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  • 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., 2021,

    A real-world evaluation of a case-based reasoning algorithm to support antimicrobial prescribing decisions in acute care

    , Clinical Infectious Diseases, Vol: 72, Pages: 2103-2111, 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
    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

    , Clinical Microbiology and Infection, Vol: 27, Pages: 762-768, ISSN: 1198-743X

    ObjectivesWe investigated the impact of COVID-19 and national pandemic response on primary care antibiotic prescribing in London.MethodsIndividual 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.ResultsRecords 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.DiscussionA 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
    Arkell P, Mahboobani S, Wilson R, Fatania N, Coleman M, Borman AM, Johnson EM, Armstrong-James DPH, Abdolrasouli Aet al., 2021,

    Bronchoalveolar lavage fluid IMMY Sona Aspergillus lateral-flow assay for the diagnosis of invasive pulmonary aspergillosis: a prospective, real life evaluation

    , MEDICAL MYCOLOGY, Vol: 59, Pages: 404-408, ISSN: 1369-3786
  • 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
    Rodriguez-Manzano J, Malpartida-Cardenas K, Moser N, Pennisi I, Cavuto M, Miglietta L, Moniri A, Penn R, Satta G, Randell P, Davies F, Bolt F, Barclay W, Holmes A, Georgiou Pet al., 2021,

    Handheld point-of-care system for rapid detection of SARS-CoV-2 extracted RNA in under 20 min

    , ACS Central Science, Vol: 7, Pages: 307-317, ISSN: 2374-7943

    The COVID-19 pandemic is a global health emergency characterized by the high rate of transmission and ongoing increase of cases globally. Rapid point-of-care (PoC) diagnostics to detect the causative virus, SARS-CoV-2, are urgently needed to identify and isolate patients, contain its spread and guide clinical management. In this work, we report the development of a rapid PoC diagnostic test (<20 min) based on reverse transcriptase loop-mediated isothermal amplification (RT-LAMP) and semiconductor technology for the detection of SARS-CoV-2 from extracted RNA samples. The developed LAMP assay was tested on a real-time benchtop instrument (RT-qLAMP) showing a lower limit of detection of 10 RNA copies per reaction. It was validated against extracted RNA from 183 clinical samples including 127 positive samples (screened by the CDC RT-qPCR assay). Results showed 91% sensitivity and 100% specificity when compared to RT-qPCR and average positive detection times of 15.45 ± 4.43 min. For validating the incorporation of the RT-LAMP assay onto our PoC platform (RT-eLAMP), a subset of samples was tested (n = 52), showing average detection times of 12.68 ± 2.56 min for positive samples (n = 34), demonstrating a comparable performance to a benchtop commercial instrument. Paired with a smartphone for results visualization and geolocalization, this portable diagnostic platform with secure cloud connectivity will enable real-time case identification and epidemiological surveillance.

  • Journal article
    Yu L-S, Rodriguez-Manzano J, Moser N, Moniri A, Malpartida-Cardenas K, Miscourides N, Sewell T, Kochina T, Brackin A, Rhodes J, Holmes AH, Fisher MC, Georgiou Pet al., 2020,

    Rapid detection of azole-resistant Aspergillus fumigatus in clinical and environmental isolates using lab-on-a-chip diagnostic system

    , Journal of Clinical Microbiology, Vol: 58, Pages: 1-11, ISSN: 0095-1137

    Aspergillus fumigatus has widely evolved resistance to the most commonly used class of antifungal chemicals, the azoles. Current methods for identifying azole resistance are time-consuming and depend on specialized laboratories. There is an urgent need for rapid detection of these emerging pathogens at point-of-care to provide the appropriate treatment in the clinic and to improve management of environmental reservoirs to mitigate the spread of antifungal resistance. Our study demonstrates the rapid and portable detection of the two most relevant genetic markers linked to azole resistance, the mutations TR34 and TR46, found in the promoter region of the gene encoding the azole target, cyp51A. We developed a lab-on-a-chip platform consisting of: (1) tandem-repeat loop-mediated isothermal amplification, (2) state-of-the-art complementary metal-oxide-semiconductor microchip technology for nucleic-acid amplification detection and, (3) and a smartphone application for data acquisition, visualization and cloud connectivity. Specific and sensitive detection was validated with isolates from clinical and environmental samples from 6 countries across 5 continents, showing a lower limit-of-detection of 10 genomic copies per reaction in less than 30 minutes. When fully integrated with a sample preparation module, this diagnostic system will enable the detection of this ubiquitous fungus at the point-of-care, and could help to improve clinical decision making, infection control and epidemiological surveillance.

  • Journal article
    Rawson TM, Wilson R, Holmes A, 2021,

    Understanding the role of bacterial and fungal infection in COVID-19

    , Clinical Microbiology and Infection, ISSN: 1198-743X
  • Journal article
    Moniri A, Miglietta L, Holmes A, Georgiou P, Rodriguez Manzano Jet al., 2020,

    High-level multiplexing in digital PCR with intercalating dyes by coupling real-time kinetics and melting curve analysis.

    , Analytical Chemistry, Vol: 92, Pages: 14181-14188, ISSN: 0003-2700

    Digital polymerase chain reaction (dPCR) is a mature technique that has enabled scientific breakthroughs in several fields. However, this technology is primarily used in research environments with high-level multiplexing representing a major challenge. Here, we propose a novel method for multiplexing, referred to as amplification and melting curve analysis (AMCA), which leverages the kinetic information in real-time amplification data and the thermodynamic melting profile using an affordable intercalating dye (EvaGreen). The method trains a system comprised of supervised machine learning models for accurate classification, by virtue of the large volume of data from dPCR platforms. As a case study, we develop a new 9-plex assay to detect mobilised colistin resistant (mcr) genes as clinically relevant targets for antimicrobial resistance. Over 100,000 amplification events have been analysed, and for the positive reactions, the AMCA approach reports a classification accuracy of 99.33 ± 0.13%, an increase of 10.0% over using melting curve analysis. This work provides an affordable method of high-level multiplexing without fluorescent probes, extending the benefits of dPCR in research and clinical settings.

  • Journal article
    Moniri A, Miglietta L, Malpartida Cardenas K, Pennisi I, Cacho Soblechero M, Moser N, Holmes A, Georgiou P, Rodriguez Manzano Jet al., 2020,

    Amplification curve analysis: Data-driven multiplexing using real-time digital PCR

    , Analytical Chemistry, Vol: 92, Pages: 13134-13143, ISSN: 0003-2700

    Information about the kinetics of PCR reactions are encoded in the amplification curve. However, in digital PCR (dPCR), this information is typically neglected by collapsing each amplification curve into a binary output (positive/negative). Here, we demonstrate that the large volume of raw data obtained from realtime dPCR instruments can be exploited to perform data-driven multiplexing in a single fluorescent channel using machine learning methods, by virtue of the information in the amplification curve. This new approach, referred to as amplification curve analysis (ACA), was shown using an intercalating dye (EvaGreen), reducing the cost and complexity of the assay and enabling the use of melting curve analysis for validation. As a case study, we multiplexed 3 carbapenem-resistant genes to show the impact of this approach on global challenges such as antimicrobial resistance. In the presence of single targets, we report a classification accuracy of 99.1% (N = 16188) which represents a 19.7% increase compared to multiplexing based on the final fluorescent intensity. Considering all combinations of amplification events (including coamplifications), the accuracy was shown to be 92.9% (N = 10383). To support the analysis, we derived a formula to estimate the occurrence of co-amplification in dPCR based on multivariate Poisson statistics, and suggest reducing the digital occupancy in the case of multiple targets in the same digital panel. The ACA approach takes a step towards maximizing the capabilities of existing real-time dPCR instruments and chemistries, by extracting more information from data to enable data-driven multiplexing with high accuracy. Furthermore, we expect that combining this method with existing probe-based assays will increase multiplexing capabilities significantly. We envision that once emerging point-of-care technologies can reliably capture real-time data from isothermal chemistries, the ACA method will facilitate the implementation of dPCR outs

  • Journal article
    Ming DK, Sorawat S, Chanh HQ, Nhat PTH, Yacoub S, Georgiou P, Holmes AHet al., 2020,

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

    , International Journal of Infectious Diseases, Vol: 96, Pages: 648-654, ISSN: 1201-9712

    Optimal management of infectious diseases is guided by up-to-date information at the individual and public health level. For infections of global importance including emerging pandemics such as COVID-19 or prevalent endemic diseases such like dengue, identifying patients at risk of severe disease and clinical deterioration can be challenging given the majority present with a mild illness. In our article, we describe the use of wearable technology for continuous physiological monitoring in healthcare. 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, with specific focus on low- and middle- income countries. Finally, we propose a set of essential criteria for the roll-out of wearable technology for clinical use.

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