Imperial College London

DrTimothy MilesRawson

Faculty of MedicineDepartment of Infectious Disease

Honorary Clinical Research Fellow
 
 
 
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Contact

 

timothy.rawson07

 
 
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Commonwealth BuildingHammersmith Campus

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Summary

 

Publications

Publication Type
Year
to

86 results found

Denny S, Rawson T, Hart P, Satta G, Pallett S, Abdulaal A, Hughes S, Gilchrist M, Mughal N, Moore Let al., 2021, Bacteraemia variation during the COVID-19 pandemic; a multi-centre UK secondary care ecological analysis, BMC Infectious Diseases, ISSN: 1471-2334

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 –119,584 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.

Journal article

Rawson TM, Hernandez B, Wilson R, Wilson R, Ming D, Herrero P, Ranganathan N, Skolimowska K, Gilchrist M, Satta G, Georgiou P, Holmes Aet al., 2021, Supervised machine learning to support the diagnosis of bacterial infection in the context of COVID-19, JAC-Antimicrobial Resistance, Vol: 3, Pages: 1-4, ISSN: 2632-1823

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 COVID-19.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-Naïve Bayes (GNB), Support Vector Machine (SVM), and Artificial Neuronal Network (ANN) 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 hours of admission. Results: A total of 15,599 daily blood profiles for 1,186 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. A 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 (0.90-1.00). Conclusion: A 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.

Journal article

Zhu N, Aylin P, Rawson T, Gilchrist M, Majeed A, Holmes Aet al., 2021, Investigating the impact of COVID-19 on primary care antibiotic prescribing in North West London across two epidemic waves., Clin Microbiol Infect

OBJECTIVES: We investigated the impact of COVID-19 and national pandemic response on primary care antibiotic prescribing in London. METHODS: Individual prescribing records between 2015 and 2020 for 2 million residents in north west London were analysed. Prescribing records were linked to SARS-CoV-2 test results. Prescribing volumes, in total, and stratified by patient characteristics, antibiotic class and AWaRe classification, were investigated. Interrupted time series analysis was performed to detect measurable change in the trend of prescribing volume since the national lockdown in March 2020, immediately before the first COVID-19 peak in London. RESULTS: Records covering 366 059 patients, 730 001 antibiotic items and 848 201 SARS-CoV-2 tests between January and November 2020 were analysed. Before March 2020, there was a background downward trend (decreasing by 584 items/month) in primary care antibiotic prescribing. This reduction rate accelerated to 3504 items/month from March 2020. This rate of decrease was sustained beyond the initial peak, continuing into winter and the second peak. Despite an overall reduction in prescribing volume, co-amoxiclav, a broad-spectrum "Access" antibiotic, prescribing rose by 70.1% in patients aged 50 and older from February to April. Commonly prescribed antibiotics within 14 days of a positive SARS-CoV-2 test were amoxicillin (863/2474, 34.9%) and doxycycline (678/2474, 27.4%). This aligned with national guidelines on management of community pneumonia of unclear cause. The proportion of "Watch" antibiotics used decreased during the peak in COVID-19. DISCUSSION: A sustained reduction in community antibiotic prescribing has been observed since the first lockdown. Investigation of community-onset infectious diseases and potential unintended consequences of reduced prescribing is urgently needed.

Journal article

Denny S, Rawson TM, Satta G, Pallett SJC, Abdulaal A, Hughes S, Gilchrist M, Mughal N, Moore LSPet al., 2020, Bacteraemia Variation During The COVID-19 Pandemic; A Multi-Centre UK Secondary Care Ecological Analysis., Publisher: Research Square

<jats:title>Abstract</jats:title> <jats:p><jats:bold><jats:italic>Objectives </jats:italic></jats:bold>– We investigated for change in blood stream infections (BSI) with Enterobacterales, coagulase negative staphylococci (CoNS),<jats:italic> Streptococcus pneumoniae,</jats:italic> and <jats:italic>Staphylococcus aureus</jats:italic> during the first UK wave of SARS-CoV-2 across six London hospitals.<jats:bold><jats:italic>Methods</jats:italic> </jats:bold>– A retrospective multicentre ecological analysis was undertaken evaluating all blood cultures taken from adults from 01 April 2017 to 30 April 2020 across six acute hospitals in London. Linear trend analysis and ARIMA models allowing for seasonality were used to look for significant variation.<jats:bold><jats:italic>Results</jats:italic> </jats:bold>–119,584 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 CoNS were at an historic high (173/3814, 4.25%). This differed significantly for both Enterobacterales (p=0.013) and CoNS (p&lt;0.01), when compared with prior periods, even allowing for seasonal variation. <jats:italic>S. pneumoniae </jats:italic>(p=0.631)<jats:italic> </jats:italic>and <jats:italic>S. aureus </jats:italic>(p=0.617) BSI did not vary significant throughout the study period.<jats:bold><jats:italic>Conclusions</jats:italic></jats:bold> – Significantly fewer than expected Enterobacteriales 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, presumably representing contamination assoc

Working paper

Rawson TM, Wilson R, Holmes A, 2020, Understanding the role of bacterial and fungal infection in COVID-19, Clinical Microbiology and Infection, ISSN: 1198-743X

Journal article

Peiffer-Smadja N, Rawson TM, Ahmad R, Buchard A, Georgiou P, Lescure F-X, Birgand G, Holmes AHet al., 2020, machine learning for clinical decision support in infectious diseases: a narrative review of current applications (vol 26, pg 584, 2020), CLINICAL MICROBIOLOGY AND INFECTION, Vol: 26, Pages: 1118-1118, ISSN: 1198-743X

Journal article

Rawson TM, Moore L, Castro Sanchez E, Charani E, Davies F, Satta G, Ellington M, Holmes Aet al., 2020, COVID-19 and the potential long term impact on antimicrobial resistance, Journal of Antimicrobial Chemotherapy, Vol: 75, Pages: 1681-1684, ISSN: 0305-7453

The emergence of the SARS-CoV-2 respiratory virus 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 allaying this pandemic, sustained efforts to address the longer-term global threat of antimicrobial resistance should not be overlooked.

Journal article

Rawson TM, Moore LSP, Zhu N, Ranganathan N, Skolimowska K, Gilchrist M, Satta G, Cooke G, Holmes Aet al., 2020, Response to Dudoignon et al., Clin Infect Dis

Journal article

Rawson TM, Ming D, Ahmad R, Moore LSP, Holmes AHet al., 2020, Antimicrobial use, drug-resistant infections and COVID-19., Nature Reviews Microbiology, ISSN: 1740-1526

Journal article

Rawson TM, Moore L, Zhu N, Ranganathan N, Skolimowska K, Gilchrist M, Satta G, Cooke G, Holmes Aet al., 2020, Bacterial and fungal co-infection in individuals with coronavirus: A rapid review to support COVID-19 antimicrobial prescribing, Clinical Infectious Diseases, Vol: 71, Pages: 2459-2468, ISSN: 1058-4838

BackgroundTo explore and describe the current literature surrounding bacterial/fungal co-infection in patients with coronavirus infection.MethodsMEDLINE, EMBASE, and Web of Science were searched using broad based search criteria relating to coronavirus and bacterial co-infection. Articles presenting clinical data for patients with coronavirus infection (defined as SARS-1, MERS, SARS-COV-2, and other coronavirus) and bacterial/fungal co-infection reported in English, Mandarin, or Italian were included. Data describing bacterial/fungal co-infections, treatments, and outcomes were extracted. Secondary analysis of studies reporting antimicrobial prescribing in SARS-COV-2 even in the absence of co-infection was performed.Results1007 abstracts were identified. Eighteen full texts reported bacterial/fungal co-infection were included. Most studies did not identify or report bacterial/fungal coinfection (85/140;61%). 9/18 (50%) studies reported on COVID-19, 5/18 (28%) SARS-1, 1/18 (6%) MERS, and 3/18 (17%) other coronavirus.For COVID-19, 62/806 (8%) patients were reported as experiencing bacterial/fungal co-infection 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 co-infection was reported in 89/815 (11%) of patients. Broad-spectrum antibiotic use was reported.ConclusionsDespite 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 co-infection. Generation of prospective evidence to support development of antimicrobial policy and appropriate stewardship interventions specific for the COVID-19 pandemic are urgently requi

Journal article

Peiffer-Smadja N, Rawson TM, Ahmad R, Buchard A, Pantelis G, Lescure F-X, Birgand G, Holmes Aet al., 2020, Machine learning for clinical decision support in infectious diseases: A narrative review of current applications, Clinical Microbiology and Infection, Vol: 26, Pages: 584-595, ISSN: 1198-743X

BACKGROUNDMachine 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). OBJECTIVESWe aim to inform clinicians about the use of ML for diagnosis, classification, outcome prediction and antimicrobial management in ID.SOURCESReferences 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.CONTENTWe found 60 unique 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%). IMPLICATIONSConsidering comprehensive patient data from socioeconomically diverse health care 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 cli

Journal article

Rawson TM, Hernandez B, Moore L, Herrero P, Charani E, Ming D, Wilson R, Blandy O, Sriskandan S, Toumazou C, Georgiou P, Holmes Aet al., 2020, A real-world evaluation of a Case-Based Reasoning algorithm to support antimicrobial prescribing decisions in acute care, Clinical Infectious Diseases, ISSN: 1058-4838

BackgroundA locally developed Case-Based Reasoning (CBR) algorithm, designed to augment antimicrobial prescribing in secondary care was evaluated.MethodsPrescribing recommendations made by a CBR algorithm were compared to decisions made by physicians in clinical practice. Comparisons were examined in two patient populations. Firstly, in patients with confirmed Escherichia coli blood stream infections (‘E.coli patients’), and secondly 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 WHO 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.ResultsIn total, 224 patients (145 E.coli patients and 79 ward patients) were included. Mean (SD) age was 66 (18) years with 108/224 (48%) female gender. The CBR recommendations were appropriate in 202/224 (90%) compared to 186/224 (83%) in practice (OR: 1.24 95%CI:0.392-3.936;p=0.71). CBR recommendations had a smaller ASI compared to practice with a median (range) of 6 (0-13) compared to 8 (0-12) (p<0.01). 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<0.01). Results were similar for E.coli and ward patients on subgroup analysis.ConclusionsA 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 behaviours more broadly and patient outcomes.

Journal article

Rawson TM, Gowers SAN, Freeman DME, Wilson RC, Sharma S, Gilchrist M, MacGowan A, Lovering A, Bayliss M, Kyriakides M, Georgiou P, Cass AEG, O'Hare D, Holmes AHet al., 2019, Microneedle biosensors for real-time, minimally invasive drug monitoring of phenoxymethylpenicillin: a first-in-human evaluation in healthy volunteers, The Lancet Digital Health, Vol: 1, Pages: e335-e343, ISSN: 2589-7500

Background: Enhanced methods of drug monitoring are required to support the individualisation of antibiotic dosing. We report the first-in-human evaluation of real-time phenoxymethylpenicillin monitoring using a minimally invasive microneedle-based β-lactam biosensor in healthy volunteers.Methods: This first-in-human, proof-of-concept study was done at the National Institute of Health Research/Wellcome Trust Imperial Clinical Research Facility (Imperial College London, London, UK). The study was approved by London-Harrow Regional Ethics Committee. Volunteers were identified through emails sent to a healthy volunteer database from the Imperial College Clinical Research Facility. Volunteers, who had to be older than 18 years, were excluded if they had evidence of active infection, allergies to penicillin, were at high risk of skin infection, or presented with anaemia during screening. Participants wore a solid microneedle β-lactam biosensor for up to 6 h while being dosed at steady state with oral phenoxymethylpenicillin (five 500 mg doses every 6 h). On arrival at the study centre, two microneedle sensors were applied to the participant's forearm. Blood samples (via cannula, at −30, 0, 10, 20, 30, 45, 60, 90, 120, 150, 180, 210, 240 min) and extracellular fluid (ECF; via microdialysis, every 15 min) pharmacokinetic (PK) samples were taken during one dosing interval. Phenoxymethylpenicillin concentration data obtained from the microneedles were calibrated using locally estimated scatter plot smoothing and compared with free-blood and microdialysis (gold standard) data. Phenoxymethylpenicillin PK for each method was evaluated using non-compartmental analysis. Area under the concentration–time curve (AUC), maximum concentration, and time to maximum concentration were compared. Bias and limits of agreement were investigated with Bland–Altman plots. Microneedle biosensor limits of detection were estimated. The study was registered with Clinical

Journal article

Charani E, DeBarra E, Gill D, Rawson T, Gilchrist M, Naylor N, Holmes Aet al., 2019, Antibiotic prescribing in general medical and surgical specialties: a prospective cohort study, Antimicrobial Resistance and Infection Control, Vol: 8, Pages: 1-10, ISSN: 2047-2994

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 c

Journal article

Lee A, Niruttan K, Rawson T, Moore Let al., 2019, Antibacterial resistance in ophthalmic infections: a multi-centre analysis across UK care settings, BMC Infectious Diseases, Vol: 19, Pages: 1-8, ISSN: 1471-2334

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 2009-2015 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: 2681 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 v

Journal article

Charani E, Ahmad R, Rawson T, Castro-Sanchez E, Tarrant C, Holmes Aet al., 2019, The differences in antibiotic decision-making between acute surgical and acute medical teams: An ethnographic study of culture and team dynamics, Clinical Infectious Diseases, Vol: 69, Pages: 12-20, ISSN: 1058-4838

BackgroundCultural 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.MethodsAn 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.ResultsIn 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.ConclusionsIn 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.

Journal article

Castro-Sánchez E, Sood A, Rawson TM, Firth J, Holmes AHet al., 2019, Forecasting Implementation, Adoption and Evaluation Challenges For an Electronic Game-Based Antimicrobial Stewardship Intervention: Results of a Codesign Workshop with Experts (Preprint), Journal of Medical Internet Research, Vol: 21, ISSN: 1438-8871

Background:Serious games have been proposed to address the lack of engagement and sustainability traditionally affecting interventions aiming to improve optimal antibiotic use among hospital prescribers.Objectives:To forecast gaps in implementation, adoption and evaluation of game-based interventions, and co-design solutions with antimicrobial clinicians and digital and behavioural researchers. Methods: A co-development workshop with clinicians and academics in serious games, antimicrobials and behavioural sciences was organised to open an international summit on serious games for health in London (United Kingdom), in March 2018. The workshop was announced on social media and online platforms. On the day, attendees were asked to work in small groups provided with a laptop/tablet with the latest version of ‘On call: Antibiotics. A workshop leader guided open group discussions around implementation, adoption and evaluation threats and potential solutions. Workshop summary notes were collated by an observer.Results: 29 participants attended the workshop. Anticipated challenges to resolve reflected implementation threats such as an inadequate organisational arrangement to scale and sustain the use of the game, requiring sufficient technical and educational support and a streamlined feedback mechanism that made best use of data arriving from the game; adoption threats, particularly collective perceptions that a game would be a ludic rather than professional tool, and demanding efforts to integrate all available educational solutions so none is seen as inferior; and evaluation threats due to the need to combine game metrics with organisational indicators such as antibiotic use, which may be difficult to enable.Conclusions:As with other technology-based interventions, organisations interested in deploying game-based solutions should carefully plan how to engage and support clinicians in their use, and how best integrate the game and game outputs onto existing workflo

Journal article

Gowers SAN, Freeman DME, Rawson TM, Rogers ML, Wilson RC, Holmes AH, Cass AE, O'Hare Det al., 2019, Development of a minimally invasive microneedle-based sensor for continuous monitoring of β-lactam antibiotic concentrations in vivo, ACS sensors, Vol: 4, Pages: 1072-1080, ISSN: 2379-3694

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.

Journal article

Rawson TM, Hernandez B, Moore L, Blandy O, Herrero P, Gilchrist M, Gordon A, Toumazou C, Sriskandan S, Georgiou P, Holmes Aet al., 2019, Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study, Journal of Antimicrobial Chemotherapy, Vol: 74, Pages: 1108-1115, ISSN: 0305-7453

BackgroundInfection 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.MethodsAn 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 160 203 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.ResultsOne 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).ConclusionsAn SML algorithm was able to diagnose infection in individuals presenting to hospital using routinely available blood parameters.

Journal article

Rawson TM, Ahmad R, Toumazou C, Georgiou P, Holmes Aet al., 2019, Artificial intelligence can improve decision-making in infection management, Nature Human Behaviour, Vol: 3, Pages: 543-545, ISSN: 2397-3374

Antibiotic resistance is an emerging global danger. Reaching responsible prescribing decisions requires the integration of broad and complex information. Artificial intelligence tools could support decision-making at multiple levels, but building them needs a transparent co-development approach to ensure their adoption upon implementation.

Journal article

Ming D, Rawson T, Sangkaew S, Rodriguez-Manzano J, Georgiou P, Holmes Aet al., 2019, Connectivity of rapid-testing diagnostics and surveillance of infectious diseases, Bulletin of the World Health Organization, Vol: 97, Pages: 242-244, ISSN: 0042-9686

The World Health Organization (WHO) developed the ASSURED criteria to describe the ideal characteristics for point-of-care testing in low-resource settings: affordable, sensitive, specific, user-friendly, rapid and robust, equipment-free and deliverable.1 These standards describe. Over the last decade, widespread adoption of point-of-care testing has led to significant changes in clinical decision-making processes. The development of compact molecular diagnostics, such as the GeneXpert® platform, have enabled short turnaround times and allowed profiling of antimicrobial resistance. Although modern assays have increased operational requirements, many devices are robust and can be operated within communities with minimal training. These new generation of rapid tests have bypassed barriers to care and enabled treatment to take place independently from central facilities. Here we describe the importance of connectivity, the automatic capture and sharing of patient healthcare data from testing, in the adoption and roll-out of rapid testing.

Journal article

Castro-Sánchez E, Sood A, Rawson TM, Firth J, Holmes AHet al., 2019, Forecasting implementation, adoption, and evaluation challenges for an electronic game–based antimicrobial stewardship intervention: co-design workshop with multidisciplinary stakeholders (Preprint), Publisher: JMIR Publications

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

Working paper

Rawson T, Ming D, Gowers S, Freeman D, Herrero P, Georgiou P, Cass AEG, O'Hare D, Holmes Aet al., 2019, Public acceptability of computer-controlled antibiotic management: an exploration of automated dosing and opportunities for implementation, Journal of Infection, Vol: 78, Pages: 75-86, ISSN: 0163-4453

Journal article

Charani E, Ahmad R, Rawson TM, Castro-Sanchèz E, Tarrant C, Holmes Aet al., 2018, Reply to Peiffer-Smadja, et al., Clin Infect Dis

Journal article

Alnaimi S, Rawson T, Holmes A, 2018, 1472. Antibiotic de-escalation compared with continued empirical treatment in non-ventilated hospital-acquired pneumonia., Open forum infectious diseases, Vol: 5, Pages: S455-S456, ISSN: 2328-8957

Background: Antibiotic de-escalation is an important component of antimicrobial stewardship programs. Nosocomial pneumonia is the most common healthcare-associated infection with nonventilated hospital-acquired pneumonia (HAP) comprising the majority of cases. We aimed to compare antibiotic de-escalation with continued empirical treatment in terms of clinical outcomes in nonventilated HAP.MethodsA retrospective cohort study was conducted including patients meeting the American Thoracic Society criteria for HAP. This compared de-escalated HAP patients to those continued on empirical treatment across three hospitals in West London over 3 months. The primary outcome was the length of stay (LOS), and secondary outcomes were duration of treatment and cost of hospital stay. Effects were adjusted for confounders using multivariate linear regression models.ResultsEighty patients with HAP were identified. Overall, 22/80 (27.5%) had therapy de-escalated and 47/80 (58.8%) continued empirical treatment. A total of 58 patients survived and were included in the analysis, 20 in de-escalation and 38 in continued empirical treatment. Length of stay was shorter in de-escalation by −7.2 (95% CI −12.2, −3.0) days, P < 0.01, with an adjusted difference of −3.2 (95% CI −8.3, 1.9) days, P = 0.21. The duration of treatment was shorter in de-escalation by −3.4 (95% CI −5.8, −0.9) days, P < 0.01, with an adjusted difference of −2.6 (95% CI −5.2, 0.1) days, P = 0.06. The cost of hospital stay was lower in de-escalation by £-2, 907.37 (95% CI −4,865.31, −949.43), P < 0.01, with an adjusted difference of £-1,290.00 (95% CI −3,320.75, 740.74), P = 0.21.ConclusionIn HAP, 27.5% of patients were de-escalated. There was no difference in LOS, duration of treatment, and cost of hospital stay between de-escalation and continued empirical treatment on adjustment for confounders. Future work should explore

Journal article

Herrero P, Rawson TM, Philip A, Moore LSP, Holmes AH, Georgiou Pet al., 2018, Closed-loop control for precision antimicrobial delivery: an In silico proof-of-concept, IEEE Transactions on Biomedical Engineering, Vol: 65, Pages: 2231-2236, ISSN: 0018-9294

IEEE Objective: Inappropriate dosing of patients with antibiotics is a driver of antimicrobial resistance, toxicity, and poor outcomes of therapy. In this paper, we investigate, in silico, the hypothesis that the use of a closed-loop control system could improve the attainment of pharmacokinetic-pharmacodynamic targets for antimicrobial therapy, where wide variations in target attainment have been reported. This includes patients in critical care, patients with renal disease and patients with obesity.

Journal article

Abbara A, Rawson T, Karah N, El-Amin W, Hatcher J, Tajaldin B, Dar O, Dewachi O, Abu Sitta G, Uhlin B, Sparrow Aet al., 2018, A summary and appraisal of existing evidence of antimicrobial resistance in the Syrian conflict, International Journal of Infectious Diseases, Vol: 75, Pages: 26-33, ISSN: 1201-9712

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. We use the example of the Syrian conflict to explore the feasibility of using existing global policies on AMR in conditions of extreme conflict. 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 contextualise the burden of AMR. However, post- onset of the conflict an increasing number of studies conducted in neighboring countries and Europe report AMR in Syrian refugees. High rates of multi-drug resistance, particularly Gram-negative organisms, are 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 neighboring countries hosting refugees and in European countries providing asylum; this requires WHO and other global health institutions to address the causes, costs, and future considerations of conflict-related AMR as an issue of global governance.

Journal article

Abdolrasouli A, Petrou MA, Park H, Rhodes J, Rawson T, Moore L, Donaldson H, Holmes A, Fisher M, Armstrong-James Det al., 2018, Surveillance for azole-resistant Aspergillus fumigatus in a centralized diagnostic mycology service, London, United Kingdom, 1998-2017, Frontiers in Microbiology, Vol: 9, ISSN: 1664-302X

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.

Journal article

Alividza V, Mariano V, Ahmad R, Charani E, Rawson T, Holmes A, Castro Sanchez EMet al., 2018, Investigating the impact of poverty on colonization and infection with drug-resistant organisms in humans: a systematic review, Infectious Diseases of Poverty, Vol: 7, ISSN: 2049-9957

BackgroundPoverty 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.MethodsA 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.ResultsNineteen 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.ConclusionsDespite methodological limitations, the results suggest that addressing social determinants of poverty worldwide remains a crucial yet neglected step towards preventing antimicrobial resistance.

Journal article

Hernandez B, Herrero P, Rawson TM, Moore LSP, Toumazou C, Holmes AH, Georgiou Pet al., 2018, Enhancing antimicrobial surveillance: an automated, dynamic and interactive approach, 18th International Congress on Infectious Disease, Publisher: Elsevier, Pages: 122-122, ISSN: 1201-9712

Conference paper

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