) between the deep IR and deep red wavelength from the fingertip sensor, finding a significant difference between male and female values (median of 1.9 and 2.93 respectively) pointing to devices sensitivity to Hct.

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.

Continuous Measurement of Lactate Concentration in Human Subjects through Direct Electron Transfer from Enzymes to Microneedle Electrodes

Microneedle lactate sensors may be used to continuously measure lactate concentration in the interstitial fluid in a minimally invasive and pain-free manner. First- and second-generation enzymatic sensors produce a redox-active product that is electrochemically sensed at the electrode surface. Direct electron transfer enzymes produce electrons directly as the product of enzymatic action; in this study, a direct electron transfer enzyme specific to lactate has been immobilized onto a microneedle surface to create lactate-sensing devices that function at low applied voltages (0.2 V). These devices have been validated in a small study of human volunteers; lactate concentrations were raised and lowered through physical exercise and subsequent rest. Lactazyme microneedle devices show good agreement with concurrently obtained and analyzed serum lactate levels.

An analysis of existing national action plans for antimicrobial resistance-gaps and opportunities in strategies optimising antibiotic use in human populations

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

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.

SARS-CoV-2 surface and air contamination in an acute healthcare setting during the first and second pandemic waves

BACKGROUND

Surfaces and air in healthcare facilities can be contaminated with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Previously, the authors identified SARS-CoV-2 RNA on surfaces and air in their hospital during the first wave of the coronavirus disease 2019 pandemic (April 2020).

AIM

To explore whether the profile of SARS-CoV-2 surface and air contamination had changed between April 2020 and January 2021.

METHODS

This was a prospective, cross-sectional, observational study in a multi-site London hospital. In January 2021, surface and air samples were collected from comparable areas to those sampled in April 2020, comprising six clinical areas and a public area. SARS-CoV-2 was detected using reverse transcription polymerase chain reaction and viral culture. Sampling was also undertaken in two wards with natural ventilation alone. The ability of the prevalent variants at the time of the study to survive on dry surfaces was evaluated.

FINDINGS

No viable virus was recovered from surfaces or air. Five percent (N=14) of 270 surface samples and 4% (N=1) of 27 air samples were positive for SARS-CoV-2, which was significantly lower than in April 2020 [52% (N=114) of 218 surface samples and 48% (N=13) of 27 air samples (P<0.001, Fisher's exact test)]. There was no clear difference in the proportion of surface and air samples positive for SARS-CoV-2 RNA based on the type of ventilation in the ward. All variants tested survived on dry surfaces for >72 h, with a <3-log10 reduction in viable count.

CONCLUSION

This study suggests that enhanced infection prevention measures have reduced the burden of SARS-CoV-2 RNA on surfaces and air in healthcare facilities.

Learning meaningful latent space representations for patient risk stratification: Model development and validation for dengue and other acute febrile illness

Background

Increased data availability has prompted the creation of clinical decision support systems. These systems utilise clinical information to enhance health care provision, both to predict the likelihood of specific clinical outcomes or evaluate the risk of further complications. However, their adoption remains low due to concerns regarding the quality of recommendations, and a lack of clarity on how results are best obtained and presented.

Methods

We used autoencoders capable of reducing the dimensionality of complex datasets in order to produce a 2D representation denoted as latent space to support understanding of complex clinical data. In this output, meaningful representations of individual patient profiles are spatially mapped in an unsupervised manner according to their input clinical parameters. This technique was then applied to a large real-world clinical dataset of over 12,000 patients with an illness compatible with dengue infection in Ho Chi Minh City, Vietnam between 1999 and 2021. Dengue is a systemic viral disease which exerts significant health and economic burden worldwide, and up to 5% of hospitalised patients develop life-threatening complications.

Results

The latent space produced by the selected autoencoder aligns with established clinical characteristics exhibited by patients with dengue infection, as well as features of disease progression. Similar clinical phenotypes are represented close to each other in the latent space and clustered according to outcomes broadly described by the World Health Organisation dengue guidelines. Balancing distance metrics and density metrics produced results covering most of the latent space, and improved visualisation whilst preserving utility, with similar patients grouped closer together. In this case, this balance is achieved by using the sigmoid activation function and one hidden layer with three neurons, in addition to the latent dimension layer, which produces the output (Pearson, 0.840; Spearman, 0.830; Procrustes, 0.301; GMM 0.321).

Conclusion

This study demonstrates that when adequately configured, autoencoders can produce two-dimensional representations of a complex dataset that conserve the distance relationship between points. The output visualisation groups patients with clinically relevant features closely together and inherently supports user interpretability. Work is underway to incorporate these findings into an electronic clinical decision support system to guide individual patient management.

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.

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.

The Diagnosis of Dengue in Patients Presenting With Acute Febrile Illness Using Supervised Machine Learning and Impact of Seasonality

Background

Symptomatic dengue infection can result in a life-threatening shock syndrome and timely diagnosis is essential. Point-of-care tests for non-structural protein 1 and IgM are used widely but performance can be limited. We developed a supervised machine learning model to predict whether patients with acute febrile illnesses had a diagnosis of dengue or other febrile illnesses (OFI). The impact of seasonality on model performance over time was examined.

Methods

We analysed data from a prospective observational clinical study in Vietnam. Enrolled patients presented with an acute febrile illness of <72 h duration. A gradient boosting model (XGBoost) was used to predict final diagnosis using age, sex, haematocrit, platelet, white cell, and lymphocyte count collected on enrolment. Data was randomly split 80/20% into a training and hold-out set, respectively, with the latter not used in model development. Cross-validation and hold out set testing was used, with performance over time evaluated through a rolling window approach.

Results

We included 8,100 patients recruited between 16th October 2010 and 10th December 2014. In total 2,240 (27.7%) patients were diagnosed with dengue infection. The optimised model from training data had an overall median area under the receiver operator curve (AUROC) of 0.86 (interquartile range 0.84-0.86), specificity of 0.92, sensitivity of 0.56, positive predictive value of 0.73, negative predictive value (NPV) of 0.84, and Brier score of 0.13 in predicting the final diagnosis, with similar performances in hold-out set testing (AUROC of 0.86). Model performances varied significantly over time as a function of seasonality and other factors. Incorporation of a dynamic threshold which continuously learns from recent cases resulted in a more consistent performance throughout the year (NPV >90%).

Conclusion

Supervised machine learning models are able to discriminate between dengue and OFI diagnoses in patients presenting with an early undifferentiated febrile illness. These models could be of clinical utility in supporting healthcare decision-making and provide passive surveillance across dengue endemic regions. Effects of seasonality and changing disease prevalence must however be taken into account-this is of significant importance given unpredictable effects of human-induced climate change and the impact on health.

Applied machine learning for the risk-stratification and clinical decision support of hospitalised patients with dengue in Vietnam

BACKGROUND

Identifying patients at risk of dengue shock syndrome (DSS) is vital for effective healthcare delivery. This can be challenging in endemic settings because of high caseloads and limited resources. Machine learning models trained using clinical data could support decision-making in this context.

METHODS

We developed supervised machine learning prediction models using pooled data from adult and paediatric patients hospitalised with dengue. Individuals from 5 prospective clinical studies in Ho Chi Minh City, Vietnam conducted between 12th April 2001 and 30th January 2018 were included. The outcome was onset of dengue shock syndrome during hospitalisation. Data underwent random stratified splitting at 80:20 ratio with the former used only for model development. Ten-fold cross-validation was used for hyperparameter optimisation and confidence intervals derived from percentile bootstrapping. Optimised models were evaluated against the hold-out set.

FINDINGS

The final dataset included 4,131 patients (477 adults and 3,654 children). DSS was experienced by 222 (5.4%) of individuals. Predictors were age, sex, weight, day of illness at hospitalisation, indices of haematocrit and platelets over first 48 hours of admission and before the onset of DSS. An artificial neural network model (ANN) model had best performance with an area under receiver operator curve (AUROC) of 0.83 (95% confidence interval [CI], 0.76-0.85) in predicting DSS. When evaluated against the independent hold-out set this calibrated model exhibited an AUROC of 0.82, specificity of 0.84, sensitivity of 0.66, positive predictive value of 0.18 and negative predictive value of 0.98.

INTERPRETATION

The study demonstrates additional insights can be obtained from basic healthcare data, when applied through a machine learning framework. The high negative predictive value could support interventions such as early discharge or ambulatory patient management in this population. Work is underway to incorporate these findings into an electronic clinical decision support system to guide individual patient management.

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.

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.

Investigating Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Surface and Air Contamination in an Acute Healthcare Setting During the Peak of the Coronavirus Disease 2019 (COVID-19) Pandemic in London

BACKGROUND

We evaluated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surface and air contamination during the coronavirus disease 2019 (COVID-19) pandemic in London.

METHODS

Prospective, cross-sectional, observational study in a multisite London hospital. Air and surface samples were collected from 7 clinical areas occupied by patients with COVID-19 and a public area of the hospital. Three or four 1.0-m3 air samples were collected in each area using an active air sampler. Surface samples were collected by swabbing items in the immediate vicinity of each air sample. SARS-CoV-2 was detected using reverse-transcription quantitative polymerase chain reaction (PCR) and viral culture; the limit of detection for culturing SARS-CoV-2 from surfaces was determined.

RESULTS

Viral RNA was detected on 114 of 218 (52.3%) surfaces and in 14 of 31 (38.7%) air samples, but no virus was cultured. Viral RNA was more likely to be found in areas immediately occupied by COVID-19 patients than in other areas (67 of 105 [63.8%] vs 29 of 64 [45.3%]; odds ratio, 0.5; 95% confidence interval, 0.2-0.9; P = .025, χ2 test). The high PCR cycle threshold value for all samples (>30) indicated that the virus would not be culturable.

CONCLUSIONS

Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines the potential risk from environmental contamination in managing COVID-19 and the need for effective use of personal protective equipment, physical distancing, and hand/surface hygiene.

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.

Hospital-onset COVID-19 infection surveillance systems: a systematic review

Hospital-onset COVID-19 infections (HOCIs) are associated with excess morbidity and mortality in patients and healthcare workers. The aim of this review was to explore and describe the current literature in HOCI surveillance. Medline, EMBASE, the Cochrane Database of Systematic Reviews, the Cochrane Register of Controlled Trials, and MedRxiv were searched up to 30 November 2020 using broad search criteria. Articles of HOCI surveillance systems were included. Data describing HOCI definitions, HOCI incidence, types of HOCI identification surveillance systems, and level of system implementation were extracted. A total of 292 citations were identified. Nine studies on HOCI surveillance were included. Six studies reported on the proportion of HOCI among hospitalized COVID-19 patients, which ranged from 0 to 15.2%. Six studies provided HOCI case definitions. Standardized national definitions provided by the UK and US governments were identified. Four studies included healthcare workers in the surveillance. One study articulated a multimodal strategy of infection prevention and control practices including HOCI surveillance. All identified HOCI surveillance systems were implemented at institutional level, with eight studies focusing on all hospital inpatients and one study focusing on patients in the emergency department. Multiple types of surveillance were identified. Four studies reported automated surveillance, of which one included real-time analysis, and one included genomic data. Overall, the study quality was limited by the observational nature with short follow-up periods. In conclusion, HOCI case definitions and surveillance methods were developed pragmatically. Whilst standardized case definitions and surveillance systems are ideal for integration with existing routine surveillance activities and adoption in different settings, we acknowledged the difficulties in establishing such standards in the short-term.

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.

Macro level factors influencing strategic responses to emergent pandemics: A scoping review

BACKGROUND

Strategic planning is critical for successful pandemic management. This study aimed to identify and review the scope and analytic depth of situation analyses conducted to understand their utility, and capture the documented macro-level factors impacting pandemic management.

METHODS

To synthesise this disparate body of literature, we adopted a two-step search and review process. A systematic search of the literature was conducted to identify all studies since 2000, that have 1) employed a situation analysis; and 2) examined contextual factors influencing pandemic management. The included studies are analysed using a seven-domain systems approach from the discipline of strategic management.

RESULTS

Nineteen studies were included in the final review ranging from single country (6) to regional, multi-country studies (13). Fourteen studies had a single disease focus, with 5 studies evaluating responses to one or more of COVID-19, Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), Influenza A (H1N1), Ebola virus disease, and Zika virus disease pandemics. Six studies examined a single domain from political, economic, sociological, technological, ecological or wider industry (PESTELI), 5 studies examined two to four domains, and 8 studies examined five or more domains. Methods employed were predominantly literature reviews. The recommendations focus predominantly on addressing inhibitors in the sociological and technological domains with few recommendations articulated in the political domain. Overall, the legislative domain is least represented.

CONCLUSIONS

Ex-post analysis using the seven-domain strategic management framework provides further opportunities for a planned systematic response to pandemics which remains critical as the current COVID-19 pandemic evolves.

Macro level influences on strategic responses to the COVID-19 pandemic - an international survey and tool for national assessments

BACKGROUND

Variation in the approaches taken to contain the SARS-CoV-2 (COVID-19) pandemic at country level has been shaped by economic and political considerations, technical capacity, and assumptions about public behaviours. To address the limited application of learning from previous pandemics, this study aimed to analyse perceived facilitators and inhibitors during the pandemic and to inform the development of an assessment tool for pandemic response planning.

METHODS

A cross-sectional electronic survey of health and non-health care professionals (5 May - 5 June 2020) in six languages, with respondents recruited via email, social media and website posting. Participants were asked to score inhibitors (-10 to 0) or facilitators (0 to +10) impacting country response to COVID-19 from the following domains - Political, Economic, Sociological, Technological, Ecological, Legislative, and wider Industry (the PESTELI framework). Participants were then asked to explain their responses using free text. Descriptive and thematic analysis was followed by triangulation with the literature and expert validation to develop the assessment tool, which was then compared with four existing pandemic planning frameworks.

RESULTS

928 respondents from 66 countries (57% health care professionals) participated. Political and economic influences were consistently perceived as powerful negative forces and technology as a facilitator across high- and low-income countries. The 103-item tool developed for guiding rapid situational assessment for pandemic planning is comprehensive when compared to existing tools and highlights the interconnectedness of the 7 domains.

CONCLUSIONS

The tool developed and proposed addresses the problems associated with decision making in disciplinary silos and offers a means to refine future use of epidemic modelling.

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.

Development and Delivery of a Real-time Hospital-onset COVID-19 Surveillance System Using Network Analysis

Background

Understanding nosocomial acquisition, outbreaks, and transmission chains in real time will be fundamental to ensuring infection-prevention measures are effective in controlling coronavirus disease 2019 (COVID-19) in healthcare. We report the design and implementation of a hospital-onset COVID-19 infection (HOCI) surveillance system for an acute healthcare setting to target prevention interventions.

Methods

The study took place in a large teaching hospital group in London, United Kingdom. All patients tested for SARS-CoV-2 between 4 March and 14 April 2020 were included. Utilizing data routinely collected through electronic healthcare systems we developed a novel surveillance system for determining and reporting HOCI incidence and providing real-time network analysis. We provided daily reports on incidence and trends over time to support HOCI investigation and generated geotemporal reports using network analysis to interrogate admission pathways for common epidemiological links to infer transmission chains. By working with stakeholders the reports were co-designed for end users.

Results

Real-time surveillance reports revealed changing rates of HOCI throughout the course of the COVID-19 epidemic, key wards fueling probable transmission events, HOCIs overrepresented in particular specialties managing high-risk patients, the importance of integrating analysis of individual prior pathways, and the value of co-design in producing data visualization. Our surveillance system can effectively support national surveillance.

Conclusions

Through early analysis of the novel surveillance system we have provided a description of HOCI rates and trends over time using real-time shifting denominator data. We demonstrate the importance of including the analysis of patient pathways and networks in characterizing risk of transmission and targeting infection-control interventions.

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.

Validating a prediction tool to determine the risk of nosocomial multidrug-resistant Gram-negative bacilli infection in critically ill patients: A retrospective case-control study

BACKGROUND

The Singapore GSDCS score was developed to enable clinicians predict the risk of nosocomial multidrug-resistant Gram-negative bacilli (RGNB) infection in critically ill patients. We aimed to validate this score in a UK setting.

METHOD

A retrospective case-control study was conducted including patients who stayed for more than 24h in intensive care units (ICUs) across two tertiary National Health Service hospitals in London, UK (April 2011-April 2016). Cases with RGNB and controls with sensitive Gram-negative bacilli (SGNB) infection were identified.

RESULTS

The derived GSDCS score was calculated from when there was a step change in antimicrobial therapy in response to clinical suspicion of infection as follows: prior Gram-negative organism, Surgery, Dialysis with end-stage renal disease, prior Carbapenem use and intensive care Stay of more than 5 days. A total of 110 patients with RGNB infection (cases) were matched 1:1 to 110 geotemporally chosen patients with SGNB infection (controls). The discriminatory ability of the prediction tool by receiver operating characteristic curve analysis in our validation cohort was 0.75 (95% confidence interval 0.65-0.81), which is comparable with the area under the curve of the derivation cohort (0.77). The GSDCS score differentiated between low- (0-1.3), medium- (1.4-2.3) and high-risk (2.4-4.3) patients for RGNB infection (P<0.001) in a UK setting.

CONCLUSION

A simple bedside clinical prediction tool may be used to identify and differentiate patients at low, medium and high risk of RGNB infection prior to initiation of prompt empirical antimicrobial therapy in the intensive care setting.

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.

Continuous physiological monitoring using wearable technology to inform individual management of infectious diseases, public health and outbreak responses

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

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.

Detecting carbapenemase-producing Enterobacterales (CPE): an evaluation of an enhanced CPE infection control and screening programme in acute care

Objectives

The transmission of carbapenemase-producing Enterobacterales (CPE) poses an increasing healthcare challenge. A range of infection prevention activities, including screening and contact precautions, are recommended by international and national guidelines. We evaluated the introduction of an enhanced screening programme in a multisite London hospital group.

Methods

In June 2015, an enhanced CPE policy was launched in response to a local rise in CPE detection. This increased infection prevention measures beyond the national recommendations, with enhanced admission screening, contact tracing and environmental disinfection, improved laboratory protocols and staff/patient education. We report the CPE incidence and trends of CPE in screening and clinical cultures and the adoption of enhanced CPE screening. All non-duplicate CPE isolates identified between April 2014 and March 2018 were included.

Results

The number of CPE screens increased progressively, from 4530 in July 2015 to 10 589 in March 2018. CPE detection increased from 18 patients in July 2015 (1.0 per 1000 admissions) to 50 patients in March 2018 (2.7 per 1000 admissions). The proportion of CPE-positive screening cultures remained at approximately 0.4% throughout, suggesting that whilst the CPE carriage rate was unchanged, carrier identification increased. Also, 123 patients were identified through positive CPE clinical cultures over the study period; there was no significant change in the rate of CPE from clinical cultures per 1000 admissions (P = 0.07).

Conclusions

Our findings suggest that whilst the enhanced screening programme identified a previously undetected reservoir of CPE colonization in our patient population, the rate of detection of CPE in clinical cultures did not increase.

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.

Nurse roles in antimicrobial stewardship: lessons from public sectors models of acute care service delivery in the United Kingdom

Background

Health care services must engage all relevant healthcare workers, including nurses, in optimal antimicrobial use to address the global threat of drug-resistant infections. Reflecting upon the variety of antimicrobial stewardship (AMS) nursing models already implemented in the UK could facilitate policymaking and decisions in other settings about context-sensitive, pragmatic nurse roles.

Methods

We describe purposefully selected cases drawn from the UK network of public sector nurses in AMS exploring their characteristics, influence, relations with clinical and financial structures, and role content.

Results

AMS nursing has been deployed in the UK within 'vertical', 'horizontal' or 'hybrid' models. The 'vertical' model refers to a novel, often unique consultant-type role ideally suited to transform organisational practice by legitimising nurse participation in antimicrobial decisions. Such organisational improvements may not be straightforward, though, due to scalability issues. The 'horizontal' model can foster coordinated efforts to increase optimal AMS behaviours in all nurses around a narrative of patient safety and quality. Such model may be unable to address tensions between the required institutional response to sepsis and the inappropriate use of antibiotics. Finally, the 'hybrid' model would increase AMS responsibilities for all nurses whilst allocating some expanded AMS skills to existing teams of specialists such as sepsis or vascular access nurses. This model can generate economies of scale, yet it may be threatened by a lack of clarity about a nurse-relevant vision.

Conclusions

A variety of models articulating the participation of nurses in antimicrobial stewardship efforts have already been implemented in public sector organisations in the UK. The strengths and weaknesses of each model need considering before implementation in other settings and healthcare systems, including precise metrics of success and careful consideration of context-sensitive, resource dependent and pragmatic solutions.

Addressing the Unknowns of Antimicrobial Resistance: Quantifying and Mapping the Drivers of Burden

The global threat of antimicrobial resistance (AMR) has arisen through a network of complex interacting factors. Many different sources and transmission pathways contribute to the ever-growing burden of AMR in our clinical settings. The lack of data on these mechanisms and the relative importance of different factors causing the emergence and spread of AMR hampers our global efforts to effectively manage the risks. Importantly, we have little quantitative knowledge on the relative contributions of these sources and are likely to be targeting our interventions suboptimally as a result. Here we propose a systems mapping approach to address the urgent need for reliable and timely data to strengthen the response to AMR.

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.

Improving the estimation of the global burden of antimicrobial resistant infections

Estimating the global burden of disease from infections caused by pathogens that have acquired antimicrobial resistance (AMR) is essential for resource allocation and to inform AMR action plans at national and global levels. However, the scarcity of robust and accepted methods to determine burden is widely acknowledged. In this Personal View, we discuss the underlying assumptions, characteristics, limitations, and comparability of the approaches used to quantify mortality from AMR bacterial infections. We show that the global burdens of AMR estimated in previous studies are not comparable because of their different methodological approaches, assumptions, and data used to generate the estimates. The analytical frameworks from previous studies are inadequate, and we conclude that a new approach to the estimation of deaths caused by AMR infection is needed. The innovation of a new approach will require the development of mechanisms to systematically collect a clinical dataset of substantial breadth and quality to support the accurate assessment of burden, combined with decision-making and resource allocation for interventions against AMR. We define key actions required and call for innovative thinking and solutions to address these problems.

A multilevel neo-institutional analysis of infection prevention and control in English hospitals: coerced safety culture change?

Despite committed policy, regulative and professional efforts on healthcare safety, little is known about how such macro-interventions permeate organisations and shape culture over time. Informed by neo-institutional theory, we examined how inter-organisational influences shaped safety practices and inter-subjective meanings following efforts for coerced culture change. We traced macro-influences from 2000 to 2015 in infection prevention and control (IPC). Safety perceptions and meanings were inductively analysed from 130 in-depth qualitative interviews with senior- and middle-level managers from 30 English hospitals. A total of 869 institutional interventions were identified; 69% had a regulative component. In this context of forced implementation of safety practices, staff experienced inherent tensions concerning the scope of safety, their ability to be open and prioritisation of external mandates over local need. These tensions stemmed from conflicts among three co-existing institutional logics prevalent in the NHS. In response to requests for change, staff flexibly drew from a repertoire of cognitive, material and symbolic resources within and outside their organisations. They crafted 'strategies of action', guided by a situated assessment of first-hand practice experiences complementing collective evaluations of interventions such as 'pragmatic', 'sensible' and also 'legitimate'. Macro-institutional forces exerted influence either directly on individuals or indirectly by enriching the organisational cultural repertoire.

Articulating citizen participation in national anti-microbial resistance plans: a comparison of European countries

Background

National action plans determine country responses to anti-microbial resistance (AMR). These plans include interventions aimed at citizens. As the language used in documents could persuade certain behaviours, we sought to assess the positioning and implied responsibilities of citizens in current European AMR plans. This understanding could lead to improved policies and interventions.

Methods

Review and comparison of national action plans for AMR (NAP-AMR) obtained from the European Centre for Disease Prevention and Control (plans from 28 European Union and four European Economic Area/European Free Trade Association countries), supplemented by European experts (June-September 2016). To capture geographical diversity, 11 countries were purposively sampled for content and discourse analyses using frameworks of lay participation in healthcare organization, delivery and decision-making.

Results

Countries were at different stages of NAP-AMR development (60% completed, 25% in-process, 9% no plan). The volume allocated to citizen roles in the plans ranged from 0.3 to 18%. The term 'citizen' was used by three countries, trailing behind 'patients' and 'public' (9/11), 'general population' (6/11) and 'consumers' (6/11). Increased citizen awareness about AMR was pursued by ∼2/3 plans. Supporting interventions included awareness campaigns (11/11), training/education (7/11) or materials during clinical encounters (4/11). Prevention of infection transmission or self-care behaviours were much less emphasized. Personal/individual and social/collective role perspectives seemed more frequently stimulated in Nordic countries.

Conclusion

Citizen roles in AMR plans are not fully articulated. Documents could employ direct language to emphasise social or collective responsibilities in optimal antibiotic use.

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.

Adherence to antibiotic guidelines and reported penicillin allergy: pooled cohort data on prescribing and allergy documentation from two English National Health Service (NHS) trusts

OBJECTIVE

To investigate documentation of antimicrobial allergy and to determine prescribing adherence to local antibiotic guidelines for inpatients with and without reported penicillin allergy treated for infection in a National Health Service (NHS) context.

SETTING

Data were collected at two English hospital NHS trusts over two time-periods: June 2016 and February 2017.

DESIGN

Cohort study. Trust 1 data were sourced from prospective point prevalence surveys. Trust 2 data were extracted retrospectively from an electronic report.

PARTICIPANTS

Inpatients treated for urinary tract infection (UTI), community-acquired pneumonia (CAP), hospital-acquired pneumonia (HAP) and skin and soft tissue infection (SSTI). Data on allergy were collected, and antibiotic selection assessed for adherence to trust guidelines with differences between groups presented as adjusted ORs.

RESULTS

A total of 1497 patients were included, with 2645 antibiotics orders. Patients were treated for CAP (n=495; 33.1%), UTI (407; 27.2%), HAP (330; 22%) and SSTI (265; 17.7%). There were 240 (16%) patients with penicillin allergy. Penicillin allergy was recorded as allergy (n=52; 21.7%), side effect (27; 11.3%) and no documentation (161; 67.1%). Overall, 2184 (82.6%) antibiotic orders were guideline-adherent. Adherence was greatest for those labelled penicillin allergy (453 of 517; 87.6%) versus no allergy (1731 of 2128; 81.3%) (OR 0.52 (95% CI 0.37 to 0.73) p<0.001). Guideline-adherence for CAP was higher if penicillin allergy (151 of 163; 92.6%) versus no allergy (582 of 810; 71.9%) (OR 0.20 (95% CI 0.10 to 0.37) p<0.001). There was no difference in adherence between those with and without penicillin allergy for UTI, HAP or SSTI treatment.

CONCLUSIONS

A relatively high proportion of patients had a penicillin allergy and two thirds of these had no description of their allergy, which has important implications for patient safety. Patients with penicillin allergy treated for CAP, received more guideline adherent antibiotics than those without allergy. Future studies investigating the clinical impact of penicillin allergy should include data on adherence to antibiotic guidelines.

Antibiotic management of urinary tract infection in elderly patients in primary care and its association with bloodstream infections and all cause mortality: population based cohort study

OBJECTIVE

To evaluate the association between antibiotic treatment for urinary tract infection (UTI) and severe adverse outcomes in elderly patients in primary care.

DESIGN

Retrospective population based cohort study.

SETTING

Clinical Practice Research Datalink (2007-15) primary care records linked to hospital episode statistics and death records in England.

PARTICIPANTS

157 264 adults aged 65 years or older presenting to a general practitioner with at least one diagnosis of suspected or confirmed lower UTI from November 2007 to May 2015.

MAIN OUTCOME MEASURES

Bloodstream infection, hospital admission, and all cause mortality within 60 days after the index UTI diagnosis.

RESULTS

Among 312 896 UTI episodes (157 264 unique patients), 7.2% (n=22 534) did not have a record of antibiotics being prescribed and 6.2% (n=19 292) showed a delay in antibiotic prescribing. 1539 episodes of bloodstream infection (0.5%) were recorded within 60 days after the initial UTI. The rate of bloodstream infection was significantly higher among those patients not prescribed an antibiotic (2.9%; n=647) and those recorded as revisiting the general practitioner within seven days of the initial consultation for an antibiotic prescription compared with those given a prescription for an antibiotic at the initial consultation (2.2% v 0.2%; P=0.001). After adjustment for covariates, patients were significantly more likely to experience a bloodstream infection in the deferred antibiotics group (adjusted odds ratio 7.12, 95% confidence interval 6.22 to 8.14) and no antibiotics group (8.08, 7.12 to 9.16) compared with the immediate antibiotics group. The number needed to harm (NNH) for occurrence of bloodstream infection was lower (greater risk) for the no antibiotics group (NNH=37) than for the deferred antibiotics group (NNH=51) compared with the immediate antibiotics group. The rate of hospital admissions was about double among cases with no antibiotics (27.0%) and deferred antibiotics (26.8%) compared with those prescribed immediate antibiotics (14.8%; P=0.001). The risk of all cause mortality was significantly higher with deferred antibiotics and no antibiotics than with immediate antibiotics at any time during the 60 days follow-up (adjusted hazard ratio 1.16, 95% confidence interval 1.06 to 1.27 and 2.18, 2.04 to 2.33, respectively). Men older than 85 years were particularly at risk for both bloodstream infection and 60 day all cause mortality.

CONCLUSIONS

In elderly patients with a diagnosis of UTI in primary care, no antibiotics and deferred antibiotics were associated with a significant increase in bloodstream infection and all cause mortality compared with immediate antibiotics. In the context of an increase of Escherichia coli bloodstream infections in England, early initiation of recommended first line antibiotics for UTI in the older population is advocated.

Implementation of antibiotic stewardship in different settings - results of an international survey

Background

Antibiotic stewardship interventions are being implemented across different healthcare settings. We report the findings of a global survey of healthcare professionals on the implementation of antibiotic stewardship programmes.

Methods

Learners of a Massive Online Open Course (MOOC) on antibiotic stewardship were invited to complete an online survey on the core available organisational resources for stewardship. The categorical variables were analysed using chi-squared test, and Likert questions were analysed using an ordinal regression model. The p-values were considered as two-tailed. Significance was set at p-value of < 0.05.

Results

The response rate was 55% (505/920), from 53 countries. The responders were 36% (182) doctors, 26% (130) pharmacists, 18% (89) nurses and 20% (104) other (researchers, students and members of the public). Post-graduate training in infection management and stewardship was reported by 56% of doctors compared with 43% (OR 0.59, 95%CI 0.35–1.00) nurses and 35% (OR 0.39, 95%CI 0.24–0.62) of pharmacists. Hospitals were significantly (83% in teaching hospitals, 79% in regional hospitals, p = < 0.01) more likely to have antibiotic policies, when compared to primary care. A surveillance mechanism for antibiotic consumption was reported in 58% (104/178) of teaching hospitals and 62% (98/159) of regional hospitals. Antimicrobial resistance, patient needs, policy, peer influence and specialty level culture and practices were deemed important determinants for decision-making.

Conclusion

Postgraduate training and support in antibiotic prescribing remains low amongst nurses and pharmacists. Whilst antibiotic policies and committees are established in most institutions, surveillance of antibiotic use is not. The impact of specialty level culture, and peer influence appears to be important factors of antibiotic prescribing.