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Wei J, Uppal A, Nganjimi C, Warr H, Ibrahim Y, Gu Q, Yuan H, Rahman NM, Jones N, Walker AS, Eyre DW. No evidence of difference in mortality with amoxicillin versus co-amoxiclav for hospital treatment of community-acquired pneumonia. J Infect 2024; 88:106161. [PMID: 38663754 DOI: 10.1016/j.jinf.2024.106161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/14/2024] [Accepted: 04/15/2024] [Indexed: 04/28/2024]
Abstract
OBJECTIVES Current guidelines recommend broad-spectrum antibiotics for high-severity community-acquired pneumonia (CAP), potentially contributing to antimicrobial resistance (AMR). We aim to compare outcomes in CAP patients treated with amoxicillin (narrow-spectrum) versus co-amoxiclav (broad-spectrum), to understand if narrow-spectrum antibiotics could be used more widely. METHODS We analysed electronic health records from adults (≥16 y) admitted to hospital with a primary diagnosis of pneumonia between 01-January-2016 and 30-September-2023 in Oxfordshire, United Kingdom. Patients receiving baseline ([-12 h,+24 h] from admission) amoxicillin or co-amoxiclav were included. The association between 30-day all-cause mortality and baseline antibiotic was examined using propensity score (PS) matching and inverse probability treatment weighting (IPTW) to address confounding by baseline characteristics and disease severity. Subgroup analyses by disease severity and sensitivity analyses with missing covariates imputed were also conducted. RESULTS Among 16,072 admissions with a primary diagnosis of pneumonia, 9685 received either baseline amoxicillin or co-amoxiclav. There was no evidence of a difference in 30-day mortality between patients receiving initial co-amoxiclav vs. amoxicillin (PS matching: marginal odds ratio 0.97 [0.76-1.27], p = 0.61; IPTW: 1.02 [0.78-1.33], p = 0.87). Results remained similar across stratified analyses of mild, moderate, and severe pneumonia. Results were also similar with missing data imputed. There was also no evidence of an association between 30-day mortality and use of additional macrolides or additional doxycycline. CONCLUSIONS There was no evidence of co-amoxiclav being advantageous over amoxicillin for treatment of CAP in 30-day mortality at a population-level, regardless of disease severity. Wider use of narrow-spectrum empirical treatment of moderate/severe CAP should be considered to curb potential for AMR.
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Affiliation(s)
- Jia Wei
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Aashna Uppal
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Christy Nganjimi
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Hermione Warr
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Yasin Ibrahim
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Qingze Gu
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Hang Yuan
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Najib M Rahman
- Nuffield Department of Medicine, University of Oxford, Oxford, UK; Oxford Centre for Respiratory Medicine, Churchill Hospital, Oxford, UK; The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Nicola Jones
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK; The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
| | - David W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK; The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK; The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK.
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Gu Q, Wei J, Yoon CH, Yuan K, Jones N, Brent A, Llewelyn M, Peto TEA, Pouwels KB, Eyre DW, Walker AS. Distinct patterns of vital sign and inflammatory marker responses in adults with suspected bloodstream infection. J Infect 2024; 88:106156. [PMID: 38599549 DOI: 10.1016/j.jinf.2024.106156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 04/01/2024] [Accepted: 04/04/2024] [Indexed: 04/12/2024]
Abstract
OBJECTIVES To identify patterns in inflammatory marker and vital sign responses in adult with suspected bloodstream infection (BSI) and define expected trends in normal recovery. METHODS We included patients ≥16 y from Oxford University Hospitals with a blood culture taken between 1-January-2016 and 28-June-2021. We used linear and latent class mixed models to estimate trajectories in C-reactive protein (CRP), white blood count, heart rate, respiratory rate and temperature and identify CRP response subgroups. Centile charts for expected CRP responses were constructed via the lambda-mu-sigma method. RESULTS In 88,348 suspected BSI episodes; 6908 (7.8%) were culture-positive with a probable pathogen, 4309 (4.9%) contained potential contaminants, and 77,131(87.3%) were culture-negative. CRP levels generally peaked 1-2 days after blood culture collection, with varying responses for different pathogens and infection sources (p < 0.0001). We identified five CRP trajectory subgroups: peak on day 1 (36,091; 46.3%) or 2 (4529; 5.8%), slow recovery (10,666; 13.7%), peak on day 6 (743; 1.0%), and low response (25,928; 33.3%). Centile reference charts tracking normal responses were constructed from those peaking on day 1/2. CONCLUSIONS CRP and other infection response markers rise and recover differently depending on clinical syndrome and pathogen involved. However, centile reference charts, that account for these differences, can be used to track if patients are recovering line as expected and to help personalise infection.
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Affiliation(s)
- Qingze Gu
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jia Wei
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Chang Ho Yoon
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Kevin Yuan
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Nicola Jones
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Andrew Brent
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | | | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, UK; Oxford University Hospitals NHS Foundation Trust, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
| | - Koen B Pouwels
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
| | - David W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK; Oxford University Hospitals NHS Foundation Trust, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK.
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Street TL, Sanderson ND, Barker L, Kavanagh J, Cole K, Llewelyn M, Eyre DW. Target enrichment improves culture-independent detection of Neisseria gonorrhoeae and antimicrobial resistance determinants direct from clinical samples with Nanopore sequencing. Microb Genom 2024; 10:001208. [PMID: 38529900 PMCID: PMC10995632 DOI: 10.1099/mgen.0.001208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 02/10/2024] [Indexed: 03/27/2024] Open
Abstract
Multi-drug-resistant Neisseria gonorrhoeae infection is a significant public health risk. Rapidly detecting N. gonorrhoeae and antimicrobial-resistant (AMR) determinants by metagenomic sequencing of urine is possible, although high levels of host DNA and overgrowth of contaminating species hamper sequencing and limit N. gonorrhoeae genome coverage. We performed Nanopore sequencing of nucleic acid amplification test-positive urine samples and culture-positive urethral swabs with and without probe-based target enrichment, using a custom SureSelect panel, to investigate whether selective enrichment of N. gonorrhoeae DNA improves detection of both species and AMR determinants. Probes were designed to cover the entire N. gonorrhoeae genome, with tenfold enrichment of probes covering selected AMR determinants. Multiplexing was tested in a subset of samples. The proportion of sequence bases classified as N. gonorrhoeae increased in all samples after enrichment, from a median (IQR) of 0.05 % (0.01-0.1 %) to 76 % (42-82 %), giving a corresponding median improvement in fold genome coverage of 365 times (112-720). Over 20-fold coverage, required for robust AMR determinant detection, was achieved in 13/15(87 %) samples, compared to 2/15(13 %) without enrichment. The four samples multiplexed together also achieved >20-fold genome coverage. Coverage of AMR determinants was sufficient to predict resistance conferred by changes in chromosomal genes, where present, and genome coverage also enabled phylogenetic relationships to be reconstructed. Probe-based target enrichment can improve N. gonorrhoeae genome coverage when sequencing DNA extracts directly from urine or urethral swabs, allowing for detection of AMR determinants. Additionally, multiplexing prior to enrichment provided enough genome coverage for AMR detection and reduces the costs associated with this method.
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Affiliation(s)
- Teresa L. Street
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
- National Institute for Health Research Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Nicholas D. Sanderson
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
- National Institute for Health Research Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Leanne Barker
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - James Kavanagh
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Kevin Cole
- Department of Microbiology and Infection, University Hospitals Sussex NHS Trust, Brighton, UK
| | - The GonFast Investigators Group
- Nuffield Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
- National Institute for Health Research Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
- Department of Microbiology and Infection, University Hospitals Sussex NHS Trust, Brighton, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Martin Llewelyn
- Department of Microbiology and Infection, University Hospitals Sussex NHS Trust, Brighton, UK
| | - David W. Eyre
- National Institute for Health Research Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
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Wei J, Stoesser N, Matthews PC, Khera T, Gethings O, Diamond I, Studley R, Taylor N, Peto TEA, Walker AS, Pouwels KB, Eyre DW. Risk of SARS-CoV-2 reinfection during multiple Omicron variant waves in the UK general population. Nat Commun 2024; 15:1008. [PMID: 38307854 PMCID: PMC10837445 DOI: 10.1038/s41467-024-44973-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 01/10/2024] [Indexed: 02/04/2024] Open
Abstract
SARS-CoV-2 reinfections increased substantially after Omicron variants emerged. Large-scale community-based comparisons across multiple Omicron waves of reinfection characteristics, risk factors, and protection afforded by previous infection and vaccination, are limited. Here we studied ~45,000 reinfections from the UK's national COVID-19 Infection Survey and quantified the risk of reinfection in multiple waves, including those driven by BA.1, BA.2, BA.4/5, and BQ.1/CH.1.1/XBB.1.5 variants. Reinfections were associated with lower viral load and lower percentages of self-reporting symptoms compared with first infections. Across multiple Omicron waves, estimated protection against reinfection was significantly higher in those previously infected with more recent than earlier variants, even at the same time from previous infection. Estimated protection against Omicron reinfections decreased over time from the most recent infection if this was the previous or penultimate variant (generally within the preceding year). Those 14-180 days after receiving their most recent vaccination had a lower risk of reinfection than those >180 days from their most recent vaccination. Reinfection risk was independently higher in those aged 30-45 years, and with either low or high viral load in their most recent previous infection. Overall, the risk of Omicron reinfection is high, but with lower severity than first infections; both viral evolution and waning immunity are independently associated with reinfection.
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Affiliation(s)
- Jia Wei
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The Francis Crick Institute, 1 Midland Road, London, UK
- Division of infection and immunity, University College London, London, UK
| | | | | | | | | | | | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
| | - Koen B Pouwels
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - David W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
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Soltan AAS, Thakur A, Yang J, Chauhan A, D'Cruz LG, Dickson P, Soltan MA, Thickett DR, Eyre DW, Zhu T, Clifton DA. A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals. Lancet Digit Health 2024; 6:e93-e104. [PMID: 38278619 DOI: 10.1016/s2589-7500(23)00226-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 10/17/2023] [Accepted: 10/30/2023] [Indexed: 01/28/2024]
Abstract
BACKGROUND Multicentre training could reduce biases in medical artificial intelligence (AI); however, ethical, legal, and technical considerations can constrain the ability of hospitals to share data. Federated learning enables institutions to participate in algorithm development while retaining custody of their data but uptake in hospitals has been limited, possibly as deployment requires specialist software and technical expertise at each site. We previously developed an artificial intelligence-driven screening test for COVID-19 in emergency departments, known as CURIAL-Lab, which uses vital signs and blood tests that are routinely available within 1 h of a patient's arrival. Here we aimed to federate our COVID-19 screening test by developing an easy-to-use embedded system-which we introduce as full-stack federated learning-to train and evaluate machine learning models across four UK hospital groups without centralising patient data. METHODS We supplied a Raspberry Pi 4 Model B preloaded with our federated learning software pipeline to four National Health Service (NHS) hospital groups in the UK: Oxford University Hospitals NHS Foundation Trust (OUH; through the locally linked research University, University of Oxford), University Hospitals Birmingham NHS Foundation Trust (UHB), Bedfordshire Hospitals NHS Foundation Trust (BH), and Portsmouth Hospitals University NHS Trust (PUH). OUH, PUH, and UHB participated in federated training, training a deep neural network and logistic regressor over 150 rounds to form and calibrate a global model to predict COVID-19 status, using clinical data from patients admitted before the pandemic (COVID-19-negative) and testing positive for COVID-19 during the first wave of the pandemic. We conducted a federated evaluation of the global model for admissions during the second wave of the pandemic at OUH, PUH, and externally at BH. For OUH and PUH, we additionally performed local fine-tuning of the global model using the sites' individual training data, forming a site-tuned model, and evaluated the resultant model for admissions during the second wave of the pandemic. This study included data collected between Dec 1, 2018, and March 1, 2021; the exact date ranges used varied by site. The primary outcome was overall model performance, measured as the area under the receiver operating characteristic curve (AUROC). Removable micro secure digital (microSD) storage was destroyed on study completion. FINDINGS Clinical data from 130 941 patients (1772 COVID-19-positive), routinely collected across three hospital groups (OUH, PUH, and UHB), were included in federated training. The evaluation step included data from 32 986 patients (3549 COVID-19-positive) attending OUH, PUH, or BH during the second wave of the pandemic. Federated training of a global deep neural network classifier improved upon performance of models trained locally in terms of AUROC by a mean of 27·6% (SD 2·2): AUROC increased from 0·574 (95% CI 0·560-0·589) at OUH and 0·622 (0·608-0·637) at PUH using the locally trained models to 0·872 (0·862-0·882) at OUH and 0·876 (0·865-0·886) at PUH using the federated global model. Performance improvement was smaller for a logistic regression model, with a mean increase in AUROC of 13·9% (0·5%). During federated external evaluation at BH, AUROC for the global deep neural network model was 0·917 (0·893-0·942), with 89·7% sensitivity (83·6-93·6) and 76·6% specificity (73·9-79·1). Site-specific tuning of the global model did not significantly improve performance (change in AUROC <0·01). INTERPRETATION We developed an embedded system for federated learning, using microcomputing to optimise for ease of deployment. We deployed full-stack federated learning across four UK hospital groups to develop a COVID-19 screening test without centralising patient data. Federation improved model performance, and the resultant global models were generalisable. Full-stack federated learning could enable hospitals to contribute to AI development at low cost and without specialist technical expertise at each site. FUNDING The Wellcome Trust, University of Oxford Medical and Life Sciences Translational Fund.
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Affiliation(s)
- Andrew A S Soltan
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK; Department of Oncology, University of Oxford, Oxford, UK; Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK; Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK; Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
| | - Anshul Thakur
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Jenny Yang
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Anoop Chauhan
- Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | - Leon G D'Cruz
- Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | | | - Marina A Soltan
- The Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
| | - David R Thickett
- The Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
| | - David W Eyre
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK; Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford and Public Health England, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Tingting Zhu
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - David A Clifton
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford, UK; Oxford-Suzhou Centre for Advanced Research, Suzhou, China
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Danielsen AS, Elstrøm P, Eriksen-Volle HM, Hofvind S, Eyre DW, Kacelnik O, Bjørnholt JV. The epidemiology of multidrug-resistant organisms in persons diagnosed with cancer in Norway, 2008-2018: expanding surveillance using existing laboratory and register data. Eur J Clin Microbiol Infect Dis 2024; 43:121-132. [PMID: 37980302 PMCID: PMC10774199 DOI: 10.1007/s10096-023-04698-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 10/31/2023] [Indexed: 11/20/2023]
Abstract
Surveillance has revealed an increase of multidrug-resistant organisms (MDROs), even in low-prevalent settings such as Norway. MDROs pose a particular threat to at-risk populations, including persons with cancer. It is necessary to include such populations in future infection surveillance. By combining existing data sources, we aimed to describe the epidemiology of MDROs in persons diagnosed with cancer in Norway from 2008 to 2018. A cohort was established using data from the Cancer Registry of Norway, which was then linked to notifications of methicillin-resistant Staphylococcus aureus (MRSA), vancomycin- and/or linezolid-resistant enterococci (V/LRE), and carbapenemase-producing Gram-negative bacilli (CP-GNB) from the Norwegian Surveillance System for Communicable Diseases, and laboratory data on third-generation cephalosporin-resistant Enterobacterales (3GCR-E) from Oslo University Hospital (OUH). We described the incidence of MDROs and resistance proportion in Enterobacterales from 6 months prior to the person's first cancer diagnosis and up to 3 years after. The cohort included 322,005 persons, of which 0.3% (878) were diagnosed with notifiable MDROs. Peak incidence rates per 100,000 person-years were 60.9 for MRSA, 97.2 for V/LRE, and 6.8 for CP-GNB. The proportion of 3GCR-E in Enterobacterales in blood or urine cultures at OUH was 6% (746/12,534). Despite overall low MDRO incidence, there was an unfavourable trend in the incidence and resistance proportion of Gram-negative bacteria. To address this, there is a need for effective infection control and surveillance. Our study demonstrated the feasibility of expanding the surveillance of MDROs and at-risk populations through the linkage of existing laboratory and register data.
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Affiliation(s)
- Anders Skyrud Danielsen
- Department of Microbiology, Oslo University Hospital, Oslo, Norway.
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Petter Elstrøm
- Centre for Epidemic Intervention Research, Norwegian Institute of Public Health, Oslo, Norway
| | | | | | - David W Eyre
- Big Data Institute, University of Oxford, Oxford, UK
| | - Oliver Kacelnik
- Department of Microbiology, Oslo University Hospital, Oslo, Norway
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Bešević J, Lacey B, Callen H, Omiyale W, Conroy M, Feng Q, Crook DW, Doherty N, Ebner D, Eyre DW, Fry D, Horn E, Jones EY, Marsden BD, Peto TEA, Starkey F, Stuart D, Welsh S, Wood N, Young A, Young A, Effingham M, Collins R, Holliday J, Allen N. Persistence of SARS-CoV-2 antibodies over 18 months following infection: UK Biobank COVID-19 Serology Study. J Epidemiol Community Health 2023; 78:jech-2023-220569. [PMID: 37923370 PMCID: PMC10850672 DOI: 10.1136/jech-2023-220569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 10/08/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND Little is known about the persistence of antibodies after the first year following SARS-CoV-2 infection. We aimed to determine the proportion of individuals that maintain detectable levels of SARS-CoV-2 antibodies over an 18-month period following infection. METHODS Population-based prospective study of 20 000 UK Biobank participants and their adult relatives recruited in May 2020. The proportion of SARS-CoV-2 cases testing positive for immunoglobulin G (IgG) antibodies against the spike protein (IgG-S), and the nucleocapsid protein (IgG-N), was calculated at varying intervals following infection. RESULTS Overall, 20 195 participants were recruited. Their median age was 56 years (IQR 39-68), 56% were female and 88% were of white ethnicity. The proportion of SARS-CoV-2 cases with IgG-S antibodies following infection remained high (92%, 95% CI 90%-93%) at 6 months after infection. Levels of IgG-N antibodies following infection gradually decreased from 92% (95% CI 88%-95%) at 3 months to 72% (95% CI 70%-75%) at 18 months. There was no strong evidence of heterogeneity in antibody persistence by age, sex, ethnicity or socioeconomic deprivation. CONCLUSION This study adds to the limited evidence on the long-term persistence of antibodies following SARS-CoV-2 infection, with likely implications for waning immunity following infection and the use of IgG-N in population surveys.
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Affiliation(s)
- Jelena Bešević
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | - Ben Lacey
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | - Howard Callen
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | - Wemimo Omiyale
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | - Megan Conroy
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | - Qi Feng
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | - Derrick W Crook
- Nuffield Department of Medicine (NDM), University of Oxford, Oxford, UK
| | | | - Daniel Ebner
- Nuffield Department of Medicine (NDM), University of Oxford, Oxford, UK
| | - David W Eyre
- University of Oxford Big Data Institute, Oxford, UK
| | | | - Edward Horn
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | - E Yvonne Jones
- Nuffield Department of Medicine (NDM), University of Oxford, Oxford, UK
| | - Brian D Marsden
- Nuffield Department of Medicine (NDM), University of Oxford, Oxford, UK
| | - Tim E A Peto
- Nuffield Department of Medicine (NDM), University of Oxford, Oxford, UK
| | - Fenella Starkey
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | - David Stuart
- Nuffield Department of Medicine (NDM), University of Oxford, Oxford, UK
| | | | - Natasha Wood
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | - Alan Young
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
- UK Biobank, Stockport, UK
| | - Allen Young
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | | | - Rory Collins
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
- UK Biobank, Stockport, UK
| | - Jo Holliday
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | - Naomi Allen
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
- UK Biobank, Stockport, UK
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8
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Cooper BS, Evans S, Jafari Y, Pham TM, Mo Y, Lim C, Pritchard MG, Pople D, Hall V, Stimson J, Eyre DW, Read JM, Donnelly CA, Horby P, Watson C, Funk S, Robotham JV, Knight GM. The burden and dynamics of hospital-acquired SARS-CoV-2 in England. Nature 2023; 623:132-138. [PMID: 37853126 PMCID: PMC10620085 DOI: 10.1038/s41586-023-06634-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 09/12/2023] [Indexed: 10/20/2023]
Abstract
Hospital-based transmission had a dominant role in Middle East respiratory syndrome coronavirus (MERS-CoV) and severe acute respiratory syndrome coronavirus (SARS-CoV) epidemics1,2, but large-scale studies of its role in the SARS-CoV-2 pandemic are lacking. Such transmission risks spreading the virus to the most vulnerable individuals and can have wider-scale impacts through hospital-community interactions. Using data from acute hospitals in England, we quantify within-hospital transmission, evaluate likely pathways of spread and factors associated with heightened transmission risk, and explore the wider dynamical consequences. We estimate that between June 2020 and March 2021 between 95,000 and 167,000 inpatients acquired SARS-CoV-2 in hospitals (1% to 2% of all hospital admissions in this period). Analysis of time series data provided evidence that patients who themselves acquired SARS-CoV-2 infection in hospital were the main sources of transmission to other patients. Increased transmission to inpatients was associated with hospitals having fewer single rooms and lower heated volume per bed. Moreover, we show that reducing hospital transmission could substantially enhance the efficiency of punctuated lockdown measures in suppressing community transmission. These findings reveal the previously unrecognized scale of hospital transmission, have direct implications for targeting of hospital control measures and highlight the need to design hospitals better equipped to limit the transmission of future high-consequence pathogens.
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Affiliation(s)
- Ben S Cooper
- NDM Centre for Global Health Research, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
| | - Stephanie Evans
- HCAI, Fungal, AMR, AMU and Sepsis Division, UK Health Security Agency, London, UK
| | - Yalda Jafari
- Centre for Mathematical Modelling of Infectious Diseases, IDE, EPH, London School of Hygiene & Tropical Medicine, London, UK
| | - Thi Mui Pham
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Yin Mo
- NDM Centre for Global Health Research, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Division of Infectious Disease, Department of Medicine, National University Hospital, Singapore, Singapore
- Department of Medicine, National University of Singapore, Singapore, Singapore
| | - Cherry Lim
- NDM Centre for Global Health Research, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Mark G Pritchard
- NDM Centre for Global Health Research, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Diane Pople
- HCAI, Fungal, AMR, AMU and Sepsis Division, UK Health Security Agency, London, UK
| | - Victoria Hall
- HCAI, Fungal, AMR, AMU and Sepsis Division, UK Health Security Agency, London, UK
| | - James Stimson
- HCAI, Fungal, AMR, AMU and Sepsis Division, UK Health Security Agency, London, UK
| | - David W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with UKHSA, Oxford, UK
| | - Jonathan M Read
- Lancaster Medical School, Lancaster University, Lancaster, UK
| | - Christl A Donnelly
- Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Statistics, University of Oxford, Oxford, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Peter Horby
- Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Conall Watson
- Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Sebastian Funk
- Centre for Mathematical Modelling of Infectious Diseases, IDE, EPH, London School of Hygiene & Tropical Medicine, London, UK
| | - Julie V Robotham
- HCAI, Fungal, AMR, AMU and Sepsis Division, UK Health Security Agency, London, UK
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with UKHSA, Oxford, UK
| | - Gwenan M Knight
- Centre for Mathematical Modelling of Infectious Diseases, IDE, EPH, London School of Hygiene & Tropical Medicine, London, UK
- AMR Centre, IDE, EPH, London School of Hygiene & Tropical Medicine, London, UK
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9
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Kapel N, Kalimeris E, Lumley S, Decano A, Rodger G, Lopes Alves M, Dingle K, Oakley S, Barrett L, Barnett S, Crook D, Eyre DW, Matthews PC, Street T, Stoesser N. Evaluation of sequence hybridization for respiratory viruses using the Twist Bioscience Respiratory Virus Research panel and the OneCodex Respiratory Virus sequence analysis workflow. Microb Genom 2023; 9:001103. [PMID: 37676707 PMCID: PMC10569729 DOI: 10.1099/mgen.0.001103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 08/16/2023] [Indexed: 09/08/2023] Open
Abstract
Respiratory viral infections are a major global clinical problem, and rapid, cheap, scalable and agnostic diagnostic tests that capture genome-level information on viral variation are urgently needed. Metagenomic approaches would be ideal, but remain currently limited in that much of the genetic content in respiratory samples is human, and amplifying and sequencing the viral/pathogen component in an unbiased manner is challenging. PCR-based tests, including those which detect multiple pathogens, are already widely used, but do not capture information on strain-level variation; tests with larger viral repertoires are also expensive on a per-test basis. One intermediate approach is the use of large panels of viral probes or 'baits', which target or 'capture' sequences representing complete genomes amongst several different common viral pathogens; these are then amplified, sequenced and analysed with a sequence analysis workflow. Here we evaluate one such commercial bait capture method (the Twist Bioscience Respiratory Virus Research Panel) and sequence analysis workflow (OneCodex), using control (simulated) and patient samples head-to-head with a validated multiplex PCR clinical diagnostic test (BioFire FilmArray). We highlight the limited sensitivity and specificity of the joint Twist Bioscience/OneCodex approach, which are further reduced by shortening workflow times and increasing sample throughput to reduce per-sample costs. These issues with performance may be driven by aspects of both the laboratory (e.g. capacity to enrich for viruses present in low numbers), bioinformatics methods used (e.g. a limited viral reference database) and thresholds adopted for calling a virus as present or absent. As a result, this workflow would require further optimization prior to any implementation for respiratory virus characterization in a routine diagnostic healthcare setting.
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Affiliation(s)
| | | | - Sheila Lumley
- Nuffield Department of Medicine, Oxford, UK
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | | | | | - Marcela Lopes Alves
- Nuffield Department of Medicine, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Kate Dingle
- Nuffield Department of Medicine, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Sarah Oakley
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Lucinda Barrett
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Sophie Barnett
- Nuffield Department of Medicine, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Derrick Crook
- Nuffield Department of Medicine, Oxford, UK
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - David W. Eyre
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford, UK
- Big Data Institute, Nuffield Department of Public Health, Oxford, UK
| | | | - Teresa Street
- Nuffield Department of Medicine, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Nicole Stoesser
- Nuffield Department of Medicine, Oxford, UK
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford, UK
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10
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Yang J, Soltan AAS, Eyre DW, Clifton DA. Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning. NAT MACH INTELL 2023; 5:884-894. [PMID: 37615031 PMCID: PMC10442224 DOI: 10.1038/s42256-023-00697-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 06/27/2023] [Indexed: 08/25/2023]
Abstract
As models based on machine learning continue to be developed for healthcare applications, greater effort is needed to ensure that these technologies do not reflect or exacerbate any unwanted or discriminatory biases that may be present in the data. Here we introduce a reinforcement learning framework capable of mitigating biases that may have been acquired during data collection. In particular, we evaluated our model for the task of rapidly predicting COVID-19 for patients presenting to hospital emergency departments and aimed to mitigate any site (hospital)-specific and ethnicity-based biases present in the data. Using a specialized reward function and training procedure, we show that our method achieves clinically effective screening performances, while significantly improving outcome fairness compared with current benchmarks and state-of-the-art machine learning methods. We performed external validation across three independent hospitals, and additionally tested our method on a patient intensive care unit discharge status task, demonstrating model generalizability.
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Affiliation(s)
- Jenny Yang
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Andrew A. S. Soltan
- John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- RDM Division of Cardiovascular Medicine, University of Oxford, Oxford, UK
| | - David W. Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - David A. Clifton
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
- Oxford-Suzhou Centre for Advanced Research, Suzhou, China
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11
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Wei J, Matthews PC, Stoesser N, Newton JN, Diamond I, Studley R, Taylor N, Bell JI, Farrar J, Kolenchery J, Marsden BD, Hoosdally S, Jones EY, Stuart DI, Crook DW, Peto TEA, Walker AS, Pouwels KB, Eyre DW. Protection against SARS-CoV-2 Omicron BA.4/5 variant following booster vaccination or breakthrough infection in the UK. Nat Commun 2023; 14:2799. [PMID: 37193713 PMCID: PMC10187514 DOI: 10.1038/s41467-023-38275-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 04/21/2023] [Indexed: 05/18/2023] Open
Abstract
Following primary SARS-CoV-2 vaccination, whether boosters or breakthrough infections provide greater protection against SARS-CoV-2 infection is incompletely understood. Here we investigated SARS-CoV-2 antibody correlates of protection against new Omicron BA.4/5 (re-)infections and anti-spike IgG antibody trajectories after a third/booster vaccination or breakthrough infection following second vaccination in 154,149 adults ≥18 y from the United Kingdom general population. Higher antibody levels were associated with increased protection against Omicron BA.4/5 infection and breakthrough infections were associated with higher levels of protection at any given antibody level than boosters. Breakthrough infections generated similar antibody levels to boosters, and the subsequent antibody declines were slightly slower than after boosters. Together our findings show breakthrough infection provides longer-lasting protection against further infections than booster vaccinations. Our findings, considered alongside the risks of severe infection and long-term consequences of infection, have important implications for vaccine policy.
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Affiliation(s)
- Jia Wei
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The Francis Crick Institute, 1 Midland Road, London, UK
- Division of infection and immunity, University College London, London, UK
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - John N Newton
- European Centre for Environment and Human Health, University of Exeter, Truro, UK
| | | | | | | | - John I Bell
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
| | | | - Jaison Kolenchery
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Brian D Marsden
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Sarah Hoosdally
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - E Yvonne Jones
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - David I Stuart
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Derrick W Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
| | - Koen B Pouwels
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - David W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK.
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.
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12
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Dingle KE, Freeman J, Didelot X, Quan TP, Eyre DW, Swann J, Spittal WD, Clark EV, Jolley KA, Walker AS, Wilcox MH, Crook DW. Penicillin Binding Protein Substitutions Cooccur with Fluoroquinolone Resistance in Epidemic Lineages of Multidrug-Resistant Clostridioides difficile. mBio 2023; 14:e0024323. [PMID: 37017518 PMCID: PMC10128037 DOI: 10.1128/mbio.00243-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2023] Open
Abstract
Clostridioides difficile remains a key cause of healthcare-associated infection, with multidrug-resistant (MDR) lineages causing high-mortality (≥20%) outbreaks. Cephalosporin treatment is a long-established risk factor, and antimicrobial stewardship is a key control. A mechanism underlying raised cephalosporin MICs has not been identified in C. difficile, but among other species, this is often acquired via amino acid substitutions in cell wall transpeptidases (penicillin binding proteins [PBPs]). Here, we investigated five C. difficile transpeptidases (PBP1 to PBP5) for recent substitutions, associated cephalosporin MICs, and co-occurrence with fluoroquinolone resistance. Previously published genome assemblies (n = 7,096) were obtained, representing 16 geographically widespread lineages, including healthcare-associated ST1(027). Recent amino acid substitutions were found within PBP1 (n = 50) and PBP3 (n = 48), ranging from 1 to 10 substitutions per genome. β-Lactam MICs were measured for closely related pairs of wild-type and PBP-substituted isolates separated by 20 to 273 single nucleotide polymorphisms (SNPs). Recombination-corrected phylogenies were constructed to date substitution acquisition. Key substitutions such as PBP3 V497L and PBP1 T674I/N/V emerged independently across multiple lineages. They were associated with extremely high cephalosporin MICs; 1 to 4 doubling dilutions >wild-type, up to 1,506 μg/mL. Substitution patterns varied by lineage and clade, showed geographic structure, and occurred post-1990, coincident with the gyrA and/or gyrB substitutions conferring fluoroquinolone resistance. In conclusion, recent PBP1 and PBP3 substitutions are associated with raised cephalosporin MICs in C. difficile. Their co-occurrence with fluoroquinolone resistance hinders attempts to understand the relative importance of these drugs in the dissemination of epidemic lineages. Further controlled studies of cephalosporin and fluoroquinolone stewardship are needed to determine their relative effectiveness in outbreak control. IMPORTANCE Fluoroquinolone and cephalosporin use in healthcare settings has triggered outbreaks of high-mortality, multidrug-resistant C. difficile infection. Here, we identify a mechanism associated with raised cephalosporin MICs in C. difficile comprising amino acid substitutions in two cell wall transpeptidase enzymes (penicillin binding proteins). The higher the number of substitutions, the greater the impact on phenotype. Dated phylogenies revealed that substitutions associated with raised cephalosporin and fluoroquinolone MICs were co-acquired immediately before clinically important outbreak strains emerged. PBP substitutions were geographically structured within genetic lineages, suggesting adaptation to local antimicrobial prescribing. Antimicrobial stewardship of cephalosporins and fluoroquinolones is an effective means of C. difficile outbreak control. Genetic changes associated with raised MIC may impart a "fitness cost" after antibiotic withdrawal. Our study therefore identifies a mechanism that may explain the contribution of cephalosporin stewardship to resolving outbreak conditions. However, due to the co-occurrence of raised cephalosporin MICs and fluoroquinolone resistance, further work is needed to determine the relative importance of each.
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Affiliation(s)
- Kate E Dingle
- Nuffield Department of Clinical Medicine, John Radcliffe Hospital, Oxford University, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, United Kingdom
| | - Jane Freeman
- Department of Microbiology, Leeds Teaching Hospitals Trust, Leeds, United Kingdom
- Healthcare Associated Infections Research Group, The Leeds Institute of Medical Research, University of Leeds, Leeds, United Kingdom
| | - Xavier Didelot
- School of Life Sciences and Department of Statistics, University of Warwick, Coventry, United Kingdom
| | - T Phuong Quan
- Nuffield Department of Clinical Medicine, John Radcliffe Hospital, Oxford University, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, United Kingdom
| | - David W Eyre
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, United Kingdom
- Big Data Institute, Nuffield Department of Population Health, Oxford University of Oxford, Oxford, United Kingdom
| | - Jeremy Swann
- Nuffield Department of Clinical Medicine, John Radcliffe Hospital, Oxford University, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, United Kingdom
| | - William D Spittal
- Department of Microbiology, Leeds Teaching Hospitals Trust, Leeds, United Kingdom
- Healthcare Associated Infections Research Group, The Leeds Institute of Medical Research, University of Leeds, Leeds, United Kingdom
| | - Emma V Clark
- Department of Microbiology, Leeds Teaching Hospitals Trust, Leeds, United Kingdom
- Healthcare Associated Infections Research Group, The Leeds Institute of Medical Research, University of Leeds, Leeds, United Kingdom
| | - Keith A Jolley
- Department of Biology, University of Oxford, Oxford, United Kingdom
| | - A Sarah Walker
- Nuffield Department of Clinical Medicine, John Radcliffe Hospital, Oxford University, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, United Kingdom
| | - Mark H Wilcox
- Department of Microbiology, Leeds Teaching Hospitals Trust, Leeds, United Kingdom
- Healthcare Associated Infections Research Group, The Leeds Institute of Medical Research, University of Leeds, Leeds, United Kingdom
| | - Derrick W Crook
- Nuffield Department of Clinical Medicine, John Radcliffe Hospital, Oxford University, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, United Kingdom
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13
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Li X, Muñoz JF, Gade L, Argimon S, Bougnoux ME, Bowers JR, Chow NA, Cuesta I, Farrer RA, Maufrais C, Monroy-Nieto J, Pradhan D, Uehling J, Vu D, Yeats CA, Aanensen DM, d’Enfert C, Engelthaler DM, Eyre DW, Fisher MC, Hagen F, Meyer W, Singh G, Alastruey-Izquierdo A, Litvintseva AP, Cuomo CA. Comparing genomic variant identification protocols for Candida auris. Microb Genom 2023; 9:mgen000979. [PMID: 37043380 PMCID: PMC10210944 DOI: 10.1099/mgen.0.000979] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 02/09/2023] [Indexed: 04/13/2023] Open
Abstract
Genomic analyses are widely applied to epidemiological, population genetic and experimental studies of pathogenic fungi. A wide range of methods are employed to carry out these analyses, typically without including controls that gauge the accuracy of variant prediction. The importance of tracking outbreaks at a global scale has raised the urgency of establishing high-accuracy pipelines that generate consistent results between research groups. To evaluate currently employed methods for whole-genome variant detection and elaborate best practices for fungal pathogens, we compared how 14 independent variant calling pipelines performed across 35 Candida auris isolates from 4 distinct clades and evaluated the performance of variant calling, single-nucleotide polymorphism (SNP) counts and phylogenetic inference results. Although these pipelines used different variant callers and filtering criteria, we found high overall agreement of SNPs from each pipeline. This concordance correlated with site quality, as SNPs discovered by a few pipelines tended to show lower mapping quality scores and depth of coverage than those recovered by all pipelines. We observed that the major differences between pipelines were due to variation in read trimming strategies, SNP calling methods and parameters, and downstream filtration criteria. We calculated specificity and sensitivity for each pipeline by aligning three isolates with chromosomal level assemblies and found that the GATK-based pipelines were well balanced between these metrics. Selection of trimming methods had a greater impact on SAMtools-based pipelines than those using GATK. Phylogenetic trees inferred by each pipeline showed high consistency at the clade level, but there was more variability between isolates from a single outbreak, with pipelines that used more stringent cutoffs having lower resolution. This project generated two truth datasets useful for routine benchmarking of C. auris variant calling, a consensus VCF of genotypes discovered by 10 or more pipelines across these 35 diverse isolates and variants for 2 samples identified from whole-genome alignments. This study provides a foundation for evaluating SNP calling pipelines and developing best practices for future fungal genomic studies.
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Affiliation(s)
- Xiao Li
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - José F. Muñoz
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Lalitha Gade
- Mycotic Diseases Branch, Centers for Disease Control and Prevention, US Department of Health and Human Services, Atlanta, GA, 30329, USA
| | - Silvia Argimon
- Centre for Genomic Pathogen Surveillance, Big Data Institute, University of Oxford, Oxford, UK
| | - Marie-Elisabeth Bougnoux
- Institut Pasteur, Université Paris Cité, INRAE, USC2019, Unité Biologie et Pathogénicité Fongiques, Paris, France
- Université Paris Cité, Hôpital Necker-Enfants-Malades, Unité de Parasitologie-Mycologie, Assistance Publique des Hôpitaux de Paris, Paris, France
| | - Jolene R. Bowers
- Translational Genomics Research Institute, Pathogen and Microbiome Division, Flagstaff, AZ 86005, USA
| | - Nancy A. Chow
- Mycotic Diseases Branch, Centers for Disease Control and Prevention, US Department of Health and Human Services, Atlanta, GA, 30329, USA
| | - Isabel Cuesta
- Mycology Reference Laboratory, National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain
| | - Rhys A. Farrer
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Medical Research Council Centre for Medical Mycology, University of Exeter, Exeter, EX4 4PY, UK
| | - Corinne Maufrais
- Institut Pasteur, Université Paris Cité, INRAE, USC2019, Unité Biologie et Pathogénicité Fongiques, Paris, France
- Institut Pasteur, Université Paris Cité, CNRS USR 3756, Hub de Bioinformatique et Biostatistique, Paris, France
| | - Juan Monroy-Nieto
- Translational Genomics Research Institute, Pathogen and Microbiome Division, Flagstaff, AZ 86005, USA
| | - Dibyabhaba Pradhan
- All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110029, India
| | - Jessie Uehling
- Botany and Plant Pathology, Oregon State University, Corvallis, OR 97330, USA
| | - Duong Vu
- Westerdijk Fungal Biodiversity Institute, Uppsalalaan 8, 3584CT, Utrecht, Netherlands
| | - Corin A. Yeats
- Centre for Genomic Pathogen Surveillance, Big Data Institute, University of Oxford, Oxford, UK
| | - David M. Aanensen
- Centre for Genomic Pathogen Surveillance, Big Data Institute, University of Oxford, Oxford, UK
| | - Christophe d’Enfert
- Institut Pasteur, Université Paris Cité, INRAE, USC2019, Unité Biologie et Pathogénicité Fongiques, Paris, France
| | - David M. Engelthaler
- Translational Genomics Research Institute, Pathogen and Microbiome Division, Flagstaff, AZ 86005, USA
| | - David W. Eyre
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Matthew C. Fisher
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Ferry Hagen
- Westerdijk Fungal Biodiversity Institute, Uppsalalaan 8, 3584CT, Utrecht, Netherlands
- Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, Amsterdam, Netherlands
- Department of Medical Microbiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Wieland Meyer
- Sydney Medical School, University of Sydney, Sydney, NSW 2050, Australia
| | - Gagandeep Singh
- All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110029, India
| | - Ana Alastruey-Izquierdo
- Mycology Reference Laboratory, National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain
| | - Anastasia P. Litvintseva
- Mycotic Diseases Branch, Centers for Disease Control and Prevention, US Department of Health and Human Services, Atlanta, GA, 30329, USA
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14
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Yang J, Soltan AAS, Eyre DW, Yang Y, Clifton DA. An adversarial training framework for mitigating algorithmic biases in clinical machine learning. NPJ Digit Med 2023; 6:55. [PMID: 36991077 PMCID: PMC10050816 DOI: 10.1038/s41746-023-00805-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 03/13/2023] [Indexed: 03/31/2023] Open
Abstract
Machine learning is becoming increasingly prominent in healthcare. Although its benefits are clear, growing attention is being given to how these tools may exacerbate existing biases and disparities. In this study, we introduce an adversarial training framework that is capable of mitigating biases that may have been acquired through data collection. We demonstrate this proposed framework on the real-world task of rapidly predicting COVID-19, and focus on mitigating site-specific (hospital) and demographic (ethnicity) biases. Using the statistical definition of equalized odds, we show that adversarial training improves outcome fairness, while still achieving clinically-effective screening performances (negative predictive values >0.98). We compare our method to previous benchmarks, and perform prospective and external validation across four independent hospital cohorts. Our method can be generalized to any outcomes, models, and definitions of fairness.
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Affiliation(s)
- Jenny Yang
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, England.
| | - Andrew A S Soltan
- John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, England
- RDM Division of Cardiovascular Medicine, University of Oxford, Oxford, England
| | - David W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, England
| | - Yang Yang
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - David A Clifton
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, England
- Oxford-Suzhou Centre for Advanced Research (OSCAR), Suzhou, China
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15
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Govender KN, Eyre DW. Benchmarking taxonomic classifiers with Illumina and Nanopore sequence data for clinical metagenomic diagnostic applications. Microb Genom 2022; 8. [PMID: 36269282 PMCID: PMC9676057 DOI: 10.1099/mgen.0.000886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Culture-independent metagenomic detection of microbial species has the potential to provide rapid and precise real-time diagnostic results. However, it is potentially limited by sequencing and taxonomic classification errors. We use simulated and real-world data to benchmark rates of species misclassification using 100 reference genomes for each of the ten common bloodstream pathogens and six frequent blood-culture contaminants (n=1568, only 68 genomes were available for Micrococcus luteus). Simulating both with and without sequencing error for both the Illumina and Oxford Nanopore platforms, we evaluated commonly used classification tools including Kraken2, Bracken and Centrifuge, utilizing mini (8 GB) and standard (30–50 GB) databases. Bracken with the standard database performed best, the median percentage of reads across both sequencing platforms identified correctly to the species level was 97.8% (IQR 92.7:99.0) [range 5:100]. For Kraken2 with a mini database, a commonly used combination, median species-level identification was 86.4% (IQR 50.5:93.7) [range 4.3:100]. Classification performance varied by species, with Escherichia coli being more challenging to classify correctly (probability of reads being assigned to the correct species: 56.1–96.0%, varying by tool used). Human read misclassification was negligible. By filtering out shorter Nanopore reads we found performance similar or superior to Illumina sequencing, despite higher sequencing error rates. Misclassification was more common when the misclassified species had a higher average nucleotide identity to the true species. Our findings highlight taxonomic misclassification of sequencing data occurs and varies by sequencing and analysis workflow. To account for ‘bioinformatic contamination’ we present a contamination catalogue that can be used in metagenomic pipelines to ensure accurate results that can support clinical decision making.
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Affiliation(s)
- Kumeren N Govender
- Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - David W Eyre
- Nuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK.,Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
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16
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Wellens J, Edmans M, Obolski U, McGregor CG, Simmonds P, Turner M, Jarvis L, Skelly D, Dunachie S, Barnes E, Eyre DW, Colombel JF, Wong SY, Klenerman P, Lindsay JO, Satsangi J, Thompson CP. Combination therapy of infliximab and thiopurines, but not monotherapy with infliximab or vedolizumab, is associated with attenuated IgA and neutralisation responses to SARS-CoV-2 in inflammatory bowel disease. Gut 2022; 71:1919-1922. [PMID: 34911744 DOI: 10.1136/gutjnl-2021-326312] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 11/06/2021] [Indexed: 12/08/2022]
Affiliation(s)
- Judith Wellens
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, KU Leuven, Leuven, Belgium.,Translational Gastroenterology Unit, University of Oxford, Oxford, UK
| | - Matthew Edmans
- Translational Gastroenterology Unit, University of Oxford, Oxford, UK.,Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Uri Obolski
- School of Public Health, Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.,Porter School of Environmental and Earth Sciences, Faculty of Exact Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | | | - Peter Simmonds
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Marc Turner
- National Microbiology Reference Unit, Scottish National Blood Transfusion Service, Edinburgh, Edinburgh, UK
| | - Lisa Jarvis
- National Microbiology Reference Unit, Scottish National Blood Transfusion Service, Edinburgh, Edinburgh, UK
| | - Donal Skelly
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Susanna Dunachie
- Department of Microbiology/Infectious Diseases, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.,Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
| | - Eleanor Barnes
- Translational Gastroenterology Unit, University of Oxford, Oxford, UK.,Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - David W Eyre
- Department of Microbiology/Infectious Diseases, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Serre-Yu Wong
- The Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Mount Sinai School of Medicine, New York, New York, USA
| | - Paul Klenerman
- Translational Gastroenterology Unit, University of Oxford, Oxford, UK.,Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - James O Lindsay
- Centre for Immunobiology, Blizard Institute, Queen Mary University of London, London, UK
| | - Jack Satsangi
- Translational Gastroenterology Unit, University of Oxford, Oxford, UK
| | - Craig P Thompson
- Warwick Medical School, University of Warwick, Coventry, UK .,Department of Zoology, University of Oxford, Oxford, UK
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17
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Vihta KD, Pouwels KB, Peto TEA, Pritchard E, Eyre DW, House T, Gethings O, Studley R, Rourke E, Cook D, Diamond I, Crook D, Matthews PC, Stoesser N, Walker AS. Symptoms and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Positivity in the General Population in the United Kingdom. Clin Infect Dis 2022; 75:e329-e337. [PMID: 34748629 PMCID: PMC8767848 DOI: 10.1093/cid/ciab945] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND "Classic" symptoms (cough, fever, loss of taste/smell) prompt severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) polymerase chain reaction (PCR) testing in the United Kingdom. Studies have assessed the ability of different symptoms to identify infection, but few have compared symptoms over time (reflecting variants) and by vaccination status. METHODS Using the COVID-19 Infection Survey, sampling households across the United Kingdom, we compared symptoms in PCR-positives vs PCR-negatives, evaluating sensitivity of combinations of 12 symptoms (percentage symptomatic PCR-positives reporting specific symptoms) and tests per case (TPC) (PCR-positives or PCR-negatives reporting specific symptoms/ PCR-positives reporting specific symptoms). RESULTS Between April 2020 and August 2021, 27 869 SARS-CoV-2 PCR-positive episodes occurred in 27 692 participants (median 42 years), of whom 13 427 (48%) self-reported symptoms ("symptomatic PCR-positives"). The comparator comprised 3 806 692 test-negative visits (457 215 participants); 130 612 (3%) self-reported symptoms ("symptomatic PCR-negatives"). Symptom reporting in PCR-positives varied by age, sex, and ethnicity, and over time, reflecting changes in prevalence of viral variants, incidental changes (eg, seasonal pathogens (with sore throat increasing in PCR-positives and PCR-negatives from April 2021), schools reopening) and vaccination rollout. After May 2021 when Delta emerged, headache and fever substantially increased in PCR-positives, but not PCR-negatives. Sensitivity of symptom-based detection increased from 74% using "classic" symptoms, to 81% adding fatigue/weakness, and 90% including all 8 additional symptoms. However, this increased TPC from 4.6 to 5.3 to 8.7. CONCLUSIONS Expanded symptom combinations may provide modest benefits for sensitivity of PCR-based case detection, but this will vary between settings and over time, and increases tests/case. Large-scale changes to targeted PCR-testing approaches require careful evaluation given substantial resource and infrastructure implications.
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Affiliation(s)
- Karina Doris Vihta
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- Department of Engineering, University of Oxford, Oxford, United Kingdom
| | - Koen B Pouwels
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Emma Pritchard
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
| | - David W Eyre
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
- IBM Research, Hartree Centre, Sci-Tech Daresbury, United Kingdom
| | - Owen Gethings
- Office for National Statistics, Newport, United Kingdom
| | - Ruth Studley
- Office for National Statistics, Newport, United Kingdom
| | - Emma Rourke
- Office for National Statistics, Newport, United Kingdom
| | - Duncan Cook
- Office for National Statistics, Newport, United Kingdom
| | - Ian Diamond
- Office for National Statistics, Newport, United Kingdom
| | - Derrick Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Ann Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
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18
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Vihta KD, Pouwels KB, Peto TEA, Pritchard E, House T, Studley R, Rourke E, Cook D, Diamond I, Crook D, Clifton DA, Matthews PC, Stoesser N, Eyre DW, Walker AS. Omicron-associated changes in SARS-CoV-2 symptoms in the United Kingdom. Clin Infect Dis 2022; 76:ciac613. [PMID: 35917440 PMCID: PMC9384604 DOI: 10.1093/cid/ciac613] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 07/14/2022] [Accepted: 07/22/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND The SARS-CoV-2 Delta variant has been replaced by the highly transmissible Omicron BA.1 variant, and subsequently by Omicron BA.2. It is important to understand how these changes in dominant variants affect reported symptoms, while also accounting for symptoms arising from other co-circulating respiratory viruses. METHODS In a nationally representative UK community study, the COVID-19 Infection Survey, we investigated symptoms in PCR-positive infection episodes vs. PCR-negative study visits over calendar time, by age and vaccination status, comparing periods when the Delta, Omicron BA.1 and BA.2 variants were dominant. RESULTS Between October-2020 and April-2022, 120,995 SARS-CoV-2 PCR-positive episodes occurred in 115,886 participants, with 70,683 (58%) reporting symptoms. The comparator comprised 4,766,366 PCR-negative study visits (483,894 participants); 203,422 (4%) reporting symptoms. Symptom reporting in PCR-positives varied over time, with a marked reduction in loss of taste/smell as Omicron BA.1 dominated, maintained with BA.2 (44%/45% 17 October 2021, 16%/13% 2 January 2022, 15%/12% 27 March 2022). Cough, fever, shortness of breath, myalgia, fatigue/weakness and headache also decreased after Omicron BA.1 dominated, but sore throat increased, the latter to a greater degree than concurrent increases in PCR-negatives. Fatigue/weakness increased again after BA.2 dominated, although to a similar degree to concurrent increases in PCR-negatives. Symptoms were consistently more common in adults aged 18-65 years than in children or older adults. CONCLUSIONS Increases in sore throat (also common in the general community), and a marked reduction in loss of taste/smell, make Omicron harder to detect with symptom-based testing algorithms, with implications for institutional and national testing policies.
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Affiliation(s)
- Karina-Doris Vihta
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- Department of Engineering, University of Oxford, Oxford, United Kingdom
| | - Koen B Pouwels
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Emma Pritchard
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, United Kingdom
- IBM Research, Hartree Centre, Sci-Tech Daresbury, Daresbury, United Kingdom
| | - Ruth Studley
- Office for National Statistics, Newport, United Kingdom
| | - Emma Rourke
- Office for National Statistics, Newport, United Kingdom
| | - Duncan Cook
- Office for National Statistics, Newport, United Kingdom
| | - Ian Diamond
- Office for National Statistics, Newport, United Kingdom
| | - Derrick Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - David A Clifton
- Department of Engineering, University of Oxford, Oxford, United Kingdom
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Francis Crick Institute, London, United Kingdom
- Division of Infection and Immunity, University College London, London, United Kingdom
- Department of Infection, University College London Hospitals, London, United Kingdom
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - David W Eyre
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Ann Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United Kingdom
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
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19
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Gu Q, Jones N, Drennan P, Peto TE, Walker AS, Eyre DW. Assessment of an institutional guideline for vancomycin dosing and identification of predictive factors associated with dose and drug trough levels. J Infect 2022; 85:382-389. [PMID: 35840011 DOI: 10.1016/j.jinf.2022.06.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 06/16/2022] [Accepted: 06/28/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVES To evaluate the effectiveness of an antimicrobial guideline for vancomycin prescribing deployed using electronic prescribing aid and web/phone-based app. To define factors associated with guideline compliance and drug levels, and to investigate if antimicrobial dosing recommendations can be refined using routinely collected electronic healthcare record data. METHODS We used data from Oxford University Hospitals between 01-January-2016 and 01-June-2021 and multivariable regression models to investigate factors associated with dosing compliance, drug levels and acute kidney injury (AKI). RESULTS 3767 patients received intravenous vancomycin for ≥24 h. Compliance with recommended loading and initial maintenance doses reached 84% and 70% respectively; 72% of subsequent maintenance doses were correctly adjusted. However, only 26% first and 32% subsequent levels reached the target range, and for patients with ongoing vancomycin treatment, 55-63% achieved target levels at 5 days. Drug levels were independently higher in older patients. Incidence of AKI was low (5.7%). Model estimates were used to propose updated age, weight and eGFR specific guidelines. CONCLUSION Despite good compliance with guidelines for vancomycin dosing, the proportion of drug levels achieving the target range remained suboptimal. Routinely collected electronic data can be used at scale to inform pharmacokinetic studies and could improve vancomycin dosing.
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Affiliation(s)
- Qingze Gu
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Nicola Jones
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Philip Drennan
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Tim Ea Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom; Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom
| | - David W Eyre
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom; Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom.
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20
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McNaughton AL, Paton RS, Edmans M, Youngs J, Wellens J, Phalora P, Fyfe A, Belij-Rammerstorfer S, Bolton JS, Ball J, Carnell GW, Dejnirattisai W, Dold C, Eyre DW, Hopkins P, Howarth A, Kooblall K, Klim H, Leaver S, Lee LN, López-Camacho C, Lumley SF, Macallan DC, Mentzer AJ, Provine NM, Ratcliff J, Slon-Compos J, Skelly D, Stolle L, Supasa P, Temperton N, Walker C, Wang B, Wyncoll D, Simmonds P, Lambe T, Baillie JK, Semple MG, Openshaw PJ, Obolski U, Turner M, Carroll M, Mongkolsapaya J, Screaton G, Kennedy SH, Jarvis L, Barnes E, Dunachie S, Lourenço J, Matthews PC, Bicanic T, Klenerman P, Gupta S, Thompson CP. Fatal COVID-19 outcomes are associated with an antibody response targeting epitopes shared with endemic coronaviruses. JCI Insight 2022; 7:156372. [PMID: 35608920 PMCID: PMC9310533 DOI: 10.1172/jci.insight.156372] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 05/18/2022] [Indexed: 11/17/2022] Open
Abstract
The role of immune responses to previously seen endemic coronavirus epitopes in severe acute respiratory coronavirus 2 (SARS-CoV-2) infection and disease progression has not yet been determined. Here, we show that a key characteristic of fatal outcomes with coronavirus disease 2019 (COVID-19) is that the immune response to the SARS-CoV-2 spike protein is enriched for antibodies directed against epitopes shared with endemic beta-coronaviruses and has a lower proportion of antibodies targeting the more protective variable regions of the spike. The magnitude of antibody responses to the SARS-CoV-2 full-length spike protein, its domains and subunits, and the SARS-CoV-2 nucleocapsid also correlated strongly with responses to the endemic beta-coronavirus spike proteins in individuals admitted to an intensive care unit (ICU) with fatal COVID-19 outcomes, but not in individuals with nonfatal outcomes. This correlation was found to be due to the antibody response directed at the S2 subunit of the SARS-CoV-2 spike protein, which has the highest degree of conservation between the beta-coronavirus spike proteins. Intriguingly, antibody responses to the less cross-reactive SARS-CoV-2 nucleocapsid were not significantly different in individuals who were admitted to an ICU with fatal and nonfatal outcomes, suggesting an antibody profile in individuals with fatal outcomes consistent with an "original antigenic sin" type response.
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Affiliation(s)
- Anna L. McNaughton
- Peter Medawar Building for Pathogen Research
- Nuffield Department of Medicine, and
| | - Robert S. Paton
- Peter Medawar Building for Pathogen Research
- Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Matthew Edmans
- Peter Medawar Building for Pathogen Research
- Nuffield Department of Medicine, and
- Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Jonathan Youngs
- Institute of Infection & Immunity, St George’s University of London, London, United Kingdom
| | - Judith Wellens
- Peter Medawar Building for Pathogen Research
- Translational Gastroenterology Unit, Experimental Medicine Division, Nuffield Department of Medicine, John Radcliffe Hospital, Oxford, United Kingdom
- Translational Research for Gastrointestinal Diseases, University Hospitals Leuven, Leuven, Belgium
| | - Prabhjeet Phalora
- Peter Medawar Building for Pathogen Research
- Nuffield Department of Medicine, and
| | - Alex Fyfe
- Peter Medawar Building for Pathogen Research
- Department of Zoology, University of Oxford, Oxford, United Kingdom
| | | | - Jai S. Bolton
- Peter Medawar Building for Pathogen Research
- Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Jonathan Ball
- General Intensive Care service, St George’s University Hospital National Health Service (NHS) Trust, London, United Kingdom
| | - George W. Carnell
- Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom
| | | | | | - David W. Eyre
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Philip Hopkins
- Centre for Human & Applied Physiological Sciences, School of Basic & Medical Biosciences, Faculty of Life Sciences & Medicine, King’s College, London, United Kingdom
| | - Alison Howarth
- Department of Microbiology/Infectious Diseases, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Kreepa Kooblall
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Churchill Hospital, and
| | - Hannah Klim
- Peter Medawar Building for Pathogen Research
- Department of Zoology, University of Oxford, Oxford, United Kingdom
- Future of Humanity Institute, Department of Philosophy, and
| | - Susannah Leaver
- General Intensive Care service, St George’s University Hospital National Health Service (NHS) Trust, London, United Kingdom
| | - Lian Ni Lee
- Peter Medawar Building for Pathogen Research
- Nuffield Department of Medicine, and
| | | | - Sheila F. Lumley
- Peter Medawar Building for Pathogen Research
- Nuffield Department of Medicine, and
- Department of Microbiology/Infectious Diseases, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Derek C. Macallan
- Institute of Infection & Immunity, St George’s University of London, London, United Kingdom
| | | | - Nicholas M. Provine
- Translational Gastroenterology Unit, Experimental Medicine Division, Nuffield Department of Medicine, John Radcliffe Hospital, Oxford, United Kingdom
| | - Jeremy Ratcliff
- Peter Medawar Building for Pathogen Research
- Nuffield Department of Medicine, and
| | - Jose Slon-Compos
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine
| | - Donal Skelly
- Peter Medawar Building for Pathogen Research
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Lucas Stolle
- Department of Biochemistry, University of Oxford, Oxford, United Kingdom
| | - Piyada Supasa
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine
| | - Nigel Temperton
- Viral Pseudotype Unit, Medway School of Pharmacy, University of Kent, Chatham, United Kingdom
| | - Chris Walker
- Meso Scale Diagnostics, Rockville, Maryland, USA
| | - Beibei Wang
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine
| | - Duncan Wyncoll
- Intensive Care Medicine, Guy’s and St Thomas’ Hospital NHS Foundation Trust, London, United Kingdom
| | | | | | - Peter Simmonds
- Peter Medawar Building for Pathogen Research
- Nuffield Department of Medicine, and
| | - Teresa Lambe
- The Jenner Institute Laboratories, University of Oxford, Oxford, United Kingdom
| | | | - Malcolm G. Semple
- NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, United Kingdom
| | | | | | - Uri Obolski
- School of Public Health, Faculty of Medicine, and
- Porter School of the Environment and Earth Sciences, Faculty of Exact Sciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Marc Turner
- National Microbiology Reference Unit, Scottish National Blood Transfusion Service, Edinburgh, United Kingdom
| | - Miles Carroll
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine
- National Infection Service, Public Health England (PHE), Salisbury, United Kingdom
| | - Juthathip Mongkolsapaya
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine
- Siriraj Center of Research for Excellence in Dengue & Emerging Pathogens, Faculty of Medicine Siriraj Hospital, Mahidol University, Thailand
- Chinese Academy of Medical Science (CAMS) Oxford Institute (COI), University of Oxford, Oxford, United Kingdom
| | - Gavin Screaton
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine
- Chinese Academy of Medical Science (CAMS) Oxford Institute (COI), University of Oxford, Oxford, United Kingdom
| | - Stephen H. Kennedy
- Nuffield Department of Women’s & Reproductive Health, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - Lisa Jarvis
- National Microbiology Reference Unit, Scottish National Blood Transfusion Service, Edinburgh, United Kingdom
| | - Eleanor Barnes
- Peter Medawar Building for Pathogen Research
- Nuffield Department of Medicine, and
- Translational Gastroenterology Unit, Experimental Medicine Division, Nuffield Department of Medicine, John Radcliffe Hospital, Oxford, United Kingdom
| | - Susanna Dunachie
- Peter Medawar Building for Pathogen Research
- Department of Microbiology/Infectious Diseases, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - José Lourenço
- Peter Medawar Building for Pathogen Research
- Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Philippa C. Matthews
- Peter Medawar Building for Pathogen Research
- Nuffield Department of Medicine, and
- Department of Microbiology/Infectious Diseases, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Tihana Bicanic
- Institute of Infection & Immunity, St George’s University of London, London, United Kingdom
| | - Paul Klenerman
- Peter Medawar Building for Pathogen Research
- Nuffield Department of Medicine, and
- Translational Research for Gastrointestinal Diseases, University Hospitals Leuven, Leuven, Belgium
| | - Sunetra Gupta
- Peter Medawar Building for Pathogen Research
- Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Craig P. Thompson
- Peter Medawar Building for Pathogen Research
- Department of Zoology, University of Oxford, Oxford, United Kingdom
- Division of Biomedical Sciences, Warwick Medical School, University of Warwick, Coventry, United Kingdom
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21
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Wei J, Matthews PC, Stoesser N, Diamond I, Studley R, Rourke E, Cook D, Bell JI, Newton JN, Farrar J, Howarth A, Marsden BD, Hoosdally S, Jones EY, Stuart DI, Crook DW, Peto TEA, Walker AS, Eyre DW, Pouwels KB. SARS-CoV-2 antibody trajectories after a single COVID-19 vaccination with and without prior infection. Nat Commun 2022; 13:3748. [PMID: 35768431 PMCID: PMC9243074 DOI: 10.1038/s41467-022-31495-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 06/17/2022] [Indexed: 11/10/2022] Open
Abstract
Given high SARS-CoV-2 incidence, coupled with slow and inequitable vaccine roll-out in many settings, there is a need for evidence to underpin optimum vaccine deployment, aiming to maximise global population immunity. We evaluate whether a single vaccination in individuals who have already been infected with SARS-CoV-2 generates similar initial and subsequent antibody responses to two vaccinations in those without prior infection. We compared anti-spike IgG antibody responses after a single vaccination with ChAdOx1, BNT162b2, or mRNA-1273 SARS-CoV-2 vaccines in the COVID-19 Infection Survey in the UK general population. In 100,849 adults median (50 (IQR: 37-63) years) receiving at least one vaccination, 13,404 (13.3%) had serological/PCR evidence of prior infection. Prior infection significantly boosted antibody responses, producing higher peak levels and/or longer half-lives after one dose of all three vaccines than those without prior infection receiving one or two vaccinations. In those with prior infection, the median time above the positivity threshold was >1 year after the first vaccination. Single-dose vaccination targeted to those previously infected may provide at least as good protection to two-dose vaccination among those without previous infection.
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Affiliation(s)
- Jia Wei
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The Francis Crick Institute, 1 Midland Road, London, UK
- Division of infection and immunity, University College London, London, UK
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | | | | | | | | | - John I Bell
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
| | - John N Newton
- European Centre for Environment and Human Health, University of Exeter, Truro, UK
| | | | - Alison Howarth
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Brian D Marsden
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Sarah Hoosdally
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - E Yvonne Jones
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - David I Stuart
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Derrick W Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
| | - David W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Koen B Pouwels
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK.
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
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22
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Tsakok MT, Watson RA, Saujani SJ, Kong M, Xie C, Peschl H, Wing L, MacLeod FK, Shine B, Talbot NP, Benamore RE, Eyre DW, Gleeson F. Reduction in Chest CT Severity and Improved Hospital Outcomes in SARS-CoV-2 Omicron Compared with Delta Variant Infection. Radiology 2022; 306:261-269. [PMID: 35727150 PMCID: PMC9272784 DOI: 10.1148/radiol.220533] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Background The SARS-Cov-2 Omicron variant demonstrates rapid spread but reduced disease severity. Studies evaluating lung imaging findings of Omicron infection versus non-Omicron infection remain lacking. Purpose To compare the Omicron variant with the SARS-CoV-2 Delta variant according to their chest CT radiologic pattern, biochemical parameters, clinical severity, and hospital outcomes after adjusting for vaccination status. Materials and Methods This retrospective study included hospitalized adult patients with reverse transcriptase-polymerase chain reaction test results positive for SARS-CoV-2, with CT pulmonary angiography performed within 7 days of admission between December 1, 2021, and January 14, 2022. Multiple readers performed blinded radiologic analyses that included RSNA CT classification, chest CT severity score (CTSS) (range, 0 [least severe] to 25 [most severe]), and CT imaging features, including bronchial wall thickening. Results A total of 106 patients (Delta group, n = 66; Omicron group, n = 40) were evaluated (overall mean age, 58 years ± 18 [SD]; 58 men). In the Omicron group, 37% of CT pulmonary angiograms (15 of 40 patients) were categorized as normal compared with 15% (10 of 66 patients) of angiograms in the Delta group (P = .016). A generalized linear model was used to control for confounding variables, including vaccination status, and Omicron infection was associated with a CTSS that was 7.2 points lower than that associated with Delta infection (β = -7.2; 95% CI: -9.9, -4.5; P < .001). Bronchial wall thickening was more common with Omicron infection than with Delta infection (odds ratio [OR], 2.4; 95% CI: 1.01, 5.92; P = .04). A booster shot was associated with a protective effect for chest infection (median CTSS, 5; IQR, 0-11) when compared with unvaccinated individuals (median CTSS, 11; IQR, 7.5-14.0) (P = .03). The Delta variant was associated with a higher OR of severe disease (OR, 4.6; 95% CI: 1.2, 26; P = .01) and admission to a critical care unit (OR, 7.0; 95% CI: 1.5, 66; P = .004) when compared with the Omicron variant. Conclusion The SARS-CoV-2 Omicron variant was associated with fewer and less severe changes on chest CT images compared with the Delta variant. Patients with Omicron infection had greater frequency of bronchial wall thickening but less severe disease and improved hospital outcomes when compared with patients with Delta infection. © RSNA, 2022 Online supplemental material is available for this article.
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Affiliation(s)
- Maria T. Tsakok
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
| | - Robert A. Watson
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
| | - Shyamal J. Saujani
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
| | - Mark Kong
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
| | - Cheng Xie
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
| | - Heiko Peschl
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
| | - Louise Wing
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
| | - Fiona K. MacLeod
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
| | - Brian Shine
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
| | - Nicholas P. Talbot
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
| | - Rachel E. Benamore
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
| | - David W. Eyre
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
| | - Fergus Gleeson
- From the Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford OX3 9DU, United Kingdom (M.T.T., R.A.W., S.J.S., M.K., C.X., H.P., L.W., F.K.M., B.S., N.P.T., R.E.B., D.W.E., F.G.); and Weatherall Institute of Molecular Medicine (R.A.W.), Department of Oncology (R.A.W.), Department of Physiology, Anatomy and Genetics (N.P.T.), and Big Data Institute, Nuffield Department of Population Health (D.W.E.), University of Oxford, Oxford, United Kingdom
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23
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Yoon CH, Bartlett S, Stoesser N, Pouwels KB, Jones N, Crook DW, Peto TEA, Walker AS, Eyre DW. Mortality risks associated with empirical antibiotic activity in Escherichia coli bacteraemia: an analysis of electronic health records. J Antimicrob Chemother 2022; 77:2536-2545. [PMID: 35723965 PMCID: PMC9410673 DOI: 10.1093/jac/dkac189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 05/17/2022] [Indexed: 11/14/2022] Open
Abstract
Background Reported bacteraemia outcomes following inactive empirical antibiotics (based on in vitro testing) are conflicting, potentially reflecting heterogeneity in causative species, MIC breakpoints defining resistance/susceptibility, and times to rescue therapy. Methods We investigated adult inpatients with Escherichia coli bacteraemia at Oxford University Hospitals, UK, from 4 February 2014 to 30 June 2021 who were receiving empirical amoxicillin/clavulanate with/without other antibiotics. We used Cox regression to analyse 30 day all-cause mortality by in vitro amoxicillin/clavulanate susceptibility (activity) using the EUCAST resistance breakpoint (>8/2 mg/L), categorical MIC, and a higher resistance breakpoint (>32/2 mg/L), adjusting for other antibiotic activity and confounders including comorbidities, vital signs and blood tests. Results A total of 1720 E. coli bacteraemias (1626 patients) were treated with empirical amoxicillin/clavulanate. Thirty-day mortality was 193/1400 (14%) for any active baseline therapy and 52/320 (16%) for inactive baseline therapy (P = 0.17). With EUCAST breakpoints, there was no evidence that mortality differed for inactive versus active amoxicillin/clavulanate [adjusted HR (aHR) = 1.27 (95% CI 0.83–1.93); P = 0.28], nor of an association with active aminoglycoside (P = 0.93) or other active antibiotics (P = 0.18). Considering categorical amoxicillin/clavulanate MIC, MICs > 32/2 mg/L were associated with mortality [aHR = 1.85 versus MIC = 2/2 mg/L (95% CI 0.99–3.73); P = 0.054]. A higher resistance breakpoint (>32/2 mg/L) was independently associated with higher mortality [aHR = 1.82 (95% CI 1.07–3.10); P = 0.027], as were MICs > 32/2 mg/L with active empirical aminoglycosides [aHR = 2.34 (95% CI 1.40–3.89); P = 0.001], but not MICs > 32/2 mg/L with active non-aminoglycoside antibiotic(s) [aHR = 0.87 (95% CI 0.40–1.89); P = 0.72]. Conclusions We found no evidence that EUCAST-defined amoxicillin/clavulanate resistance was associated with increased mortality, but a higher resistance breakpoint (MIC > 32/2 mg/L) was. Additional active baseline non-aminoglycoside antibiotics attenuated amoxicillin/clavulanate resistance-associated mortality, but aminoglycosides did not. Granular phenotyping and comparison with clinical outcomes may improve AMR breakpoints.
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Affiliation(s)
- Chang Ho Yoon
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, UK.,Nuffield Department of Medicine, University of Oxford, UK
| | - Sean Bartlett
- Nuffield Department of Medicine, University of Oxford, UK
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of Oxford, UK.,Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.,Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, UK
| | - Koen B Pouwels
- Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, UK.,Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Nicola Jones
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Derrick W Crook
- Nuffield Department of Medicine, University of Oxford, UK.,Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.,Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, UK
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, UK.,Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.,Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, UK
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, UK.,The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.,Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, UK
| | - David W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, UK.,Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.,Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, UK
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24
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Wei J, Pouwels KB, Stoesser N, Matthews PC, Diamond I, Studley R, Rourke E, Cook D, Bell JI, Newton JN, Farrar J, Howarth A, Marsden BD, Hoosdally S, Jones EY, Stuart DI, Crook DW, Peto TEA, Walker AS, Eyre DW. Antibody responses and correlates of protection in the general population after two doses of the ChAdOx1 or BNT162b2 vaccines. Nat Med 2022; 28:1072-1082. [PMID: 35165453 PMCID: PMC9117148 DOI: 10.1038/s41591-022-01721-6] [Citation(s) in RCA: 107] [Impact Index Per Article: 53.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 01/27/2022] [Indexed: 12/25/2022]
Abstract
Antibody responses are an important part of immunity after Coronavirus Disease 2019 (COVID-19) vaccination. However, antibody trajectories and the associated duration of protection after a second vaccine dose remain unclear. In this study, we investigated anti-spike IgG antibody responses and correlates of protection after second doses of ChAdOx1 or BNT162b2 vaccines for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the United Kingdom general population. In 222,493 individuals, we found significant boosting of anti-spike IgG by the second doses of both vaccines in all ages and using different dosing intervals, including the 3-week interval for BNT162b2. After second vaccination, BNT162b2 generated higher peak levels than ChAdOX1. Older individuals and males had lower peak levels with BNT162b2 but not ChAdOx1, whereas declines were similar across ages and sexes with ChAdOX1 or BNT162b2. Prior infection significantly increased antibody peak level and half-life with both vaccines. Anti-spike IgG levels were associated with protection from infection after vaccination and, to an even greater degree, after prior infection. At least 67% protection against infection was estimated to last for 2-3 months after two ChAdOx1 doses, for 5-8 months after two BNT162b2 doses in those without prior infection and for 1-2 years for those unvaccinated after natural infection. A third booster dose might be needed, prioritized to ChAdOx1 recipients and those more clinically vulnerable.
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Affiliation(s)
- Jia Wei
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Koen B Pouwels
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | | | | | | | | | - John I Bell
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
| | - John N Newton
- Health Improvement Directorate, Public Health England, London, UK
| | | | - Alison Howarth
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Brian D Marsden
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Sarah Hoosdally
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - E Yvonne Jones
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - David I Stuart
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Derrick W Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, University College London, London, UK
| | - David W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK.
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.
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Lumley SF, Rodger G, Constantinides B, Sanderson N, Chau KK, Street TL, O’Donnell D, Howarth A, Hatch SB, Marsden BD, Cox S, James T, Warren F, Peck LJ, Ritter TG, de Toledo Z, Warren L, Axten D, Cornall RJ, Jones EY, Stuart DI, Screaton G, Ebner D, Hoosdally S, Chand M, Crook DW, O’Donnell AM, Conlon CP, Pouwels KB, Walker AS, Peto TEA, Hopkins S, Walker TM, Stoesser NE, Matthews PC, Jeffery K, Eyre DW. An Observational Cohort Study on the Incidence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection and B.1.1.7 Variant Infection in Healthcare Workers by Antibody and Vaccination Status. Clin Infect Dis 2022; 74:1208-1219. [PMID: 34216472 PMCID: PMC8994591 DOI: 10.1093/cid/ciab608] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Natural and vaccine-induced immunity will play a key role in controlling the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. SARS-CoV-2 variants have the potential to evade natural and vaccine-induced immunity. METHODS In a longitudinal cohort study of healthcare workers (HCWs) in Oxfordshire, United Kingdom, we investigated the protection from symptomatic and asymptomatic polymerase chain reaction (PCR)-confirmed SARS-CoV-2 infection conferred by vaccination (Pfizer-BioNTech BNT162b2, Oxford-AstraZeneca ChAdOx1 nCOV-19) and prior infection (determined using anti-spike antibody status), using Poisson regression adjusted for age, sex, temporal changes in incidence and role. We estimated protection conferred after 1 versus 2 vaccinations and from infections with the B.1.1.7 variant identified using whole genome sequencing. RESULTS In total, 13 109 HCWs participated; 8285 received the Pfizer-BioNTech vaccine (1407 two doses), and 2738 the Oxford-AstraZeneca vaccine (49 two doses). Compared to unvaccinated seronegative HCWs, natural immunity and 2 vaccination doses provided similar protection against symptomatic infection: no HCW vaccinated twice had symptomatic infection, and incidence was 98% lower in seropositive HCWs (adjusted incidence rate ratio 0.02 [95% confidence interval {CI} < .01-.18]). Two vaccine doses or seropositivity reduced the incidence of any PCR-positive result with or without symptoms by 90% (0.10 [95% CI .02-.38]) and 85% (0.15 [95% CI .08-.26]), respectively. Single-dose vaccination reduced the incidence of symptomatic infection by 67% (0.33 [95% CI .21-.52]) and any PCR-positive result by 64% (0.36 [95% CI .26-.50]). There was no evidence of differences in immunity induced by natural infection and vaccination for infections with S-gene target failure and B.1.1.7. CONCLUSIONS Natural infection resulting in detectable anti-spike antibodies and 2 vaccine doses both provide robust protection against SARS-CoV-2 infection, including against the B.1.1.7 variant.
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Affiliation(s)
- Sheila F Lumley
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Gillian Rodger
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Bede Constantinides
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Nicholas Sanderson
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
| | - Kevin K Chau
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Teresa L Street
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
| | - Denise O’Donnell
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Alison Howarth
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Stephanie B Hatch
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Brian D Marsden
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Kennedy Institute of Rheumatology Research, University of Oxford, United Kingdom
| | - Stuart Cox
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Tim James
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Fiona Warren
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Liam J Peck
- Medical School, University of Oxford, Oxford, United Kingdom
| | - Thomas G Ritter
- Medical School, University of Oxford, Oxford, United Kingdom
| | - Zoe de Toledo
- Medical School, University of Oxford, Oxford, United Kingdom
| | - Laura Warren
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - David Axten
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Richard J Cornall
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - E Yvonne Jones
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - David I Stuart
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Gavin Screaton
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Daniel Ebner
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Target Discovery Institute, University of Oxford, Oxford, United Kingdom
| | - Sarah Hoosdally
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Meera Chand
- National Infection Service, Public Health England Colindale, United Kingdom
| | - Derrick W Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Anne-Marie O’Donnell
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | | | - Koen B Pouwels
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Susan Hopkins
- National Infection Service, Public Health England Colindale, United Kingdom
| | - Timothy M Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Nicole E Stoesser
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Philippa C Matthews
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Katie Jeffery
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - David W Eyre
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Big Data Institute, University of Oxford, Oxford, United Kingdom
- Correspondence: D. Eyre, Microbiology Department, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK ()
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Moore MP, Wilcox MH, Walker AS, Eyre DW. K-mer based prediction of Clostridioides difficile relatedness and ribotypes. Microb Genom 2022; 8. [PMID: 35384833 PMCID: PMC9453075 DOI: 10.1099/mgen.0.000804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Comparative analysis of Clostridioides difficile whole-genome sequencing (WGS) data enables fine scaled investigation of transmission and is increasingly becoming part of routine surveillance. However, these analyses are constrained by the computational requirements of the large volumes of data involved. By decomposing WGS reads or assemblies into k-mers and using the dimensionality reduction technique MinHash, it is possible to rapidly approximate genomic distances without alignment. Here we assessed the performance of MinHash, as implemented by sourmash, in predicting single nucleotide differences between genomes (SNPs) and C. difficile ribotypes (RTs). For a set of 1905 diverse C. difficile genomes (differing by 0–168 519 SNPs), using sourmash to screen for closely related genomes, at a sensitivity of 100 % for pairs ≤10 SNPs, sourmash reduced the number of pairs from 1 813 560 overall to 161 934, i.e. by 91 %, with a positive predictive value of 32 % to correctly identify pairs ≤10 SNPs (maximum SNP distance 4144). At a sensitivity of 95 %, pairs were reduced by 94 % to 108 266 and PPV increased to 45 % (maximum SNP distance 1009). Increasing the MinHash sketch size above 2000 produced minimal performance improvement. We also explored a MinHash similarity-based ribotype prediction method. Genomes with known ribotypes (n=3937) were split into a training set (2937) and test set (1000) randomly. The training set was used to construct a sourmash index against which genomes from the test set were compared. If the closest five genomes in the index had the same ribotype this was taken to predict the searched genome’s ribotype. Using our MinHash ribotype index, predicted ribotypes were correct in 780/1000 (78 %) genomes, incorrect in 20 (2 %), and indeterminant in 200 (20 %). Relaxing the classifier to 4/5 closest matches with the same RT improved the correct predictions to 87 %. Using MinHash it is possible to subsample C. difficile genome k-mer hashes and use them to approximate small genomic differences within minutes, significantly reducing the search space for further analysis.
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Affiliation(s)
- Matthew Phillip Moore
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK.,Nuffield Department of Medicine, University of Oxford, Oxford, UK.,NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Mark H Wilcox
- Healthcare Associated Infection Research Group, Leeds Teaching Hospitals NHS Trust and University of Leeds, Leeds, UK
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.,NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.,NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, UK
| | - David W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK.,NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.,NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, UK
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Soltan AAS, Yang J, Pattanshetty R, Novak A, Yang Y, Rohanian O, Beer S, Soltan MA, Thickett DR, Fairhead R, Zhu T, Eyre DW, Clifton DA, Watson A, Bhargav A, Tough A, Rogers A, Shaikh A, Valensise C, Lee C, Otasowie C, Metcalfe D, Agarwal E, Zareh E, Thangaraj E, Pickles F, Kelly G, Tadikamalla G, Shaw G, Tong H, Davies H, Bahra J, Morgan J, Wilson J, Cutteridge J, O'Byrne K, Farache Trajano L, Oliver M, Pikoula M, Mendoza M, Keevil M, Faisal M, Dole N, Deal O, Conway-Jones R, Sattar S, Kundoor S, Shah S, Muthusami V. Real-world evaluation of rapid and laboratory-free COVID-19 triage for emergency care: external validation and pilot deployment of artificial intelligence driven screening. Lancet Digit Health 2022; 4:e266-e278. [PMID: 35279399 PMCID: PMC8906813 DOI: 10.1016/s2589-7500(21)00272-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/22/2021] [Accepted: 11/24/2021] [Indexed: 12/14/2022]
Abstract
Background Uncertainty in patients' COVID-19 status contributes to treatment delays, nosocomial transmission, and operational pressures in hospitals. However, the typical turnaround time for laboratory PCR remains 12–24 h and lateral flow devices (LFDs) have limited sensitivity. Previously, we have shown that artificial intelligence-driven triage (CURIAL-1.0) can provide rapid COVID-19 screening using clinical data routinely available within 1 h of arrival to hospital. Here, we aimed to improve the time from arrival to the emergency department to the availability of a result, do external and prospective validation, and deploy a novel laboratory-free screening tool in a UK emergency department. Methods We optimised our previous model, removing less informative predictors to improve generalisability and speed, developing the CURIAL-Lab model with vital signs and readily available blood tests (full blood count [FBC]; urea, creatinine, and electrolytes; liver function tests; and C-reactive protein) and the CURIAL-Rapide model with vital signs and FBC alone. Models were validated externally for emergency admissions to University Hospitals Birmingham, Bedfordshire Hospitals, and Portsmouth Hospitals University National Health Service (NHS) trusts, and prospectively at Oxford University Hospitals, by comparison with PCR testing. Next, we compared model performance directly against LFDs and evaluated a combined pathway that triaged patients who had either a positive CURIAL model result or a positive LFD to a COVID-19-suspected clinical area. Lastly, we deployed CURIAL-Rapide alongside an approved point-of-care FBC analyser to provide laboratory-free COVID-19 screening at the John Radcliffe Hospital (Oxford, UK). Our primary improvement outcome was time-to-result, and our performance measures were sensitivity, specificity, positive and negative predictive values, and area under receiver operating characteristic curve (AUROC). Findings 72 223 patients met eligibility criteria across the four validating hospital groups, in a total validation period spanning Dec 1, 2019, to March 31, 2021. CURIAL-Lab and CURIAL-Rapide performed consistently across trusts (AUROC range 0·858–0·881, 95% CI 0·838–0·912, for CURIAL-Lab and 0·836–0·854, 0·814–0·889, for CURIAL-Rapide), achieving highest sensitivity at Portsmouth Hospitals (84·1%, Wilson's 95% CI 82·5–85·7, for CURIAL-Lab and 83·5%, 81·8–85·1, for CURIAL-Rapide) at specificities of 71·3% (70·9–71·8) for CURIAL-Lab and 63·6% (63·1–64·1) for CURIAL-Rapide. When combined with LFDs, model predictions improved triage sensitivity from 56·9% (51·7–62·0) for LFDs alone to 85·6% with CURIAL-Lab (81·6–88·9; AUROC 0·925) and 88·2% with CURIAL-Rapide (84·4–91·1; AUROC 0·919), thereby reducing missed COVID-19 cases by 65% with CURIAL-Lab and 72% with CURIAL-Rapide. For the prospective deployment of CURIAL-Rapide, 520 patients were enrolled for point-of-care FBC analysis between Feb 18 and May 10, 2021, of whom 436 received confirmatory PCR testing and ten (2·3%) tested positive. Median time from arrival to a CURIAL-Rapide result was 45 min (IQR 32–64), 16 min (26·3%) sooner than with LFDs (61 min, 37–99; log-rank p<0·0001), and 6 h 52 min (90·2%) sooner than with PCR (7 h 37 min, 6 h 5 min to 15 h 39 min; p<0·0001). Classification performance was high, with sensitivity of 87·5% (95% CI 52·9–97·8), specificity of 85·4% (81·3–88·7), and negative predictive value of 99·7% (98·2–99·9). CURIAL-Rapide correctly excluded infection for 31 (58·5%) of 53 patients who were triaged by a physician to a COVID-19-suspected area but went on to test negative by PCR. Interpretation Our findings show the generalisability, performance, and real-world operational benefits of artificial intelligence-driven screening for COVID-19 over standard-of-care in emergency departments. CURIAL-Rapide provided rapid, laboratory-free screening when used with near-patient FBC analysis, and was able to reduce the number of patients who tested negative for COVID-19 but were triaged to COVID-19-suspected areas. Funding The Wellcome Trust, University of Oxford Medical and Life Sciences Translational Fund.
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28
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Abstract
BACKGROUND Before the emergence of the B.1.617.2 (delta) variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), vaccination reduced transmission of SARS-CoV-2 from vaccinated persons who became infected, potentially by reducing viral loads. Although vaccination still lowers the risk of infection, similar viral loads in vaccinated and unvaccinated persons who are infected with the delta variant call into question the degree to which vaccination prevents transmission. METHODS We used contact-testing data from England to perform a retrospective observational cohort study involving adult contacts of SARS-CoV-2-infected adult index patients. We used multivariable Poisson regression to investigate associations between transmission and the vaccination status of index patients and contacts and to determine how these associations varied with the B.1.1.7 (alpha) and delta variants and time since the second vaccination. RESULTS Among 146,243 tested contacts of 108,498 index patients, 54,667 (37%) had positive SARS-CoV-2 polymerase-chain-reaction (PCR) tests. In index patients who became infected with the alpha variant, two vaccinations with either BNT162b2 or ChAdOx1 nCoV-19 (also known as AZD1222), as compared with no vaccination, were independently associated with reduced PCR positivity in contacts (adjusted rate ratio with BNT162b2, 0.32; 95% confidence interval [CI], 0.21 to 0.48; and with ChAdOx1 nCoV-19, 0.48; 95% CI, 0.30 to 0.78). Vaccine-associated reductions in transmission of the delta variant were smaller than those with the alpha variant, and reductions in transmission of the delta variant after two BNT162b2 vaccinations were greater (adjusted rate ratio for the comparison with no vaccination, 0.50; 95% CI, 0.39 to 0.65) than after two ChAdOx1 nCoV-19 vaccinations (adjusted rate ratio, 0.76; 95% CI, 0.70 to 0.82). Variation in cycle-threshold (Ct) values (indicative of viral load) in index patients explained 7 to 23% of vaccine-associated reductions in transmission of the two variants. The reductions in transmission of the delta variant declined over time after the second vaccination, reaching levels that were similar to those in unvaccinated persons by 12 weeks in index patients who had received ChAdOx1 nCoV-19 and attenuating substantially in those who had received BNT162b2. Protection in contacts also declined in the 3-month period after the second vaccination. CONCLUSIONS Vaccination was associated with a smaller reduction in transmission of the delta variant than of the alpha variant, and the effects of vaccination decreased over time. PCR Ct values at diagnosis of the index patient only partially explained decreased transmission. (Funded by the U.K. Government Department of Health and Social Care and others.).
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Affiliation(s)
- David W Eyre
- From the Big Data Institute (D.W.E.) and the Health Economics Research Centre (K.B.P.), the Nuffield Department of Population Health, National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance (D.W.E., K.B.P., A.S.W., T.E.A.P.), and the Nuffield Department of Medicine (A.S.W., T.E.A.P.), University of Oxford, Oxford, and the Department of Health and Social Care, National Health Service Test and Trace (D.T., M.P., T.F.), Deloitte MCS (D.C.), and William Harvey Research Institute, Queen Mary University of London (T.F.), London - all in the United Kingdom
| | - Donald Taylor
- From the Big Data Institute (D.W.E.) and the Health Economics Research Centre (K.B.P.), the Nuffield Department of Population Health, National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance (D.W.E., K.B.P., A.S.W., T.E.A.P.), and the Nuffield Department of Medicine (A.S.W., T.E.A.P.), University of Oxford, Oxford, and the Department of Health and Social Care, National Health Service Test and Trace (D.T., M.P., T.F.), Deloitte MCS (D.C.), and William Harvey Research Institute, Queen Mary University of London (T.F.), London - all in the United Kingdom
| | - Mark Purver
- From the Big Data Institute (D.W.E.) and the Health Economics Research Centre (K.B.P.), the Nuffield Department of Population Health, National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance (D.W.E., K.B.P., A.S.W., T.E.A.P.), and the Nuffield Department of Medicine (A.S.W., T.E.A.P.), University of Oxford, Oxford, and the Department of Health and Social Care, National Health Service Test and Trace (D.T., M.P., T.F.), Deloitte MCS (D.C.), and William Harvey Research Institute, Queen Mary University of London (T.F.), London - all in the United Kingdom
| | - David Chapman
- From the Big Data Institute (D.W.E.) and the Health Economics Research Centre (K.B.P.), the Nuffield Department of Population Health, National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance (D.W.E., K.B.P., A.S.W., T.E.A.P.), and the Nuffield Department of Medicine (A.S.W., T.E.A.P.), University of Oxford, Oxford, and the Department of Health and Social Care, National Health Service Test and Trace (D.T., M.P., T.F.), Deloitte MCS (D.C.), and William Harvey Research Institute, Queen Mary University of London (T.F.), London - all in the United Kingdom
| | - Tom Fowler
- From the Big Data Institute (D.W.E.) and the Health Economics Research Centre (K.B.P.), the Nuffield Department of Population Health, National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance (D.W.E., K.B.P., A.S.W., T.E.A.P.), and the Nuffield Department of Medicine (A.S.W., T.E.A.P.), University of Oxford, Oxford, and the Department of Health and Social Care, National Health Service Test and Trace (D.T., M.P., T.F.), Deloitte MCS (D.C.), and William Harvey Research Institute, Queen Mary University of London (T.F.), London - all in the United Kingdom
| | - Koen B Pouwels
- From the Big Data Institute (D.W.E.) and the Health Economics Research Centre (K.B.P.), the Nuffield Department of Population Health, National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance (D.W.E., K.B.P., A.S.W., T.E.A.P.), and the Nuffield Department of Medicine (A.S.W., T.E.A.P.), University of Oxford, Oxford, and the Department of Health and Social Care, National Health Service Test and Trace (D.T., M.P., T.F.), Deloitte MCS (D.C.), and William Harvey Research Institute, Queen Mary University of London (T.F.), London - all in the United Kingdom
| | - A Sarah Walker
- From the Big Data Institute (D.W.E.) and the Health Economics Research Centre (K.B.P.), the Nuffield Department of Population Health, National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance (D.W.E., K.B.P., A.S.W., T.E.A.P.), and the Nuffield Department of Medicine (A.S.W., T.E.A.P.), University of Oxford, Oxford, and the Department of Health and Social Care, National Health Service Test and Trace (D.T., M.P., T.F.), Deloitte MCS (D.C.), and William Harvey Research Institute, Queen Mary University of London (T.F.), London - all in the United Kingdom
| | - Tim E A Peto
- From the Big Data Institute (D.W.E.) and the Health Economics Research Centre (K.B.P.), the Nuffield Department of Population Health, National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance (D.W.E., K.B.P., A.S.W., T.E.A.P.), and the Nuffield Department of Medicine (A.S.W., T.E.A.P.), University of Oxford, Oxford, and the Department of Health and Social Care, National Health Service Test and Trace (D.T., M.P., T.F.), Deloitte MCS (D.C.), and William Harvey Research Institute, Queen Mary University of London (T.F.), London - all in the United Kingdom
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29
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Lee LYW, Rozmanowski S, Pang M, Charlett A, Anderson C, Hughes GJ, Barnard M, Peto L, Vipond R, Sienkiewicz A, Hopkins S, Bell J, Crook DW, Gent N, Walker AS, Peto TEA, Eyre DW. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infectivity by Viral Load, S Gene Variants and Demographic Factors, and the Utility of Lateral Flow Devices to Prevent Transmission. Clin Infect Dis 2022; 74:407-415. [PMID: 33972994 PMCID: PMC8136027 DOI: 10.1093/cid/ciab421] [Citation(s) in RCA: 70] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND How severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infectivity varies with viral load is incompletely understood. Whether rapid point-of-care antigen lateral flow devices (LFDs) detect most potential transmission sources despite imperfect clinical sensitivity is unknown. METHODS We combined SARS-CoV-2 testing and contact tracing data from England between 1 September 2020 and 28 February 2021. We used multivariable logistic regression to investigate relationships between polymerase chain reaction (PCR)-confirmed infection in contacts of community-diagnosed cases and index case viral load, S gene target failure (proxy for B.1.1.7 infection), demographics, SARS-CoV-2 incidence, social deprivation, and contact event type. We used LFD performance to simulate the proportion of cases with a PCR-positive contact expected to be detected using 1 of 4 LFDs. RESULTS In total, 231 498/2 474 066 (9%) contacts of 1 064 004 index cases tested PCR-positive. PCR-positive results in contacts independently increased with higher case viral loads (lower cycle threshold [Ct] values), for example, 11.7% (95% confidence interval [CI] 11.5-12.0%) at Ct = 15 and 4.5% (95% CI 4.4-4.6%) at Ct = 30. B.1.1.7 infection increased PCR-positive results by ~50%, (eg, 1.55-fold, 95% CI 1.49-1.61, at Ct = 20). PCR-positive results were most common in household contacts (at Ct = 20.1, 8.7% [95% CI 8.6-8.9%]), followed by household visitors (7.1% [95% CI 6.8-7.3%]), contacts at events/activities (5.2% [95% CI 4.9-5.4%]), work/education (4.6% [95% CI 4.4-4.8%]), and least common after outdoor contact (2.9% [95% CI 2.3-3.8%]). Contacts of children were the least likely to test positive, particularly following contact outdoors or at work/education. The most and least sensitive LFDs would detect 89.5% (95% CI 89.4-89.6%) and 83.0% (95% CI 82.8-83.1%) of cases with PCR-positive contacts, respectively. CONCLUSIONS SARS-CoV-2 infectivity varies by case viral load, contact event type, and age. Those with high viral loads are the most infectious. B.1.1.7 increased transmission by ~50%. The best performing LFDs detect most infectious cases.
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Affiliation(s)
- Lennard Y W Lee
- Nuffield Department of Medicine, University of Oxford, United Kingdom
| | - Stefan Rozmanowski
- Department of Health and Social Care, UK Government, London, United Kingdom
| | - Matthew Pang
- Department of Health and Social Care, UK Government, London, United Kingdom
| | | | | | | | - Matthew Barnard
- Department of Health and Social Care, UK Government, London, United Kingdom
| | - Leon Peto
- Nuffield Department of Medicine, University of Oxford, United Kingdom
| | | | | | | | - John Bell
- Nuffield Department of Medicine, University of Oxford, United Kingdom
| | - Derrick W Crook
- Nuffield Department of Medicine, University of Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford,United Kingdom
- NIHR Health Protection Research Unit in in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom
| | - Nick Gent
- Public Health England, London,United Kingdom
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford,United Kingdom
- NIHR Health Protection Research Unit in in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford,United Kingdom
- NIHR Health Protection Research Unit in in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom
| | - David W Eyre
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford,United Kingdom
- NIHR Health Protection Research Unit in in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
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30
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Lee LYW, Rozmanowski S, Pang M, Charlett A, Anderson C, Hughes GJ, Barnard M, Peto L, Vipond R, Sienkiewicz A, Hopkins S, Bell J, Crook DW, Gent N, Walker AS, Peto TEA, Eyre DW. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infectivity by Viral Load, S Gene Variants and Demographic Factors, and the Utility of Lateral Flow Devices to Prevent Transmission. Clin Infect Dis 2022; 74:407-415. [PMID: 33972994 DOI: 10.1101/2021.03.31.21254687] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Indexed: 05/27/2023] Open
Abstract
BACKGROUND How severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infectivity varies with viral load is incompletely understood. Whether rapid point-of-care antigen lateral flow devices (LFDs) detect most potential transmission sources despite imperfect clinical sensitivity is unknown. METHODS We combined SARS-CoV-2 testing and contact tracing data from England between 1 September 2020 and 28 February 2021. We used multivariable logistic regression to investigate relationships between polymerase chain reaction (PCR)-confirmed infection in contacts of community-diagnosed cases and index case viral load, S gene target failure (proxy for B.1.1.7 infection), demographics, SARS-CoV-2 incidence, social deprivation, and contact event type. We used LFD performance to simulate the proportion of cases with a PCR-positive contact expected to be detected using 1 of 4 LFDs. RESULTS In total, 231 498/2 474 066 (9%) contacts of 1 064 004 index cases tested PCR-positive. PCR-positive results in contacts independently increased with higher case viral loads (lower cycle threshold [Ct] values), for example, 11.7% (95% confidence interval [CI] 11.5-12.0%) at Ct = 15 and 4.5% (95% CI 4.4-4.6%) at Ct = 30. B.1.1.7 infection increased PCR-positive results by ~50%, (eg, 1.55-fold, 95% CI 1.49-1.61, at Ct = 20). PCR-positive results were most common in household contacts (at Ct = 20.1, 8.7% [95% CI 8.6-8.9%]), followed by household visitors (7.1% [95% CI 6.8-7.3%]), contacts at events/activities (5.2% [95% CI 4.9-5.4%]), work/education (4.6% [95% CI 4.4-4.8%]), and least common after outdoor contact (2.9% [95% CI 2.3-3.8%]). Contacts of children were the least likely to test positive, particularly following contact outdoors or at work/education. The most and least sensitive LFDs would detect 89.5% (95% CI 89.4-89.6%) and 83.0% (95% CI 82.8-83.1%) of cases with PCR-positive contacts, respectively. CONCLUSIONS SARS-CoV-2 infectivity varies by case viral load, contact event type, and age. Those with high viral loads are the most infectious. B.1.1.7 increased transmission by ~50%. The best performing LFDs detect most infectious cases.
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Affiliation(s)
- Lennard Y W Lee
- Nuffield Department of Medicine, University of Oxford, United Kingdom
| | - Stefan Rozmanowski
- Department of Health and Social Care, UK Government, London, United Kingdom
| | - Matthew Pang
- Department of Health and Social Care, UK Government, London, United Kingdom
| | | | | | | | - Matthew Barnard
- Department of Health and Social Care, UK Government, London, United Kingdom
| | - Leon Peto
- Nuffield Department of Medicine, University of Oxford, United Kingdom
| | | | | | | | - John Bell
- Nuffield Department of Medicine, University of Oxford, United Kingdom
| | - Derrick W Crook
- Nuffield Department of Medicine, University of Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford,United Kingdom
- NIHR Health Protection Research Unit in in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom
| | - Nick Gent
- Public Health England, London,United Kingdom
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford,United Kingdom
- NIHR Health Protection Research Unit in in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford,United Kingdom
- NIHR Health Protection Research Unit in in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom
| | - David W Eyre
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford,United Kingdom
- NIHR Health Protection Research Unit in in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
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31
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Abstract
Pathogen whole-genome sequencing has become an important tool for understanding the transmission and epidemiology of infectious diseases. It has improved our understanding of sources of infection and transmission routes for important healthcare-associated pathogens, including Clostridioides difficile and Staphylococcus aureus. Transmission from known infected or colonized patients in hospitals may explain fewer cases than previously thought and multiple introductions of these pathogens from the community may play a greater a role. The findings have had important implications for infection prevention and control. Sequencing has identified heterogeneity within pathogen species, with some subtypes transmitting and persisting in hospitals better than others. It has identified sources of infection in healthcare-associated outbreaks of food-borne pathogens, Candida auris and Mycobacterium chimera, as well as individuals or groups involved in transmission and historical sources of infection. SARS-CoV-2 sequencing has been central to tracking variants during the COVID-19 pandemic and has helped understand transmission to and from patients and healthcare workers despite prevention efforts. Metagenomic sequencing is an emerging technology for culture-independent diagnosis of infection and antimicrobial resistance. In future, sequencing is likely to become more accessible and widely available. Real-time use in hospitals may allow infection prevention and control teams to identify transmission and to target interventions. It may also provide surveillance and infection control benchmarking. Attention to ethical and wellbeing issues arising from sequencing identifying individuals involved in transmission is important. Pathogen whole-genome sequencing has provided an incredible new lens to understand the epidemiology of healthcare-associated infection and to better control and prevent these infections.
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Affiliation(s)
- D W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK; National Institiute for Health Research, Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK; Oxford University Hospitals, Oxford, UK.
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32
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Pritchard E, Jones J, Vihta KD, Stoesser N, Matthews PPC, Eyre DW, House T, Bell JI, Newton PJN, Farrar J, Crook PD, Hopkins S, Cook D, Rourke E, Studley R, Diamond PI, Peto PT, Pouwels KB, Walker PAS. Monitoring populations at increased risk for SARS-CoV-2 infection in the community using population-level demographic and behavioural surveillance. Lancet Reg Health Eur 2022; 13:100282. [PMID: 34927119 PMCID: PMC8665900 DOI: 10.1016/j.lanepe.2021.100282] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
BACKGROUND The COVID-19 pandemic is rapidly evolving, with emerging variants and fluctuating control policies. Real-time population screening and identification of groups in whom positivity is highest could help monitor spread and inform public health messaging and strategy. METHODS To develop a real-time screening process, we included results from nose and throat swabs and questionnaires taken 19 July 2020-17 July 2021 in the UK's national COVID-19 Infection Survey. Fortnightly, associations between SARS-CoV-2 positivity and 60 demographic and behavioural characteristics were estimated using logistic regression models adjusted for potential confounders, considering multiple testing, collinearity, and reverse causality. FINDINGS Of 4,091,537 RT-PCR results from 482,677 individuals, 29,903 (0·73%) were positive. As positivity rose September-November 2020, rates were independently higher in younger ages, and those living in Northern England, major urban conurbations, more deprived areas, and larger households. Rates were also higher in those returning from abroad, and working in healthcare or outside of home. When positivity peaked December 2020-January 2021 (Alpha), high positivity shifted to southern geographical regions. With national vaccine roll-out from December 2020, positivity reduced in vaccinated individuals. Associations attenuated as rates decreased between February-May 2021. Rising positivity rates in June-July 2021 (Delta) were independently higher in younger, male, and unvaccinated groups. Few factors were consistently associated with positivity. 25/45 (56%) confirmed associations would have been detected later using 28-day rather than 14-day periods. INTERPRETATION Population-level demographic and behavioural surveillance can be a valuable tool in identifying the varying characteristics driving current SARS-CoV-2 positivity, allowing monitoring to inform public health policy. FUNDING Department of Health and Social Care (UK), Welsh Government, Department of Health (on behalf of the Northern Ireland Government), Scottish Government, National Institute for Health Research.
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Affiliation(s)
- Emma Pritchard
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Joel Jones
- Office for National Statistics, Newport, UK
| | - Karina-Doris Vihta
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Department of Engineering, University of Oxford, Oxford, UK
| | - Nicole Stoesser
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Prof Philippa C. Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - David W. Eyre
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Department of Engineering, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, UK
- IBM Research, Hartree Centre, Sci-Tech Daresbury, UK
| | - John I Bell
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
| | | | | | - Prof Derrick Crook
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Susan Hopkins
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Healthcare-Associated Infection and Antimicrobial Resistance Division, Public Health England, London, UK
- National Institute for Health Research, Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
| | | | | | | | | | - Prof Tim Peto
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Koen B. Pouwels
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Prof A. Sarah Walker
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
| | - COVID-19 Infection Survey Team
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Office for National Statistics, Newport, UK
- Department of Engineering, University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Department of Mathematics, University of Manchester, Manchester, UK
- IBM Research, Hartree Centre, Sci-Tech Daresbury, UK
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
- Health Improvement Directorate, Public Health England, London, UK
- Wellcome Trust, London, UK
- Healthcare-Associated Infection and Antimicrobial Resistance Division, Public Health England, London, UK
- National Institute for Health Research, Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
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33
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Walker AS, Vihta KD, Gethings O, Pritchard E, Jones J, House T, Bell I, Bell JI, Newton JN, Farrar J, Diamond I, Studley R, Rourke E, Hay J, Hopkins S, Crook D, Peto T, Matthews PC, Eyre DW, Stoesser N, Pouwels KB. Tracking the Emergence of SARS-CoV-2 Alpha Variant in the United Kingdom. N Engl J Med 2021; 385:2582-2585. [PMID: 34879193 PMCID: PMC8693687 DOI: 10.1056/nejmc2103227] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
| | | | - Owen Gethings
- Office for National Statistics, Newport, United Kingdom
| | | | - Joel Jones
- Office for National Statistics, Newport, United Kingdom
| | - Thomas House
- University of Manchester, Manchester, United Kingdom
| | - Iain Bell
- Office for National Statistics, Newport, United Kingdom
| | - John I Bell
- University of Oxford, Oxford, United Kingdom
| | - John N Newton
- Office for Health Improvement and Disparities, London, United Kingdom
| | | | - Ian Diamond
- Office for National Statistics, Newport, United Kingdom
| | - Ruth Studley
- Office for National Statistics, Newport, United Kingdom
| | - Emma Rourke
- Office for National Statistics, Newport, United Kingdom
| | - Jodie Hay
- University of Glasgow, Glasgow, United Kingdom
| | | | | | - Tim Peto
- University of Oxford, Oxford, United Kingdom
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34
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Wang W, Balfe P, Eyre DW, Lumley SF, O'Donnell D, Warren F, Crook DW, Jeffery K, Matthews PC, Klerman EB, McKeating JA. Time of Day of Vaccination Affects SARS-CoV-2 Antibody Responses in an Observational Study of Health Care Workers. J Biol Rhythms 2021; 37:124-129. [PMID: 34866459 PMCID: PMC8825702 DOI: 10.1177/07487304211059315] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a global crisis with unprecedented challenges for public health. Vaccinations against SARS-CoV-2 have slowed the incidence of new infections and reduced disease severity. As the time of day of vaccination has been reported to influence host immune responses to multiple pathogens, we quantified the influence of SARS-CoV-2 vaccination time, vaccine type, participant age, sex, and days post-vaccination on anti-Spike antibody responses in health care workers. The magnitude of the anti-Spike antibody response is associated with the time of day of vaccination, vaccine type, participant age, sex, and days post-vaccination. These results may be relevant for optimising SARS-CoV-2 vaccine efficacy.
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Affiliation(s)
- Wei Wang
- Division of Sleep and Circadian Disorders and Division of Sleep Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Peter Balfe
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - David W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Sheila F Lumley
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.,John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Denise O'Donnell
- John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Fiona Warren
- John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Derrick W Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.,John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.,NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Katie Jeffery
- John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.,Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.,John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Elizabeth B Klerman
- Division of Sleep and Circadian Disorders and Division of Sleep Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jane A McKeating
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.,Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, Oxford, UK
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35
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Vihta KD, Gordon NC, Stoesser N, Quan TP, Tyrrell CSB, Vongsouvath M, Ashley EA, Chansamouth V, Turner P, Ling CL, Eyre DW, White NJ, Crook D, Peto TEA, Walker AS. Antimicrobial resistance in commensal opportunistic pathogens isolated from non-sterile sites can be an effective proxy for surveillance in bloodstream infections. Sci Rep 2021; 11:23359. [PMID: 34862445 PMCID: PMC8642463 DOI: 10.1038/s41598-021-02755-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 11/23/2021] [Indexed: 11/10/2022] Open
Abstract
Antimicrobial resistance (AMR) surveillance in bloodstream infections (BSIs) is challenging in low/middle-income countries (LMICs) given limited laboratory capacity. Other specimens are easier to collect and process and are more likely to be culture-positive. In 8102 E. coli BSIs, 322,087 E. coli urinary tract infections, 6952 S. aureus BSIs and 112,074 S. aureus non-sterile site cultures from Oxfordshire (1998-2018), and other (55,296 isolates) rarer commensal opportunistic pathogens, antibiotic resistance trends over time in blood were strongly associated with those in other specimens (maximum cross-correlation per drug 0.51-0.99). Resistance prevalence was congruent across drug-years for each species (276/312 (88%) species-drug-years with prevalence within ± 10% between blood/other isolates). Results were similar across multiple countries in high/middle/low income-settings in the independent ATLAS dataset (103,559 isolates, 2004-2017) and three further LMIC hospitals/programmes (6154 isolates, 2008-2019). AMR in commensal opportunistic pathogens cultured from BSIs is strongly associated with AMR in commensal opportunistic pathogens cultured from non-sterile sites over calendar time, suggesting the latter could be used as an effective proxy for AMR surveillance in BSIs.
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Affiliation(s)
- Karina-Doris Vihta
- Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK.
- National Institute for Health Research Health Protection Research Unit, Oxford, UK.
- Microbiology Research Level 7, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.
| | | | - Nicole Stoesser
- Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
- National Institute for Health Research Health Protection Research Unit, Oxford, UK
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - T Phuong Quan
- Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
- National Institute for Health Research Health Protection Research Unit, Oxford, UK
| | | | | | - Elizabeth A Ashley
- Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Vientiane, Laos
- Centre for Tropical Medicine & Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Vilada Chansamouth
- Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Vientiane, Laos
- Centre for Tropical Medicine & Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Paul Turner
- Centre for Tropical Medicine & Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Cambodia Oxford Medical Research Unit, Angkor Hospital for Children, Siem Reap, Cambodia
| | - Clare L Ling
- Centre for Tropical Medicine & Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand
| | - David W Eyre
- National Institute for Health Research Health Protection Research Unit, Oxford, UK
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Big Data Institute, University of Oxford, Oxford, UK
| | - Nicholas J White
- Centre for Tropical Medicine & Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Derrick Crook
- Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
- National Institute for Health Research Health Protection Research Unit, Oxford, UK
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Tim E A Peto
- Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
- National Institute for Health Research Health Protection Research Unit, Oxford, UK
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Ann Sarah Walker
- Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
- National Institute for Health Research Health Protection Research Unit, Oxford, UK
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36
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Pouwels KB, Pritchard E, Matthews PC, Stoesser N, Eyre DW, Vihta KD, House T, Hay J, Bell JI, Newton JN, Farrar J, Crook D, Cook D, Rourke E, Studley R, Peto TEA, Diamond I, Walker AS. Effect of Delta variant on viral burden and vaccine effectiveness against new SARS-CoV-2 infections in the UK. Nat Med 2021; 27:2127-2135. [PMID: 34650248 PMCID: PMC8674129 DOI: 10.1038/s41591-021-01548-7] [Citation(s) in RCA: 319] [Impact Index Per Article: 106.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 09/21/2021] [Indexed: 12/13/2022]
Abstract
The effectiveness of the BNT162b2 and ChAdOx1 vaccines against new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections requires continuous re-evaluation, given the increasingly dominant B.1.617.2 (Delta) variant. In this study, we investigated the effectiveness of these vaccines in a large, community-based survey of randomly selected households across the United Kingdom. We found that the effectiveness of BNT162b2 and ChAdOx1 against infections (new polymerase chain reaction (PCR)-positive cases) with symptoms or high viral burden is reduced with the B.1.617.2 variant (absolute difference of 10-13% for BNT162b2 and 16% for ChAdOx1) compared to the B.1.1.7 (Alpha) variant. The effectiveness of two doses remains at least as great as protection afforded by prior natural infection. The dynamics of immunity after second doses differed significantly between BNT162b2 and ChAdOx1, with greater initial effectiveness against new PCR-positive cases but faster declines in protection against high viral burden and symptomatic infection with BNT162b2. There was no evidence that effectiveness varied by dosing interval, but protection was higher in vaccinated individuals after a prior infection and in younger adults. With B.1.617.2, infections occurring after two vaccinations had similar peak viral burden as those in unvaccinated individuals. SARS-CoV-2 vaccination still reduces new infections, but effectiveness and attenuation of peak viral burden are reduced with B.1.617.2.
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Affiliation(s)
- Koen B Pouwels
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK.
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
| | - Emma Pritchard
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Nicole Stoesser
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - David W Eyre
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Karina-Doris Vihta
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Department of Engineering, University of Oxford, Oxford, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester, UK
- IBM Research, Hartree Centre, Sci-Tech Daresbury, UK
| | - Jodie Hay
- Glasgow Lighthouse Laboratory, Glasgow, UK
- University of Glasgow, Glasgow, UK
| | - John I Bell
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
| | - John N Newton
- Health Improvement Directorate, Public Health England, London, UK
| | | | - Derrick Crook
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | | | | | | | - Tim E A Peto
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | | | - A Sarah Walker
- National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- MRC Clinical Trials Unit at UCL, University College London, London, UK
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Bishop JA, Javed HA, el-Bouri R, Zhu T, Taylor T, Peto T, Watkinson P, Eyre DW, Clifton DA. Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge. PLoS One 2021; 16:e0260476. [PMID: 34813632 PMCID: PMC8610279 DOI: 10.1371/journal.pone.0260476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 11/10/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Delays in patient flow and a shortage of hospital beds are commonplace in hospitals during periods of increased infection incidence, such as seasonal influenza and the COVID-19 pandemic. The objective of this study was to develop and evaluate the efficacy of machine learning methods at identifying and ranking the real-time readiness of individual patients for discharge, with the goal of improving patient flow within hospitals during periods of crisis. METHODS AND PERFORMANCE Electronic Health Record data from Oxford University Hospitals was used to train independent models to classify and rank patients' real-time readiness for discharge within 24 hours, for patient subsets according to the nature of their admission (planned or emergency) and the number of days elapsed since their admission. A strategy for the use of the models' inference is proposed, by which the model makes predictions for all patients in hospital and ranks them in order of likelihood of discharge within the following 24 hours. The 20% of patients with the highest ranking are considered as candidates for discharge and would therefore expect to have a further screening by a clinician to confirm whether they are ready for discharge or not. Performance was evaluated in terms of positive predictive value (PPV), i.e., the proportion of these patients who would have been correctly deemed as 'ready for discharge' after having the second screening by a clinician. Performance was high for patients on their first day of admission (PPV = 0.96/0.94 for planned/emergency patients respectively) but dropped for patients further into a longer admission (PPV = 0.66/0.71 for planned/emergency patients still in hospital after 7 days). CONCLUSION We demonstrate the efficacy of machine learning methods at making operationally focused, next-day discharge readiness predictions for all individual patients in hospital at any given moment and propose a strategy for their use within a decision-support tool during crisis periods.
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Affiliation(s)
- Jennifer A. Bishop
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Hamza A. Javed
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Rasheed el-Bouri
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Tingting Zhu
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Thomas Taylor
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Tim Peto
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Peter Watkinson
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - David W. Eyre
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - David A. Clifton
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
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38
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Tsakok MT, Watson RA, Lumley SF, Khan F, Qamhawi Z, Lodge A, Xie C, Shine B, Matthews P, Jeffery K, Eyre DW, Benamore R, Gleeson F. Parenchymal involvement on CT pulmonary angiography in SARS-CoV-2 Alpha variant infection and correlation of COVID-19 CT severity score with clinical disease severity and short-term prognosis in a UK cohort. Clin Radiol 2021; 77:148-155. [PMID: 34895912 PMCID: PMC8608596 DOI: 10.1016/j.crad.2021.11.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 11/12/2021] [Indexed: 01/08/2023]
Abstract
AIM To determine if there is a difference in radiological, biochemical, or clinical severity between patients infected with Alpha-variant SARS-CoV-2 compared with those infected with pre-existing strains, and to determine if the computed tomography (CT) severity score (CTSS) for COVID-19 pneumonitis correlates with clinical severity and can prognosticate outcomes. MATERIALS AND METHODS Blinded CTSS scoring was applied to 137 hospital patients who had undergone both CT pulmonary angiography (CTPA) and whole-genome sequencing of SARS-CoV-2 within 14 days of CTPA between 1/12/20–5/1/21. RESULTS There was no evidence of a difference in imaging severity on CTPA, viral load, clinical parameters of severity, or outcomes between Alpha and preceding variants. CTSS on CTPA strongly correlates with clinical and biochemical severity at the time of CTPA, and with patient outcomes. Classifying CTSS into a binary value of “high” and “low”, with a cut-off score of 14, patients with a high score have a significantly increased risk of deterioration, as defined by subsequent admission to critical care or death (multivariate hazard ratio [HR] 2.76, p<0.001), and hospital length of stay (17.4 versus 7.9 days, p<0.0001). CONCLUSION There was no evidence of a difference in radiological severity of Alpha variant infection compared with pre-existing strains. High CTSS applied to CTPA is associated with increased risk of COVID-19 severity and poorer clinical outcomes and may be of use particularly in settings where CT is not performed for diagnosis of COVID-19 but rather is used following clinical deterioration.
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Affiliation(s)
- M T Tsakok
- Department of Radiology, Oxford University Hospitals NHS Trust, Oxford, Oxfordshire, UK.
| | - R A Watson
- Weatherall Institute of Molecular Medicine, Oxford, Oxfordshire, UK
| | - S F Lumley
- Department of Clinical Medicine, University of Oxford Nuffield Oxford, Oxfordshire, UK; NIHR Oxford Biomedical Research Centre, Oxford, Oxfordshire, UK; National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, London, UK; Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Trust, Oxford, Oxfordshire, UK
| | - F Khan
- Oxford Medical School, Oxford, Oxfordshire, UK
| | - Z Qamhawi
- Department of Radiology, Oxford University Hospitals NHS Trust, Oxford, Oxfordshire, UK
| | - A Lodge
- Oxford Medical School, Oxford, Oxfordshire, UK
| | - C Xie
- Department of Radiology, Oxford University Hospitals NHS Trust, Oxford, Oxfordshire, UK
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- Department of Clinical Medicine, University of Oxford Nuffield Oxford, Oxfordshire, UK
| | - B Shine
- Department of Radiology, Oxford University Hospitals NHS Trust, Oxford, Oxfordshire, UK
| | - P Matthews
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Trust, Oxford, Oxfordshire, UK
| | - K Jeffery
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Trust, Oxford, Oxfordshire, UK
| | - D W Eyre
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Trust, Oxford, Oxfordshire, UK
| | - R Benamore
- Department of Radiology, Oxford University Hospitals NHS Trust, Oxford, Oxfordshire, UK
| | - F Gleeson
- Department of Radiology, Oxford University Hospitals NHS Trust, Oxford, Oxfordshire, UK
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Laager M, Cooper BS, Eyre DW. Probabilistic modelling of effects of antibiotics and calendar time on transmission of healthcare-associated infection. Sci Rep 2021; 11:21417. [PMID: 34725404 PMCID: PMC8560804 DOI: 10.1038/s41598-021-00748-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 10/18/2021] [Indexed: 12/17/2022] Open
Abstract
Healthcare-associated infection and antimicrobial resistance are major concerns. However, the extent to which antibiotic exposure affects transmission and detection of infections such as MRSA is unclear. Additionally, temporal trends are typically reported in terms of changes in incidence, rather than analysing underling transmission processes. We present a data-augmented Markov chain Monte Carlo approach for inferring changing transmission parameters over time, screening test sensitivity, and the effect of antibiotics on detection and transmission. We expand a basic model to allow use of typing information when inferring sources of infections. Using simulated data, we show that the algorithms are accurate, well-calibrated and able to identify antibiotic effects in sufficiently large datasets. We apply the models to study MRSA transmission in an intensive care unit in Oxford, UK with 7924 admissions over 10 years. We find that falls in MRSA incidence over time were associated with decreases in both the number of patients admitted to the ICU colonised with MRSA and in transmission rates. In our inference model, the data were not informative about the effect of antibiotics on risk of transmission or acquisition of MRSA, a consequence of the limited number of possible transmission events in the data. Our approach has potential to be applied to a range of healthcare-associated infections and settings and could be applied to study the impact of other potential risk factors for transmission. Evidence generated could be used to direct infection control interventions.
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Affiliation(s)
- Mirjam Laager
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.
| | - Ben S Cooper
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - David W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
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40
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Wei J, Matthews PC, Stoesser N, Maddox T, Lorenzi L, Studley R, Bell JI, Newton JN, Farrar J, Diamond I, Rourke E, Howarth A, Marsden BD, Hoosdally S, Jones EY, Stuart DI, Crook DW, Peto TEA, Pouwels KB, Walker AS, Eyre DW. Anti-spike antibody response to natural SARS-CoV-2 infection in the general population. Nat Commun 2021; 12:6250. [PMID: 34716320 PMCID: PMC8556331 DOI: 10.1038/s41467-021-26479-2] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 10/06/2021] [Indexed: 01/08/2023] Open
Abstract
Understanding the trajectory, duration, and determinants of antibody responses after SARS-CoV-2 infection can inform subsequent protection and risk of reinfection, however large-scale representative studies are limited. Here we estimated antibody response after SARS-CoV-2 infection in the general population using representative data from 7,256 United Kingdom COVID-19 infection survey participants who had positive swab SARS-CoV-2 PCR tests from 26-April-2020 to 14-June-2021. A latent class model classified 24% of participants as 'non-responders' not developing anti-spike antibodies, who were older, had higher SARS-CoV-2 cycle threshold values during infection (i.e. lower viral burden), and less frequently reported any symptoms. Among those who seroconverted, using Bayesian linear mixed models, the estimated anti-spike IgG peak level was 7.3-fold higher than the level previously associated with 50% protection against reinfection, with higher peak levels in older participants and those of non-white ethnicity. The estimated anti-spike IgG half-life was 184 days, being longer in females and those of white ethnicity. We estimated antibody levels associated with protection against reinfection likely last 1.5-2 years on average, with levels associated with protection from severe infection present for several years. These estimates could inform planning for vaccination booster strategies.
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Affiliation(s)
- Jia Wei
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | | | | | | | - John I Bell
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
| | - John N Newton
- Health Improvement Directorate, Public Health England, London, UK
| | | | | | | | - Alison Howarth
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Brian D Marsden
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Sarah Hoosdally
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - E Yvonne Jones
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - David I Stuart
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Derrick W Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Koen B Pouwels
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
| | - David W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK.
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.
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Lumley SF, Richens N, Lees E, Cregan J, Kalimeris E, Oakley S, Morgan M, Segal S, Dawson M, Walker AS, Eyre DW, Crook DW, Beer S, Novak A, Stoesser NE, Matthews PC. Changes in paediatric respiratory infections at a UK teaching hospital 2016-2021; impact of the SARS-CoV-2 pandemic. J Infect 2021; 84:40-47. [PMID: 34757137 PMCID: PMC8591975 DOI: 10.1016/j.jinf.2021.10.022] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 10/25/2021] [Accepted: 10/26/2021] [Indexed: 01/21/2023]
Abstract
Objective To describe the impact of the SARS-CoV-2 pandemic on the incidence of paediatric viral respiratory tract infection in Oxfordshire, UK. Methods Data on paediatric Emergency Department (ED) attendances (0-15 years inclusive), respiratory virus testing, vital signs and mortality at Oxford University Hospitals were summarised using descriptive statistics. Results Between 1-March-2016 and 30-July-2021, 155,056 ED attendances occurred and 7,195 respiratory virus PCRs were performed. Detection of all pathogens was suppressed during the first national lockdown. Rhinovirus and adenovirus rates increased when schools reopened September-December 2020, then fell, before rising in March-May 2021. The usual winter RSV peak did not occur in 2020/21, with an inter-seasonal rise (32/1,000 attendances in 0-3 yr olds) in July 2021. Influenza remained suppressed throughout. A higher paediatric early warning score (PEWS) was seen for attendees with adenovirus during the pandemic compared to pre-pandemic (p = 0.04, Mann-Witney U test), no other differences in PEWS were seen. Conclusions SARS-CoV-2 caused major changes in the incidence of paediatric respiratory viral infection in Oxfordshire, with implications for clinical service demand, testing strategies, timing of palivizumab RSV prophylaxis, and highlighting the need to understand which public health interventions are most effective for preventing respiratory virus infections.
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Affiliation(s)
- Sheila F Lumley
- NHS Foundation Trust, Oxford University Hospitals, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Medawar Building, South Parks Road, Oxford OX1 3SY, UK.
| | | | - Emily Lees
- Department of Paediatrics, University of Oxford, Oxford UK
| | - Jack Cregan
- Nuffield Department of Medicine, University of Oxford, Medawar Building, South Parks Road, Oxford OX1 3SY, UK
| | | | - Sarah Oakley
- NHS Foundation Trust, Oxford University Hospitals, Oxford, UK
| | - Marcus Morgan
- NHS Foundation Trust, Oxford University Hospitals, Oxford, UK
| | - Shelley Segal
- NHS Foundation Trust, Oxford University Hospitals, Oxford, UK
| | - Moya Dawson
- NHS Foundation Trust, Oxford University Hospitals, Oxford, UK
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Medawar Building, South Parks Road, Oxford OX1 3SY, UK; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
| | - David W Eyre
- NHS Foundation Trust, Oxford University Hospitals, Oxford, UK; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK; Nuffield Department of Population Health, University of Oxford, Oxford, UK; Big Data Institute, University of Oxford, Oxford, UK
| | - Derrick W Crook
- NHS Foundation Trust, Oxford University Hospitals, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Medawar Building, South Parks Road, Oxford OX1 3SY, UK; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
| | - Sally Beer
- NHS Foundation Trust, Oxford University Hospitals, Oxford, UK
| | - Alex Novak
- NHS Foundation Trust, Oxford University Hospitals, Oxford, UK
| | - Nicole E Stoesser
- NHS Foundation Trust, Oxford University Hospitals, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Medawar Building, South Parks Road, Oxford OX1 3SY, UK; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
| | - Philippa C Matthews
- NHS Foundation Trust, Oxford University Hospitals, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Medawar Building, South Parks Road, Oxford OX1 3SY, UK; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK.
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Young BC, Eyre DW, Kendrick S, White C, Smith S, Beveridge G, Nonnenmacher T, Ichofu F, Hillier J, Oakley S, Diamond I, Rourke E, Dawe F, Day I, Davies L, Staite P, Lacey A, McCrae J, Jones F, Kelly J, Bankiewicz U, Tunkel S, Ovens R, Chapman D, Bhalla V, Marks P, Hicks N, Fowler T, Hopkins S, Yardley L, Peto TEA. Daily testing for contacts of individuals with SARS-CoV-2 infection and attendance and SARS-CoV-2 transmission in English secondary schools and colleges: an open-label, cluster-randomised trial. Lancet 2021; 398:1217-1229. [PMID: 34534517 PMCID: PMC8439620 DOI: 10.1016/s0140-6736(21)01908-5] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 08/17/2021] [Accepted: 08/19/2021] [Indexed: 12/21/2022]
Abstract
BACKGROUND School-based COVID-19 contacts in England have been asked to self-isolate at home, missing key educational opportunities. We trialled daily testing of contacts as an alternative to assess whether this resulted in similar control of transmission, while allowing more school attendance. METHODS We did an open-label, cluster-randomised, controlled trial in secondary schools and further education colleges in England. Schools were randomly assigned (1:1) to self-isolation of school-based COVID-19 contacts for 10 days (control) or to voluntary daily lateral flow device (LFD) testing for 7 days with LFD-negative contacts remaining at school (intervention). Randomisation was stratified according to school type and size, presence of a sixth form, presence of residential students, and proportion of students eligible for free school meals. Group assignment was not masked during procedures or analysis. Coprimary outcomes in all students and staff were COVID-19-related school absence and symptomatic PCR-confirmed COVID-19, adjusted for community case rates, to estimate within-school transmission (non-inferiority margin <50% relative increase). Analyses were done on an intention-to-treat basis using quasi-Poisson regression, also estimating complier average causal effects (CACE). This trial is registered with the ISRCTN registry, ISRCTN18100261. FINDINGS Between March 18 and May 4, 2021, 204 schools were taken through the consent process, during which three decided not to participate further. 201 schools were randomly assigned (control group n=99, intervention group n=102) in the 10-week study (April 19-May 10, 2021), which continued until the pre-appointed stop date (June 27, 2021). 76 control group schools and 86 intervention group schools actively participated; additional national data allowed most non-participating schools to be included in analysis of coprimary outcomes. 2432 (42·4%) of 5763 intervention group contacts participated in daily contact testing. There were 657 symptomatic PCR-confirmed infections during 7 782 537 days-at-risk (59·1 per 100 000 per week) in the control group and 740 during 8 379 749 days-at-risk (61·8 per 100 000 per week) in the intervention group (intention-to-treat adjusted incidence rate ratio [aIRR] 0·96 [95% CI 0·75-1·22]; p=0·72; CACE aIRR 0·86 [0·55-1·34]). Among students and staff, there were 59 422 (1·62%) COVID-19-related absences during 3 659 017 person-school-days in the control group and 51 541 (1·34%) during 3 845 208 person-school-days in the intervention group (intention-to-treat aIRR 0·80 [95% CI 0·54-1·19]; p=0·27; CACE aIRR 0·61 [0·30-1·23]). INTERPRETATION Daily contact testing of school-based contacts was non-inferior to self-isolation for control of COVID-19 transmission, with similar rates of symptomatic infections among students and staff with both approaches. Infection rates in school-based contacts were low, with very few school contacts testing positive. Daily contact testing should be considered for implementation as a safe alternative to home isolation following school-based exposures. FUNDING UK Government Department of Health and Social Care.
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Affiliation(s)
| | - David W Eyre
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK; Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | | | - Chris White
- Department of Health and Social Care, London, UK
| | | | | | | | - Fegor Ichofu
- Department of Health and Social Care, London, UK
| | | | - Sarah Oakley
- Microbiology Department, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | | | | | - Fiona Dawe
- Office for National Statistics, Newport, UK
| | - Ieuan Day
- Office for National Statistics, Newport, UK
| | | | | | | | | | | | | | | | - Sarah Tunkel
- Department of Health and Social Care, London, UK
| | | | | | | | - Peter Marks
- Department of Health and Social Care, London, UK
| | - Nick Hicks
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK; Department of Health and Social Care, London, UK; Public Health England, London, UK
| | - Tom Fowler
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | | | - Lucy Yardley
- Health Protection Research Unit in Behavioural Science, University of Bristol, Bristol, UK; School of Psychology, University of Southampton, Southampton, UK
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, UK; National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
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Lipworth S, Hough N, Weston N, Muller-Pebody B, Phin N, Myers R, Chapman S, Flight W, Alexander E, Smith EG, Robinson E, Peto TEA, Crook DW, Walker AS, Hopkins S, Eyre DW, Walker TM. Epidemiology of Mycobacterium abscessus in England: an observational study. Lancet Microbe 2021; 2:e498-e507. [PMID: 34632432 PMCID: PMC8481905 DOI: 10.1016/s2666-5247(21)00128-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
BACKGROUND Mycobacterium abscessus has emerged as a significant clinical concern following reports that it is readily transmissible in health-care settings between patients with cystic fibrosis. We linked routinely collected whole-genome sequencing and health-care usage data with the aim of investigating the extent to which such transmission explains acquisition in patients with and without cystic fibrosis in England. METHODS In this retrospective observational study, we analysed consecutive M abscessus whole-genome sequencing data from England (beginning of February, 2015, to Nov 14, 2019) to identify genomically similar isolates. Linkage to a national health-care usage database was used to investigate possible contacts between patients. Multivariable regression analysis was done to investigate factors associated with acquisition of a genomically clustered strain (genomic distance <25 single nucleotide polymorphisms [SNPs]). FINDINGS 2297 isolates from 906 patients underwent whole-genome sequencing as part of the routine Public Health England diagnostic service. Of 14 genomic clusters containing isolates from ten or more patients, all but one contained patients with cystic fibrosis and patients without cystic fibrosis. Patients with cystic fibrosis were equally likely to have clustered isolates (258 [60%] of 431 patients) as those without cystic fibrosis (322 [63%] of 513 patients; p=0·38). High-density phylogenetic clusters were randomly distributed over a wide geographical area. Most isolates with a closest genetic neighbour consistent with potential transmission had no identifiable relevant epidemiological contacts. Having a clustered isolate was independently associated with increasing age (adjusted odds ratio 1·14 per 10 years, 95% CI 1·04-1·26), but not time spent as an hospital inpatient or outpatient. We identified two sibling pairs with cystic fibrosis with genetically highly divergent isolates and one pair with closely related isolates, and 25 uninfected presumed household contacts with cystic fibrosis. INTERPRETATION Previously identified widely disseminated dominant clones of M abscessus are not restricted to patients with cystic fibrosis and occur in other chronic respiratory diseases. Although our analysis showed a small number of cases where person-to-person transmission could not be excluded, it did not support this being a major mechanism for M abscessus dissemination at a national level in England. Overall, these data should reassure patients and clinicians that the risk of acquisition from other patients in health-care settings is relatively low and motivate future research efforts to focus on identifying routes of acquisition outside of the cystic fibrosis health-care-associated niche. FUNDING The National Institute for Health Research, Health Data Research UK, The Wellcome Trust, The Medical Research Council, and Public Health England.
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Affiliation(s)
- Samuel Lipworth
- Nuffield Department of Medicine, University of Oxford, Oxford, UK,Oxford University Hospitals NHS Foundation Trust, Oxford, UK,Correspondence to: Dr Samuel Lipworth, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | - Natasha Hough
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Natasha Weston
- National Mycobacterial Reference Service-Central and North, Public Health England, Public Health Laboratory, Birmingham, UK
| | - Berit Muller-Pebody
- Tuberculosis, Acute Respiratory, Gastrointestinal, Emerging and Zoonotic Infections and Travel Migrant Health Division, National Infection Service, Public Health England, London, UK
| | - Nick Phin
- Tuberculosis, Acute Respiratory, Gastrointestinal, Emerging and Zoonotic Infections and Travel Migrant Health Division, National Infection Service, Public Health England, London, UK
| | - Richard Myers
- Tuberculosis, Acute Respiratory, Gastrointestinal, Emerging and Zoonotic Infections and Travel Migrant Health Division, National Infection Service, Public Health England, London, UK
| | - Stephen Chapman
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - William Flight
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Eliza Alexander
- National Mycobacterial Reference Service-South, Public Health England, London, UK
| | - E Grace Smith
- National Mycobacterial Reference Service-Central and North, Public Health England, Public Health Laboratory, Birmingham, UK
| | - Esther Robinson
- National Mycobacterial Reference Service-Central and North, Public Health England, Public Health Laboratory, Birmingham, UK
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, UK,Oxford University Hospitals NHS Foundation Trust, Oxford, UK,NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Derrick W Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, UK,Oxford University Hospitals NHS Foundation Trust, Oxford, UK,NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK,NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Susan Hopkins
- Tuberculosis, Acute Respiratory, Gastrointestinal, Emerging and Zoonotic Infections and Travel Migrant Health Division, National Infection Service, Public Health England, London, UK
| | - David W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK,Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Timothy M Walker
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK,Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam
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Eyre DW, Lumley SF, Wei J, Cox S, James T, Justice A, Jesuthasan G, O'Donnell D, Howarth A, Hatch SB, Marsden BD, Jones EY, Stuart DI, Ebner D, Hoosdally S, Crook DW, Peto TEA, Walker TM, Stoesser NE, Matthews PC, Pouwels KB, Walker AS, Jeffery K. Quantitative SARS-CoV-2 anti-spike responses to Pfizer-BioNTech and Oxford-AstraZeneca vaccines by previous infection status. Clin Microbiol Infect 2021; 27:1516.e7-1516.e14. [PMID: 34111577 PMCID: PMC8180449 DOI: 10.1016/j.cmi.2021.05.041] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Revised: 05/23/2021] [Accepted: 05/25/2021] [Indexed: 10/31/2022]
Abstract
OBJECTIVES We investigated determinants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) anti-spike IgG responses in healthcare workers (HCWs) following one or two doses of Pfizer-BioNTech or Oxford-AstraZeneca vaccines. METHODS HCWs participating in regular SARS-CoV-2 PCR and antibody testing were invited for serological testing prior to first and second vaccination, and 4 weeks post-vaccination if receiving a 12-week dosing interval. Quantitative post-vaccination anti-spike antibody responses were measured using the Abbott SARS-CoV-2 IgG II Quant assay (detection threshold: ≥50 AU/mL). We used multivariable logistic regression to identify predictors of seropositivity and generalized additive models to track antibody responses over time. RESULTS 3570/3610 HCWs (98.9%) were seropositive >14 days post first vaccination and prior to second vaccination: 2706/2720 (99.5%) were seropositive after the Pfizer-BioNTech and 864/890 (97.1%) following the Oxford-AstraZeneca vaccines. Previously infected and younger HCWs were more likely to test seropositive post first vaccination, with no evidence of differences by sex or ethnicity. All 470 HCWs tested >14 days after the second vaccination were seropositive. Quantitative antibody responses were higher after previous infection: median (IQR) >21 days post first Pfizer-BioNTech 14 604 (7644-22 291) AU/mL versus 1028 (564-1985) AU/mL without prior infection (p < 0.001). Oxford-AstraZeneca vaccine recipients had lower readings post first dose than Pfizer-BioNTech recipients, with and without previous infection, 10 095 (5354-17 096) and 435 (203-962) AU/mL respectively (both p < 0.001 versus Pfizer-BioNTech). Antibody responses >21 days post second Pfizer vaccination in those not previously infected, 10 058 (6408-15 582) AU/mL, were similar to those after prior infection followed by one vaccine dose. CONCLUSIONS SARS-CoV-2 vaccination leads to detectable anti-spike antibodies in nearly all adult HCWs. Whether differences in response impact vaccine efficacy needs further study.
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Affiliation(s)
- David W Eyre
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK; Nuffield Department of Population Health, University of Oxford, Oxford, UK; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in Partnership with Public Health England, Oxford, UK.
| | - Sheila F Lumley
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in Partnership with Public Health England, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jia Wei
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Stuart Cox
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Tim James
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Anita Justice
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | | | - Denise O'Donnell
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Alison Howarth
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | - Brian D Marsden
- Nuffield Department of Medicine, University of Oxford, Oxford, UK; Kennedy Institute of Rheumatology Research, University of Oxford, UK
| | - E Yvonne Jones
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - David I Stuart
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Daniel Ebner
- Nuffield Department of Medicine, University of Oxford, Oxford, UK; Target Discovery Institute, University of Oxford, Oxford, UK
| | - Sarah Hoosdally
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in Partnership with Public Health England, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Derrick W Crook
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in Partnership with Public Health England, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Tim E A Peto
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in Partnership with Public Health England, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Timothy M Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK; Oxford University Clinical Research Unit, Ho Chi Minh City, Viet nam
| | - Nicole E Stoesser
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in Partnership with Public Health England, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Philippa C Matthews
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in Partnership with Public Health England, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Koen B Pouwels
- Nuffield Department of Population Health, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in Partnership with Public Health England, Oxford, UK
| | - A Sarah Walker
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in Partnership with Public Health England, Oxford, UK; Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Katie Jeffery
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
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Mo Y, Eyre DW, Lumley SF, Walker TM, Shaw RH, O’Donnell D, Butcher L, Jeffery K, Donnelly CA, Cooper BS. Transmission of community- and hospital-acquired SARS-CoV-2 in hospital settings in the UK: A cohort study. PLoS Med 2021; 18:e1003816. [PMID: 34637439 PMCID: PMC8509983 DOI: 10.1371/journal.pmed.1003816] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 09/14/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Nosocomial spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has been widely reported, but the transmission pathways among patients and healthcare workers (HCWs) are unclear. Identifying the risk factors and drivers for these nosocomial transmissions is critical for infection prevention and control interventions. The main aim of our study was to quantify the relative importance of different transmission pathways of SARS-CoV-2 in the hospital setting. METHODS AND FINDINGS This is an observational cohort study using data from 4 teaching hospitals in Oxfordshire, United Kingdom, from January to October 2020. Associations between infectious SARS-CoV-2 individuals and infection risk were quantified using logistic, generalised additive and linear mixed models. Cases were classified as community- or hospital-acquired using likely incubation periods of 3 to 7 days. Of 66,184 patients who were hospitalised during the study period, 920 had a positive SARS-CoV-2 PCR test within the same period (1.4%). The mean age was 67.9 (±20.7) years, 49.2% were females, and 68.5% were from the white ethnic group. Out of these, 571 patients had their first positive PCR tests while hospitalised (62.1%), and 97 of these occurred at least 7 days after admission (10.5%). Among the 5,596 HCWs, 615 (11.0%) tested positive during the study period using PCR or serological tests. The mean age was 39.5 (±11.1) years, 78.9% were females, and 49.8% were nurses. For susceptible patients, 1 day in the same ward with another patient with hospital-acquired SARS-CoV-2 was associated with an additional 7.5 infections per 1,000 susceptible patients (95% credible interval (CrI) 5.5 to 9.5/1,000 susceptible patients/day) per day. Exposure to an infectious patient with community-acquired Coronavirus Disease 2019 (COVID-19) or to an infectious HCW was associated with substantially lower infection risks (2.0/1,000 susceptible patients/day, 95% CrI 1.6 to 2.2). As for HCW infections, exposure to an infectious patient with hospital-acquired SARS-CoV-2 or to an infectious HCW were both associated with an additional 0.8 infection per 1,000 susceptible HCWs per day (95% CrI 0.3 to 1.6 and 0.6 to 1.0, respectively). Exposure to an infectious patient with community-acquired SARS-CoV-2 was associated with less than half this risk (0.2/1,000 susceptible HCWs/day, 95% CrI 0.2 to 0.2). These assumptions were tested in sensitivity analysis, which showed broadly similar results. The main limitations were that the symptom onset dates and HCW absence days were not available. CONCLUSIONS In this study, we observed that exposure to patients with hospital-acquired SARS-CoV-2 is associated with a substantial infection risk to both HCWs and other hospitalised patients. Infection control measures to limit nosocomial transmission must be optimised to protect both staff and patients from SARS-CoV-2 infection.
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Affiliation(s)
- Yin Mo
- Oxford Centre for Global Health Research, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Division of Infectious Disease, Department of Medicine, National University Hospital, Singapore
- Department of Medicine, National University of Singapore, Singapore
| | - David W. Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Oxford University Hospitals, NHS Foundation Trust, Oxford, United Kingdom
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Sheila F. Lumley
- Oxford University Hospitals, NHS Foundation Trust, Oxford, United Kingdom
| | - Timothy M. Walker
- Oxford Centre for Global Health Research, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Oxford University Hospitals, NHS Foundation Trust, Oxford, United Kingdom
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Robert H. Shaw
- Oxford University Hospitals, NHS Foundation Trust, Oxford, United Kingdom
| | - Denise O’Donnell
- Oxford University Hospitals, NHS Foundation Trust, Oxford, United Kingdom
| | - Lisa Butcher
- Oxford University Hospitals, NHS Foundation Trust, Oxford, United Kingdom
| | - Katie Jeffery
- Oxford University Hospitals, NHS Foundation Trust, Oxford, United Kingdom
- Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Christl A. Donnelly
- Department of Statistics, University of Oxford, Oxford, United Kingdom
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | | | - Ben S. Cooper
- Oxford Centre for Global Health Research, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
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46
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Lumley SF, Constantinides B, Sanderson N, Rodger G, Street TL, Swann J, Chau KK, O'Donnell D, Warren F, Hoosdally S, O'Donnell AM, Walker TM, Stoesser NE, Butcher L, Peto TE, Crook DW, Jeffery K, Matthews PC, Eyre DW. Epidemiological data and genome sequencing reveals that nosocomial transmission of SARS-CoV-2 is underestimated and mostly mediated by a small number of highly infectious individuals. J Infect 2021; 83:473-482. [PMID: 34332019 PMCID: PMC8316632 DOI: 10.1016/j.jinf.2021.07.034] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 07/21/2021] [Accepted: 07/24/2021] [Indexed: 12/12/2022]
Abstract
OBJECTIVES Despite robust efforts, patients and staff acquire SARS-CoV-2 infection in hospitals. We investigated whether whole-genome sequencing enhanced the epidemiological investigation of healthcare-associated SARS-CoV-2 acquisition. METHODS From 17-November-2020 to 5-January-2021, 803 inpatients and 329 staff were diagnosed with SARS-CoV-2 infection at four Oxfordshire hospitals. We classified cases using epidemiological definitions, looked for a potential source for each nosocomial infection, and evaluated genomic evidence supporting transmission. RESULTS Using national epidemiological definitions, 109/803(14%) inpatient infections were classified as definite/probable nosocomial, 615(77%) as community-acquired and 79(10%) as indeterminate. There was strong epidemiological evidence to support definite/probable cases as nosocomial. Many indeterminate cases were likely infected in hospital: 53/79(67%) had a prior-negative PCR and 75(95%) contact with a potential source. 89/615(11% of all 803 patients) with apparent community-onset had a recent hospital exposure. Within 764 samples sequenced 607 genomic clusters were identified (>1 SNP distinct). Only 43/607(7%) clusters contained evidence of onward transmission (subsequent cases within ≤ 1 SNP). 20/21 epidemiologically-identified outbreaks contained multiple genomic introductions. Most (80%) nosocomial acquisition occurred in rapid super-spreading events in settings with a mix of COVID-19 and non-COVID-19 patients. CONCLUSIONS Current surveillance definitions underestimate nosocomial acquisition. Most nosocomial transmission occurs from a relatively limited number of highly infectious individuals.
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Affiliation(s)
- Sheila F Lumley
- John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust etc.; Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom.
| | - Bede Constantinides
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Nicholas Sanderson
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
| | - Gillian Rodger
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Teresa L Street
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
| | - Jeremy Swann
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
| | - Kevin K Chau
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Denise O'Donnell
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Fiona Warren
- John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust etc
| | - Sarah Hoosdally
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Anne-Marie O'Donnell
- John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust etc.; Nuffield Department of Population Health, University of Oxford, Oxford, Unit ed Kingdom
| | - Timothy M Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom; Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Nicole E Stoesser
- John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust etc.; Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Lisa Butcher
- John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust etc
| | - Tim Ea Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Derrick W Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Katie Jeffery
- John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust etc
| | - Philippa C Matthews
- John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust etc.; Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - David W Eyre
- NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom; Nuffield Department of Population Health, University of Oxford, Oxford, Unit ed Kingdom; Big Data Institute, University of Oxford, Oxford, United Kingdom
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Kong LY, Wilson JD, Moura IB, Fawley W, Kelly L, Walker AS, Eyre DW, Wilcox MH. Utility of Whole Genome Sequencing in Assessing and Enhancing Partner Notification of Neisseria gonorrhoeae Infection. Sex Transm Dis 2021; 48:773-780. [PMID: 34110743 DOI: 10.1097/olq.0000000000001419] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Gonorrhea is a sexually transmitted infection of global concern. We investigated whole-genome sequencing (WGS) as a tool to measure and enhance partner notification (PN) in gonorrhea management. METHODS Between May and November 2018, all N. gonorrhoeae isolated from patients attending Leeds Sexual Health, United Kingdom, underwent WGS. Reports listing sequences within 20 single-nucleotide polymorphisms (SNPs) of study isolates within a database containing select isolates from April 1, 2016, to November 15, 2018, were issued to clinicians. The proportion of cases with a potential transmission partner identified by PN was determined from patient and PN data. The WGS reports were reviewed to identify additional cases within 6 SNPs or less and verified for PN concordance. RESULTS Three hundred eighty isolates from 377 cases were successfully sequenced; 292 had traceable/contactable partners and 69 (18%) had a potential transmission partner identified by PN. Concordant PN and WGS links were identified in 47 partner pairs. Of 308 cases with no transmission partner by PN, 185 (60%) had a case within 6 SNPs or less; examination of these cases' PN data identified 7 partner pairs with previously unrecognized PN link, giving a total of 54 pairs; all had 4 or less SNP differences. The WGS clusters confirmed gaps in partner finding, at individual and group levels. Despite the clinic providing sexual health services to the whole city, 35 cases with multiple partners had no genetically related case, suggesting multiple undiagnosed infections. CONCLUSIONS Whole-genome sequencing could improve gonorrhea PN and control by identifying new links and clusters with significant gaps in partner finding.
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Affiliation(s)
| | | | - Ines B Moura
- Leeds Institute for Medical Research, Faculty of Medicine and Health, University of Leeds
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Wei J, Stoesser N, Matthews PC, Ayoubkhani D, Studley R, Bell I, Bell JI, Newton JN, Farrar J, Diamond I, Rourke E, Howarth A, Marsden BD, Hoosdally S, Jones EY, Stuart DI, Crook DW, Peto TEA, Pouwels KB, Eyre DW, Walker AS. Antibody responses to SARS-CoV-2 vaccines in 45,965 adults from the general population of the United Kingdom. Nat Microbiol 2021; 6:1140-1149. [PMID: 34290390 PMCID: PMC8294260 DOI: 10.1038/s41564-021-00947-3] [Citation(s) in RCA: 196] [Impact Index Per Article: 65.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 07/01/2021] [Indexed: 02/08/2023]
Abstract
We report that in a cohort of 45,965 adults, who were receiving either the ChAdOx1 or the BNT162b2 SARS-CoV-2 vaccines, in those who had no prior infection with SARS-CoV-2, seroconversion rates and quantitative antibody levels after a single dose were lower in older individuals, especially in those aged >60 years. Two vaccine doses achieved high responses across all ages. Antibody levels increased more slowly and to lower levels with a single dose of ChAdOx1 compared with a single dose of BNT162b2, but waned following a single dose of BNT162b2 in older individuals. In descriptive latent class models, we identified four responder subgroups, including a 'low responder' group that more commonly consisted of people aged >75 years, males and individuals with long-term health conditions. Given our findings, we propose that available vaccines should be prioritized for those not previously infected and that second doses should be prioritized for individuals aged >60 years. Further data are needed to better understand the extent to which quantitative antibody responses are associated with vaccine-mediated protection.
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Affiliation(s)
- Jia Wei
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Nicole Stoesser
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | | | | | - Iain Bell
- Office for National Statistics, Newport, UK
| | - John I Bell
- Office of the Regius Professor of Medicine, University of Oxford, Oxford, UK
| | - John N Newton
- Health Improvement Directorate, Public Health England, London, UK
| | | | | | | | - Alison Howarth
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Brian D Marsden
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Sarah Hoosdally
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - E Yvonne Jones
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - David I Stuart
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Derrick W Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Koen B Pouwels
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - David W Eyre
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK.
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.
- Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
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Moloney G, Eyre DW, Mac Aogáin M, McElroy MC, Vaughan A, Peto TE, Crook DW, Rogers TR. Human and Porcine Transmission of Clostridioides difficile Ribotype 078, Europe. Emerg Infect Dis 2021; 27:2294-2300. [PMID: 34423760 PMCID: PMC8386809 DOI: 10.3201/eid2709.203468] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Genomic analysis of a diverse collection of Clostridioides difficile ribotype 078 isolates from Ireland and 9 countries in Europe provided evidence for complex regional and international patterns of dissemination that are not restricted to humans. These isolates are associated with C. difficile colonization and clinical illness in humans and pigs.
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50
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Lumley SF, Wei J, O'Donnell D, Stoesser NE, Matthews PC, Howarth A, Hatch SB, Marsden BD, Cox S, James T, Peck LJ, Ritter TG, de Toledo Z, Cornall RJ, Jones EY, Stuart DI, Screaton G, Ebner D, Hoosdally S, Crook DW, Conlon CP, Pouwels KB, Walker AS, Peto TEA, Walker TM, Jeffery K, Eyre DW. The Duration, Dynamics, and Determinants of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Antibody Responses in Individual Healthcare Workers. Clin Infect Dis 2021; 73:e699-e709. [PMID: 33400782 DOI: 10.1101/2020.11.02.20224824] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Indexed: 05/20/2023] Open
Abstract
BACKGROUND Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin G (IgG) antibody measurements can be used to estimate the proportion of a population exposed or infected and may be informative about the risk of future infection. Previous estimates of the duration of antibody responses vary. METHODS We present 6 months of data from a longitudinal seroprevalence study of 3276 UK healthcare workers (HCWs). Serial measurements of SARS-CoV-2 anti-nucleocapsid and anti-spike IgG were obtained. Interval censored survival analysis was used to investigate the duration of detectable responses. Additionally, Bayesian mixed linear models were used to investigate anti-nucleocapsid waning. RESULTS Anti-spike IgG levels remained stably detected after a positive result, for example, in 94% (95% credibility interval [CrI] 91-96%) of HCWs at 180 days. Anti-nucleocapsid IgG levels rose to a peak at 24 (95% CrI 19-31) days post first polymerase chain reaction (PCR)-positive test, before beginning to fall. Considering 452 anti-nucleocapsid seropositive HCWs over a median of 121 days from their maximum positive IgG titer, the mean estimated antibody half-life was 85 (95% CrI 81-90) days. Higher maximum observed anti-nucleocapsid titers were associated with longer estimated antibody half-lives. Increasing age, Asian ethnicity, and prior self-reported symptoms were independently associated with higher maximum anti-nucleocapsid levels and increasing age and a positive PCR test undertaken for symptoms with longer anti-nucleocapsid half-lives. CONCLUSIONS SARS-CoV-2 anti-nucleocapsid antibodies wane within months and fall faster in younger adults and those without symptoms. However, anti-spike IgG remains stably detected. Ongoing longitudinal studies are required to track the long-term duration of antibody levels and their association with immunity to SARS-CoV-2 reinfection.
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Affiliation(s)
- Sheila F Lumley
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Jia Wei
- Big Data Institute, University of Oxford, Oxford, United Kingdom
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Denise O'Donnell
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Nicole E Stoesser
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Philippa C Matthews
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Alison Howarth
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Stephanie B Hatch
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Brian D Marsden
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Kennedy Institute of Rheumatology Research, University of Oxford, United Kingdom
| | - Stuart Cox
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Tim James
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Liam J Peck
- Medical School, University of Oxford, Oxford, United Kingdom
| | - Thomas G Ritter
- Medical School, University of Oxford, Oxford, United Kingdom
| | - Zoe de Toledo
- Medical School, University of Oxford, Oxford, United Kingdom
| | - Richard J Cornall
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - E Yvonne Jones
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - David I Stuart
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Gavin Screaton
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Daniel Ebner
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Target Discovery Institute, University of Oxford, Oxford, United Kingdom
| | - Sarah Hoosdally
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Derrick W Crook
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | | | - Koen B Pouwels
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - A Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Tim E A Peto
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
| | - Timothy M Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam
| | - Katie Jeffery
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - David W Eyre
- Big Data Institute, University of Oxford, Oxford, United Kingdom
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford in partnership with Public Health England, Oxford, United Kingdom
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
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