Publications
440 results found
Zhang S, Chen Y-C, Riezk A, et al., 2022, Rapid measurement of lactate in exhaled breath condensate: biosensor optimisation and in-human proof-of-concept, ACS Sensors, Vol: 7, Pages: 3809-3816, ISSN: 2379-3694
Lactate concentration is of increasing interest as a diagnostic for sepsis, septic shock, and trauma. Compared with the traditional blood sample media, the exhaled breath condensate (EBC) has the advantages of non-invasiveness and higher user acceptance. An amperometric biosensor was developed and its application in EBC lactate detection was investigated in this paper. The sensor was modified with PEDOT:PSS-PB, and two different lactate oxidases (LODs). A rotating disk electrode and Koutecky–Levich analysis were applied for the kinetics analysis and gel optimization. The optimized gel formulation was then tested on disposable screen-printed sensors. The disposable sensors exhibited good performance and presented a high stability for both LOD modifications. Finally, human EBC analysis was conducted from a healthy subject at rest and after 30 min of intense aerobic cycling exercise. The sensor coulometric measurements showed good agreement with fluorometric and triple quadrupole liquid chromatography mass spectrometry reference methods. The EBC lactate concentration increased from 22.5 μM (at rest) to 28.0 μM (after 30 min of cycling) and dropped back to 5.3 μM after 60 min of rest.
Bolton W, Rawson T, Hernandez B, et al., 2022, Machine learning and synthetic outcome estimation for individualised antimicrobial cessation, Frontiers in Digital Health, Vol: 4, Pages: 1-12, ISSN: 2673-253X
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.
Ahmad R, Gordon AC, Aylin P, et al., 2022, Effective knowledge mobilisation: creating environments for quick generation, dissemination, and use of evidence., The BMJ, Vol: 379, Pages: 1-5, ISSN: 1759-2151
Ahuja S, Singh S, Charani E, et al., 2022, An evaluation of the implementation of interventions to reduce post-operative infections and optimise antibiotic use across the surgical pathway in India: A mixed methods exploratory study protocol, Pilot and Feasibility Studies, Vol: 8, ISSN: 2055-5784
Introduction:Postoperative infections represent a significant burden of disease, demanding antibiotic prescriptions, and are contributing to antimicrobial resistance. The burden of infection as a surgical complication is greater in low- and middle-income countries (LMICs). We report the protocol of a pilot study for the co-design, implementation and evaluation of two infection prevention and control (IPC) and antimicrobial stewardship (AMS) interventions across the surgical pathway in a teaching hospital in India.Methods and analysis:The two interventions developed following in-depth qualitative enquiry are (i) surveillance and feedback of postoperative infections to optimise the use of antibiotics in two surgical departments (gastrointestinal and cardiovascular and thoracic surgery) and (ii) raising awareness amongst patients, carers and members of public about IPC and AMS. We will conduct a prospective study, formatively evaluating the implementation process of delivering the two co-designed interventions using implementation science frameworks. The study will systematically assess the context of intervention delivery, so that implementation support for the interventions may be adapted to the needs of stakeholders throughout the study. Analysis of implementation logs and interviews with stakeholders upon completion of the implementation period, will offer insights into the perceived acceptability, appropriateness, feasibility and sustainability of the interventions and their implementation support. Implementation costs will be captured descriptively. Feasibility of clinical data collection to investigate effectiveness of interventions will also be assessed for a future larger study. Thematic framework analysis and descriptive statistics will be used to report the qualitative and quantitative data, respectively.Strengths and limitations of this study:• The paired interventions have been co-designed from their inception with involvement of stakeholders at diffe
Bolton WJ, Badea C, Georgiou P, et al., 2022, Developing moral AI to support decision-making about antimicrobial use, NATURE MACHINE INTELLIGENCE, Vol: 4, Pages: 912-915
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- Citations: 3
Herrero Vinas P, Wilson R, Armiger R, et al., 2022, Closed-loop control of continuous piperacillin delivery: an in silico study, Frontiers in Bioengineering and Biotechnology, Vol: 10, ISSN: 2296-4185
Background and objective: Sub-therapeutic dosing of piperacillin-tazobactam in critically-ill patients is associated with poor clinical outcomes and may promote the emergence of drug-resistant infections. In this paper, an in silico investigation of whether closed-loop control can improve pharmacokinetic-pharmacodynamic (PK-PD) target attainment is described.Method: An in silico platform was developed using PK data from 20 critically-ill patients receiving piperacillin-tazobactam where serum and tissue interstitial fluid (ISF) PK were defined. Intra-day variability on renal clearance, ISF sensor error, and infusion constraints were taken into account. Proportional-integral-derivative (PID) control was selected for drug delivery modulation. Dose adjustment was made based on ISF sensor data with a 30-minute sampling period, targeting a serum piperacillin concentration between 32-64 mg/L. A single tuning parameter set was employed across the virtual population. The PID controller was compared to standard therapy, including bolus and continuous infusion of piperacillin-tazobactam.Results: Despite significant inter-subject and simulated intra-day PK variability and sensor error, PID demonstrated a significant improvement in target attainment compared to traditional bolus and continuous infusion approaches. Conclusion: A PID controller driven by ISF drug concentration measurements has the potential to precisely deliver piperacillin-tazobactam in critically-ill patients undergoing treatment for sepsis.
Miglietta L, Xu K, Chhaya P, et al., 2022, Adaptive filtering framework to remove nonspecific and low-efficiency reactions in multiplex digital PCR based on sigmoidal trends., Analytical Chemistry, Vol: 94, Pages: 14159-14168, ISSN: 0003-2700
Real-time digital polymerase chain reaction (qdPCR) coupled with machine learning (ML) methods has shown the potential to unlock scientific breakthroughs, particularly in the field of molecular diagnostics for infectious diseases. One promising application of this emerging field explores single fluorescent channel PCR multiplex by extracting target-specific kinetic and thermodynamic information contained in amplification curves, also known as data-driven multiplexing. However, accurate target classification is compromised by the presence of undesired amplification events and not ideal reaction conditions. Therefore, here, we proposed a novel framework to identify and filter out nonspecific and low-efficient reactions from qdPCR data using outlier detection algorithms purely based on sigmoidal trends of amplification curves. As a proof-of-concept, this framework is implemented to improve the classification performance of the recently reported data-driven multiplexing method called amplification curve analysis (ACA), using available published data where the ACA is demonstrated to screen carbapenemase-producing organisms in clinical isolates. Furthermore, we developed a novel strategy, named adaptive mapping filter (AMF), to adjust the percentage of outliers removed according to the number of positive counts in qdPCR. From an overall total of 152,000 amplification events, 116,222 positive amplification reactions were evaluated before and after filtering by comparing against melting peak distribution, proving that abnormal amplification curves (outliers) are linked to shifted melting distribution or decreased PCR efficiency. The ACA was applied to assess classification performance before and after AMF, showing an improved sensitivity of 1.2% when using inliers compared to a decrement of 19.6% when using outliers (p-value < 0.0001), removing 53.5% of all wrong melting curves based only on the amplification shape. This work explores the correlation between the kinetics
Arkell P, Wilson R, Watkins K, et al., 2022, Application of therapeutic drug monitoring to the treatment of bacterial central nervous system infection: a scoping review, Journal of Antimicrobial Chemotherapy, Vol: 77, Pages: 3408-3413, ISSN: 0305-7453
BackgroundBacterial central nervous system (CNS) infection is challenging to treat and carries high risk of recurrence, morbidity, and mortality. Low CNS penetration of antibiotics may contribute to poor clinical outcomes from bacterial CNS infections. The current application of therapeutic drug monitoring (TDM) to management of bacterial CNS infection was reviewed.MethodsStudies were included if they described adults treated for a suspected/confirmed bacterial CNS infection and had antibiotic drug concentration(s) determined that affected individual treatment.ResultsOne-hundred-and-thirty-six citations were retrieved. Seventeen manuscripts were included describing management of 68 patients. TDM for vancomycin (58/68) and the beta-lactams (29/68) was most common. Timing of clinical sampling varied widely between studies and across different antibiotics. Methods for setting individual PK-PD targets, determining parameters and making treatment changes varied widely and were sometimes unclear.DiscussionDespite increasing observational data showing low CNS penetration of various antibiotics, there are few clinical studies describing practical implementation of TDM in management of CNS infection. Lack of consensus around clinically relevant CSF PK-PD targets and protocols for dose-adjustment may contribute. Standardised investigation of TDM as a tool to improve treatment is required, especially as innovative drug concentration-sensing and PK-PD modelling technologies are emerging. Data generated at different centres offering TDM should be open access and aggregated to enrich understanding and optimize application.
Bolton W, Badea C, Georgiou P, et al., 2022, Developing Moral AI to Support Antimicrobial Decision Making, Nature Machine Intelligence, ISSN: 2522-5839
Surendran S, Castro-Sanchez E, Nampoothiri V, et al., 2022, Indispensable yet invisible: A qualitative study of carer roles in infection prevention in a South Indian hospital, International Journal of Infectious Diseases, Vol: 123, Pages: 84-91, ISSN: 1201-9712
Objectives We investigated the roles of patient carers in infection-related care on surgical wards in a South Indian hospital, from the perspective of healthcare workers (HCW), patients, and their carers. Methods Ethnographic study including ward-round observations (138 hours) and face-to-face interviews (44 HCW, 6 patients/carers). Data (field notes, interview transcripts) were coded in NVivo 12 and thematically analysed. Data collection and analysis were iterative, recursive and continued until thematic saturation. Results Carers have important, unrecognised roles. In the study site, institutional expectations are formalised in policies demanding a carer to always accompany inpatients. Such intense presence embeds families in the patient care environment, as demonstrated by their high engagement in direct personal (bathing patients) and clinical care (wound care). Carers actively participate in discussions on patient progress with HCWs, including therapeutic options. There is a misalignment between how carers are positioned by the organisation (through policy mandates, institutional practices, and HCWs expectations), and the role that they play in practice, resulting in their role, though indispensable, remaining unrecognised. Conclusion Current models of patient and carer involvement in infection prevention and control (IPC) are poorly aligned with socio-cultural and contextual aspects of care. Culture-sensitive IPC policies which embrace the roles that carers play are urgently needed.
Moser N, Yu L-S, Rodriguez Manzano J, et al., 2022, Quantitative detection of dengue serotypes using a smartphone-connected handheld Lab-on-Chip platform, Frontiers in Bioengineering and Biotechnology, Vol: 10, Pages: 1-14, ISSN: 2296-4185
Dengue is one of the most prevalent infectious diseases in the world. Rapid, accurate and scalable diagnostics are key to patient management and epidemiological surveillance of the dengue virus (DENV), however current technologies do not match required clinical sensitivity and specificity or rely on large laboratory equipment. In this work, we report the translation of our smartphone-connected handheld Lab-on-Chip (LoC) platform for the quantitative detection of twodengue serotypes. At its core, the approach relies on the combination of Complementary Metal Oxide-Semiconductor (CMOS) microchip technology to integrate an array of 78x56 potentiometric sensors, and a label-free reverse-transcriptase loop mediated isothermal amplification (RT-LAMP) assay. The platform communicates to a smartphone app which synchronises results in real time with a secure cloud server hosted by Amazon Web Services (AWS) for epidemiological surveillance. The assay on our LoC platform (RT-eLAMP) was shown to match performance on a gold-standard fluorescence-based real-time instrument (RT-qLAMP) with synthetic DENV-1 and DENV-2 RNA and extracted RNA from 9 DENV-2 clinical isolates, achieving quantitative detection in under 15 minutes. To validate the portability of the platform and the geo-tagging capabilities, we led our study in the laboratories at Imperial College London, UK, and Kaohsiung Medical Hospital, Taiwan. This approach carries high potential for application in low resource settings at the point-of-care (PoC).
Stirrup O, Blackstone J, Mapp F, et al., 2022, Effectiveness of rapid SARS-CoV-2 genome sequencing in supporting infection control for hospital-onset COVID-19 infection: Multicentre, prospective study, ELIFE, Vol: 11, ISSN: 2050-084X
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Wilson R, Arkell P, Riezk A, et al., 2022, Addition of probenecid to oral beta-lactam antibiotics: a systematic review and meta-analysis, Journal of Antimicrobial Chemotherapy, Vol: 77, Pages: 2364-2372, ISSN: 0305-7453
Objective: Explore literature comparing the pharmacokinetic and clinical outcomes from addition of probenecid to oral beta-lactams.Data sources: Medline and EMBASE were searched from inception to December 2021.Study eligibility criteria: All English language studies comparing the addition of probenecid (intervention) to an oral beta-lactam (flucloxacillin, penicillin-V, amoxicillin(+/-clavulanate), cephalexin, cefuroxime-axetil) alone (comparator).Risk of bias: Risk of Bias in Non-randomised studies of interventions (ROBINS-I) and Risk of Bias for Randomised studies 2 (ROB-2) tools were used.Methods of data synthesis: 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%) abstracts screened were included. Populations, methodology, and outcome data were heterogenous. Common populations included healthy volunteer (9/18;50%) and gonococcal infection (6/18;33%). Most studies were cross-over trials (11/18;61%) or parallel arm randomised trial (4/18;22%). Where pharmacokinetic analyses were performed, addition of probenecid to oral beta-lactams increased total AUC (7/7;100¬%), peak observed concentration (Cmax,5/8;63%), and serum half-life (t1/2,6/8;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. Conclusion: Probenecid boosted beta-lactam therapy is associated with improved outcomes in gonococcal disease. Pharmacokinetic data suggest that probenecid boosted oral beta-lactam therapy may have a broader application, but appropriately powered mechanistic and efficacy st
Lee S-S, Tambyah PA, Abubakar AA, et al., 2022, Farewell to Professor Eskild Petersen - reflections on editorship in pandemic time, INTERNATIONAL JOURNAL OF INFECTIOUS DISEASES, Vol: 122, Pages: 572-575, ISSN: 1201-9712
Rawson TM, Eigo T, Wilson R, et al., 2022, Exploring patient acceptance of research within Complex oral and IV Outpatient Parenteral Antimicrobial Therapy (COpAT) networks, JAC-Antimicrobial Resistance, Vol: 4, ISSN: 2632-1823
Boyd SE, Holmes A, Peck R, et al., 2022, OXA-48-like β-lactamases: global epidemiology, treatment options, and development pipeline, Antimicrobial Agents and Chemotherapy, Vol: 66, ISSN: 0066-4804
Modern medicine is threatened by the rising tide of antimicrobial resistance, especially among Gram-negative bacteria, where resistance to β-lactams is most often mediated by β-lactamases. The penicillin and cephalosporin ascendancies were, in their turn, ended by the proliferation of TEM penicillinases and CTX-M extended-spectrum β-lactamases. These class A β-lactamases have long been considered the most important. For carbapenems, however, the threat is increasingly from the insidious rise of a class D carbapenemase, OXA-48, and its close relatives. Over the past 20 years, OXA-48 and "OXA-48-like" enzymes have proliferated to become the most prevalent enterobacterial carbapenemases across much of Europe, Northern Africa, and the Middle East. OXA-48-like enzymes are notoriously difficult to detect because they often cause only low-level in vitro resistance to carbapenems, meaning that the true burden is likely underestimated. Despite this, they are associated with carbapenem treatment failures. A highly conserved incompatibility complex IncL plasmid scaffold often carries blaOXA-48 and may carry other antimicrobial resistance genes, leaving limited treatment options. High conjugation efficiency means that this plasmid is sometimes carried by multiple Enterobacterales in a single patient. Producers evade most β-lactam-β-lactamase inhibitor combinations, though promising agents have recently been licensed, notably ceftazidime-avibactam and cefiderocol. The molecular machinery enabling global spread, current treatment options, and the development pipeline of potential new therapies for Enterobacterales that produce OXA-48-like β-lactamases form the focus of this review.
Myall A, Price J, Peach R, et al., 2022, Predicting hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study, The Lancet Digital Health, Vol: 4, Pages: e573-e583, ISSN: 2589-7500
Background:Real-time prediction is key to prevention and control of infections associated with health-care settings. Contacts enable spread of many infections, yet most risk prediction frameworks fail to account for their dynamics. We developed, tested, and internationally validated a real-time machine-learning framework, incorporating dynamic patient-contact networks to predict hospital-onset COVID-19 infections (HOCIs) at the individual level.Methods:We report an international retrospective cohort study of our framework, which extracted patient-contact networks from routine hospital data and combined network-derived variables with clinical and contextual information to predict individual infection risk. We trained and tested the framework on HOCIs using the data from 51 157 hospital inpatients admitted to a UK National Health Service hospital group (Imperial College Healthcare NHS Trust) between April 1, 2020, and April 1, 2021, intersecting the first two COVID-19 surges. We validated the framework using data from a Swiss hospital group (Department of Rehabilitation, Geneva University Hospitals) during a COVID-19 surge (from March 1 to May 31, 2020; 40 057 inpatients) and from the same UK group after COVID-19 surges (from April 2 to Aug 13, 2021; 43 375 inpatients). All inpatients with a bed allocation during the study periods were included in the computation of network-derived and contextual variables. In predicting patient-level HOCI risk, only inpatients spending 3 or more days in hospital during the study period were examined for HOCI acquisition risk.Findings:The framework was highly predictive across test data with all variable types (area under the curve [AUC]-receiver operating characteristic curve [ROC] 0·89 [95% CI 0·88–0·90]) and similarly predictive using only contact-network variables (0·88 [0·86–0·90]). Prediction was reduced when using only hospital contextual (AUC-ROC 0·82 [95% CI 0&middo
Zhu J, Holmes A, 2022, Changing patterns of bloodstream infections in the community and acute care across two COVID-19 epidemic waves: a retrospective analysis using data linkage, Clinical Infectious Diseases, Vol: 75, Pages: e1082-e1091, ISSN: 1058-4838
BackgroundWe examined the epidemiology of community- and hospital-acquired bloodstream infections (BSIs) in COVID-19 and non-COVID-19 patients across two epidemic waves.MethodsWe analysed blood cultures of patients presenting and admitted to a London hospital group between January 2020 and February 2021. We reported BSI incidence, as well as changes in sampling, case mix, healthcare capacity, and COVID-19 variants.Results34,044 blood cultures were taken. We identified 1,047 BSIs; 653 (62.4%) community-acquired and 394 (37.6%) hospital-acquired. Important changes in patterns were seen. Among community-acquired BSIs, Escherichia coli BSIs remained lower than pre-pandemic level during COVID-19 waves, however peaked following lockdown easing in May 2020, deviating from the historical trend of peaking in August. The hospital-acquired BSI rate was 100.4 per 100,000 patient-days across the pandemic, increasing to 132.3 during the first wave and 190.9 during the second, with significant increase seen in elective inpatients. Patients who developed a hospital-acquired BSI, including those without COVID-19, experienced 20.2 excess days of hospital stay and 26.7% higher mortality, higher than reported in pre-pandemic literature. In intensive care, the BSI rate was 421.0 per 100,000 patient-ICU days during the second wave, compared to 101.3 pre-COVID. The BSI incidence in those infected with the SARS-CoV-2 Alpha variant was similar to that seen with earlier variants.ConclusionsThe pandemic and national responses have impacted the patterns of community- and hospital-acquired BSIs, in COVID-19 and non-COVID-19 patients. Factors driving the observed patterns are complex. Infection surveillance needs to consider key aspects of pandemic response and changes in healthcare access and practice.
Charani E, McKee M, Balasegaram M, et al., 2022, Global burden of antimicrobial resistance: essential pieces of a global puzzle, The Lancet, Vol: 399, Pages: 2346-2347, ISSN: 0140-6736
Flowers P, McLeod J, Mapp F, et al., 2022, How acceptable is rapid whole genome sequencing for infectious disease management in hospitals? Perspectives of those involved in managing nosocomial SARS-CoV-2
<jats:title>Structured summary</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Whole genome sequencing (WGS) for managing healthcare associated infections (HCAIs) has developed considerably through experiences with SARS-CoV-2. We interviewed various healthcare professionals (HCPs) with direct experience of using WGS in hospitals (within the COG-UK Hospital Onset COVID-19 Infection (HOCI) study) to explore its acceptability and future use.</jats:p></jats:sec><jats:sec><jats:title>Method</jats:title><jats:p>An exploratory, cross-sectional, qualitative design employed semi-structured interviews with 39 diverse HCPs between December 2020 and June 2021. Participants were recruited from five sites within the larger clinical study of a novel genome sequencing reporting tool for SARS-CoV-2 (the HOCI study). All had experience, in their diverse roles, of using sequencing data to manage nosocomial SARS-CoV-2 infection. Deductive and inductive thematic analysis identified themes exploring aspects of the acceptability of sequencing.</jats:p></jats:sec><jats:sec><jats:title>Findings</jats:title><jats:p>The analysis highlighted the overall acceptability of rapid WGS for infectious disease using SARS-CoV-2 as a case study. Diverse professionals were largely very positive about its future use and believed that it could become a valuable and routine tool for managing HCAIs. We identified three key themes ‘1) ‘Proof of concept achieved’; 2) ‘Novel insights and implications’; and 3) ‘Challenges and demands’.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>Our qualitative analysis, drawn from five diverse hospitals, shows the broad acceptability of rapid sequencing and its potential. Participants believed it could and should become an everyday technology capable
Courtenay M, Castro-Sanchez E, Gallagher R, et al., 2022, The delivery of antimicrobial stewardship competencies in United Kingdom pre-registration nurse education programmes: A national cross-sectional survey (vol 121, pg 39, 2022), JOURNAL OF HOSPITAL INFECTION, Vol: 124, Pages: 123-123, ISSN: 0195-6701
Tonkin-Crine S, McLeod M, Borek A, et al., 2022, O01 Nuances of implementing antibiotic stewardship in high-prescribing English general practices: an implementation study, Publisher: OXFORD UNIV PRESS
, 2022, Clinicians, scientists, and public health experts call for Boris Johnson to listen to climate activist on hunger strike., BMJ, Vol: 377
Miglietta L, Xu K, Chhaya P, et al., 2022, An adaptive filtering framework for non-specific and inefficient reactions in multiplex digital PCR based on sigmoidal trends
<jats:title>ABSTRACT</jats:title><jats:p>Real-time digital PCR (qdPCR) coupled with artificial intelligence has shown the potential of unlocking scientific breakthroughs, particularly in the field of molecular diagnostics for infectious diseases. One of the most promising applications is the use of machine learning (ML) methods to enable single fluorescent channel PCR multiplex by extracting target-specific kinetic and thermodynamic information contained in amplification curves. However, the robustness of such methods can be affected by the presence of undesired amplification events and nonideal reaction conditions. Therefore, here we proposed a novel framework to filter non-specific and low efficient reactions from qdPCR data using outlier detection algorithms purely based on sigmoidal trends of amplification curves. As a proof-of-concept, this framework is implemented to improve the classification performance of the recently reported ML-based Amplification Curve Analysis (ACA), using available data from a previous publication where the ACA method was used to screen carbapenemase-producing organisms in clinical isolates. Furthermore, we developed a novel strategy, named Adaptive Mapping Filter (AMF), to consider the variability of positive counts in digital PCR. Over 152,000 amplification events were analyzed. For the positive reactions, filtered and unfiltered amplification curves were evaluated by comparing against melting peak distribution, proving that abnormalities (filtered out data) are linked to shifted melting distribution or decreased PCR efficiency. The ACA was applied to compare classification accuracies before and after AMF, showing an improved sensitivity of 1.18% for inliers and 20% for outliers (p-value < 0.0001). This work explores the correlation between kinetics of amplification curves and thermodynamics of melting curves and it demonstrates that filtering out non-specific or low efficient reactions can significantly impr
Malpartida-Cardenas K, Miglietta L, Peng T, et al., 2022, Single-channel digital LAMP multiplexing using Amplification Curve Analysis, Sensors and Diagnostics, Vol: 1, Pages: 465-468, ISSN: 2635-0998
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.
Ming DK, Jangam S, Gowers SAN, et al., 2022, Real-time continuous measurement of lactate through a minimally invasive microneedle patch: a phase I clinical study, BMJ Innovations, Vol: 8, Pages: 87-94, ISSN: 2055-8074
Introduction 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.Methods Five healthy adult participants wore a solid microneedle biosensor patch on their forearms and undertook aerobic exercise for 30 min. The microneedle biosensor quantifies lactate concentrations in interstitial fluid within the dermis continuously and in real-time. Outputs were captured as sensor current and compared with lactate concentrations from venous blood and microdialysis.Results The biosensor was well-tolerated. Participants generated a median peak venous lactate of 9.25 mmol/L (IQR 6.73–10.71). 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 min (IQR −4 to 11 min) between microneedle and blood lactate measurements.Conclusion This study provides first-in-human data on use of a minimally invasive microneedle patch for continuous lactate measurement, providing dynamic monitoring. This low-cost platform offers distinct advantages to frequent blood sampling in a wide range of clinical settings, especially where access to laboratory services is limited or blood sampling is infeasible. Implementation of this technology in healthcare settings could support personalised decision-making in a variety of hospital and community settings.
Ming DK, Tuan NM, Hernandez B, et al., 2022, The diagnosis of dengue in patients presenting with acute febrile illness using supervised machine learning and impact of seasonality, Frontiers in Digital Health, Vol: 4, ISSN: 2673-253X
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
Arkell P, Wilson R, Antcliffe DB, et al., 2022, A pilot observational study of CSF vancomycin therapeutic drug monitoring during the treatment of nosocomial ventriculitis., Journal of Infection, ISSN: 0163-4453
Myall A, Price J, Peach R, et al., 2022, Prediction of hospital-onset COVID-19 using networks of patient contact: an observational study, IMED conference, Publisher: Elsevier, Pages: S109-S110, ISSN: 1201-9712
Myall A, Peach R, Wan Y, et al., 2022, Improved contact tracing using network analysis and spatial-temporal proximity, iMED conference, Publisher: Elsevier, Pages: S20-S20, ISSN: 1201-9712
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