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Journal articleRawson TM, Brzeska-Trafny I, Maxfield R, et al., 2022,
A practical laboratory method to determine ceftazidime-avibactam-aztreonam synergy in patients with New Delhi Metallo-beta-lactamase (NDM) producing Enterobacterales infection, Journal of Global Antimicrobial Resistance, Vol: 29, Pages: 558-562, ISSN: 2213-7165
Background:In response to infection with New Delhi Metallo-beta-lactamase (NDM) producing Enterobacterales, combination antimicrobial therapy with ceftazidime/avibactam (CAZ/AVI) plus aztreonam (ATM) has been explored. This study evaluated a practical laboratory method of testing for clinically significant synergy between CAZ/AVI+ATM in NDM producing Enterobacterales.Methods:Minimum inhibitory concentration (MIC) of clinical NDM producing isolates were determined for ATM alone and CAZ/AVI+ATM using broth dilution. Restoration of ATM breakpoint following the addition of CAZ/AVI was explored. A CAZ/AVI E-test/ATM disc method was compared to broth dilution.Results:Of 43 isolates, 33/43 (77%) isolates were ATM resistant (median [range] MIC=56 [16 – 512] mg/L). Addition of CAZ/AVI restored the ATM breakpoint (MIC <4mg/L) in 29/33 (89%) of resistant isolates. Overall, the E-test/disc method correlated with findings from broth dilution in 35/43 (81%) of cases. E-test/disc sensitivity was 77% and specificity 85%. Positive predictive value was 92% and negative predictive value 61%.Conclusion:CAZ/AVI+ATM demonstrated significant synergy in most ATM resistant NDM producing Enterobacterales. The E-test/disc method is a quick, reproducible, and reliable method of testing for clinically relevant synergy in the microbiology laboratory.
Journal articleMalpartida-Cardenas K, Miglietta L, Peng T, et al., 2022,
Loop-mediated isothermal amplification assays are currently limited to one target per reaction in the absence of melting curve analysis, molecular probes or restriction enzyme digestion. Here, we demonstrate multiplexing of five targets in a single fluorescent channel using digital LAMP and the machine learning-based method amplification curve analysis, resulting in a classification accuracy of 91.33% on 54 186 positive amplification events.
Journal articleRawson TM, Fatania N, Abdolrasouli A, 2022,
Journal articleMing DK, Hernandez B, Sangkaew S, et al., 2022,
Applied machine learning for the risk-stratification and clinical decision support of hospitalised patients with dengue in Vietnam, PLOS Digital Health, Vol: 1, Pages: e0000005-e0000005
BackgroundIdentifying 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.MethodsWe 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.FindingsThe 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.InterpretationThe 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 t
Journal articleMcLeod J, Stadler E, Wilson R, et al., 2021,
Cefiderocol is a novel siderophore-conjugated β-lactam antibiotic which has been approved for clinical use. It has demonstrated efficacy against infections caused by Gram-negative bacteria, including carbapenem-resistant strains. Novel antibiotics are rarely brought to market and, as such, are ideal candidates for therapeutic drug monitoring which enables optimised dosing across a range of clinical scenarios whilst also reducing the chances of antimicrobial resistance. Here we demonstrate direct electrochemical detection of cefiderocol by oxidation using untreated gold and glassy carbon electrodes as well as multi-walled carbon nanotube (MWCNT)-coated glassy carbon and foamed gold electrodes. Quantification of cefiderocol in the therapeutic range is demonstrated in spiked whole human blood using MWCNT-coated pyrolytic carbon screen-printed electrodes.
Journal articleMing DK, Myall AC, Hernandez B, et al., 2021,
Informing antimicrobial management in the context of COVID-19: understanding the longitudinal dynamics of C-reactive protein and procalcitonin, BMC Infectious Diseases, Vol: 21
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.
Journal articleRawson TM, Wilson R, Moore L, et al., 2021,
Exploring the pharmacokinetics of phenoxymethylpenicillin (Penicillin-V) in adults: a healthy volunteer study, Open Forum Infectious Diseases, Vol: 8, Pages: 1-4, ISSN: 2328-8957
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 concentration was determined and a base population PK model were fitted to the data.
Journal articleMiglietta L, Moniri A, Pennisi I, et al., 2021,
Coupling machine learning and high throughput multiplex digital PCR enables accurate detection of carbapenem-resistant genes in clinical isolates, Frontiers in Molecular Biosciences, Vol: 8, Pages: 1-11, ISSN: 2296-889X
Rapid and accurate identification of patients colonised with carbapenemase-producing organisms (CPOs) is essential to adopt prompt prevention measures to reduce the risk of transmission. Recent studies have demonstrated the ability to combine machine learning (ML) algorithms with real-time digital PCR (dPCR) instruments to increase classification accuracy of multiplex PCR assays when using synthetic DNA templates. We sought to determine if this novel methodology could be applied to improve identification of the five major carbapenem-resistant genes in clinical CPO-isolates, which would represent a leap forward in the use of PCR-based data-driven diagnostics for clinical applications. We collected 3 clinical isolates (including 221 CPO-positive samples) and developed a novel 5-plex PCR assay for detection of blaIMP, blaKPC, blaNDM, blaOXA-48 and blaVIM. Combining the recently reported ML method ‘Amplification and Melting Curve Analysis’ (AMCA) with the abovementioned multiplex assay, we assessed the performance of the AMCA methodology in detecting these genes. The improved classification accuracy of AMCA relies on the usage of real-time data from a single fluorescent channel and benefits from the kinetic/thermodynamic information encoded in the thousands of amplification events produced by high throughput real-time dPCR. The 5-plex showed a lower limit of detection of 10 DNA copies per reaction for each primer set and no cross-reactivity with other carbapenemase genes. The AMCA classifier demonstrated excellentpredictive performance with 99.6% (CI 97.8-99.9%) accuracy (only one misclassified sample out of the 253, with a total of 160,041 positive amplification events), which represents a 7.9% increase (p value < 0.05) compared to conventional melting curve analysis. This work demonstrates the use of the AMCA method to increase the throughput and performance of state-of-the-art molecular diagnostic platforms, without hardware modifications and additiona
Journal articleHernandez B, Herrero-Viñas P, Rawson TM, et al., 2021,
Resistance trend estimation using regression analysis to enhance antimicrobial surveillance: a multi-centre study in London 2009-2016, Antibiotics, Vol: 10, Pages: 1-16, ISSN: 2079-6382
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 pro
Journal articleMing DK, Jangam S, Gowers SAN, et al., 2021,
Real-time Continuous Measurement of Lactate through a Minimally-invasive Microneedle Biosensor: a Phase I Clinical Study
<jats:title>Abstract</jats:title><jats:sec><jats:title>Introduction</jats:title><jats:p>Determination of blood lactate levels supports decision-making in a range of medical conditions. Invasive blood-sampling and laboratory access are often required, and measurements provide a static profile at each instance. We conducted a Phase I clinical study validating performance of a microneedle patch for minimally-invasive, continuous lactate measurement in healthy volunteers.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>Five healthy adult participants wore a solid microneedle biosensor on their forearms and undertook aerobic exercise for 30 minutes. The microneedle biosensor quantifies lactate concentrations in interstitial fluid (ISF) within the dermis continuously and in real-time. Outputs were captured as sensor current and compared with lactate concentrations from venous blood and microdialysis.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The biosensor was well-tolerated. Participants generated a median peak venous lactate of 9.25 mmol/L (Interquartile range, 6.73 to 10.71). Microdialysate concentrations of lactate closely correlated with blood. Microneedle biosensor current followed venous lactate concentrations and dynamics, with good agreement seen in all participants. There was an estimated lag-time of 5 minutes (IQR -4 to 11 minutes) between microneedle and blood lactate measurements.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>This study provides first-in-human data on use of a minimally-invasive microneedle biosensor for continuous lactate measurement, providing dynamic monitoring. The platform offers distinct advantages to frequent blood sampling in a wide range of clinical settings, especially where access to laboratory services is limited or b
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