Developing a framework for tracking antimicrobial resistance gene movement in a persistent environmental reservoir.

Mobile genetic elements are key to the global emergence of antibiotic resistance. We successfully reconstructed the complete bacterial genome and plasmid assemblies of isolates sharing the same carbapenemase gene to understand evolution over time in six confined hospital drains over five years. From 82 isolates we identified 14 unique strains from 10 species with 113 carrying plasmids across 16 distinct replicon types.

Machine learning and clinician predictions of antibiotic resistance in Enterobacterales bloodstream infections.

Background: Patients with Gram-negative bloodstream infections are at risk of serious adverse outcomes without active treatment, but identifying who has antimicrobial resistance (AMR) to target empirical treatment is challenging.

Methods: We used XGBoost machine learning models to predict antimicrobial resistance to seven antibiotics in patients with Enterobacterales bloodstream infection. Models were trained using hospital and community data from Oxfordshire, UK, for patients with positive blood cultures between 01-January-2017 and 31-December-2021.

Addition of macrolide antibiotics for hospital treatment of community-acquired pneumonia.

Background: Current guidelines recommend combining a macrolide with a β-lactam antibiotic for the empirical treatment of moderate-to-high severity community-acquired pneumonia (CAP); however macrolide use is associated with potential adverse events and antimicrobial resistance.

Methods: We analysed electronic health data from 8,872 adults in Oxfordshire, UK, hospitalised with CAP between 01-January-2016 and 19-March-2024, who received either amoxicillin or co-amoxiclav as initial treatment. We examined the effects of adjunctive macrolides on 30-day all-cause mortality, time to hospital discharge, and changes in Sequential Organ Failure Assessment (SOFA) score, using inverse probability treatment weighting to address confounding by baseline severity.

Predicting individual patient and hospital-level discharge using machine learning.

Background: Accurately predicting hospital discharge events could help improve patient flow and the efficiency of healthcare delivery. However, using machine learning and diverse electronic health record (EHR) data for this task remains incompletely explored.

Methods: We used EHR data from February-2017 to January-2020 from Oxfordshire, UK to predict hospital discharges in the next 24 h.

Genomic epidemiology describes introduction and outbreaks of antifungal drug-resistant .

is a globally emerged fungal pathogen causing nosocomial invasive infections. Here, we use cutting-edge genomic approaches to elucidate the temporal and geographic epidemiology of drug-resistant within the UK. We analysed a representative sample of over 200 isolates from multiple UK hospitals to assess the number and timings of introductions and infer subsequent patterns of inter- and intra-hospital transmission of azole drug-resistant isolates.

Retrospective analysis of hospital electronic health records reveals unseen cases of acute hepatitis with unknown aetiology in adults in Oxfordshire.

Background: An outbreak of acute severe hepatitis of unknown aetiology (AS-Hep-UA) in children during 2022 was subsequently linked to infections with adenovirus-associated virus 2 and other 'helper viruses', including human adenovirus. It is possible that evidence of such an outbreak could be identified at a population level based on routine data captured by electronic health records (EHR).

Methods: We used anonymised EHR to collate retrospective data for all emergency presentations to Oxford University Hospitals NHS Foundation Trust in the UK, between 2016-2022, for all ages from 18 months and older.

Immunogenicity of COVID-19 vaccines in patients with follicular lymphoma receiving frontline chemoimmunotherapy.

Immune responses to primary COVID-19 vaccination were investigated in 58 patients with follicular lymphoma (FL) as part of the PETReA trial of frontline therapy (EudraCT 2016-004010-10). COVID-19 vaccines (BNT162b2 or ChAdOx1) were administered before, during or after cytoreductive treatment comprising rituximab (depletes B cells) and either bendamustine (depletes CD4 T cells) or cyclophosphamide-based chemotherapy. Blood samples obtained after vaccine doses 1 and 2 (V1, V2) were analysed for antibodies and T cells reactive to the SARS-CoV-2 spike protein using the Abbott Architect and interferon-gamma ELISpot assays respectively.

Changes in the investigation and management of suspected myocardial infarction and injury during COVID-19: a multi-centre study using routinely collected healthcare data.

Objective: The COVID-19 pandemic was associated with a reduction in the incidence of myocardial infarction (MI) diagnosis, in part because patients were less likely to present to hospital. Whether changes in clinical decision making with respect to the investigation and management of patients with suspected MI also contributed to this phenomenon is unknown.

Methods: Multicentre retrospective cohort study in three UK centres contributing data to the National Institute for Health Research Health Informatics Collaborative.

Detecting changes in population trends in infection surveillance using community SARS-CoV-2 prevalence as an exemplar.

Detecting and quantifying changes in the growth rates of infectious diseases is vital to informing public health strategy and can inform policymakers' rationale for implementing or continuing interventions aimed at reducing their impact. Substantial changes in SARS-CoV-2 prevalence with the emergence of variants have provided an opportunity to investigate different methods for doing this. We collected polymerase chain reaction (PCR) results from all participants in the United Kingdom's COVID-19 Infection Survey between August 1, 2020, and June 30, 2022.

Nanopore sequencing of influenza A and B in Oxfordshire and the United Kingdom, 2022-23.

Objectives: We evaluated Nanopore sequencing for influenza surveillance.

Methods: Influenza A and B PCR-positive samples from hospital patients in Oxfordshire, UK, and a UK-wide population survey from winter 2022-23 underwent Nanopore sequencing following targeted rt-PCR amplification.

Results: From 941 infections, successful sequencing was achieved in 292/388 (75 %) available Oxfordshire samples: 231 (79 %) A/H3N2, 53 (18 %) A/H1N1, and 8 (3 %) B/Victoria and in 53/113 (47 %) UK-wide samples.

Distinct patterns of vital sign and inflammatory marker responses in adults with suspected bloodstream infection.

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.

Target enrichment improves culture-independent detection of and antimicrobial resistance determinants direct from clinical samples with Nanopore sequencing.

Multi-drug-resistant infection is a significant public health risk. Rapidly detecting 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 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 DNA improves detection of both species and AMR determinants.

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.

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.

The epidemiology of multidrug-resistant organisms in persons diagnosed with cancer in Norway, 2008-2018: expanding surveillance using existing laboratory and register data.

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.

Persistence of SARS-CoV-2 antibodies over 18 months following infection: UK Biobank COVID-19 Serology Study.

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 burden and dynamics of hospital-acquired SARS-CoV-2 in England.

Hospital-based transmission had a dominant role in Middle East respiratory syndrome coronavirus (MERS-CoV) and severe acute respiratory syndrome coronavirus (SARS-CoV) epidemics, 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.