Introduction This project applies a Learning Healthcare System (LHS) approach to antibiotic prescribing for common infections in primary care. The approach involves iterations of data analysis, feedback to clinicians and implementation of quality improvement activities by the clinicians. The main research question is, can a knowledge support system (KSS) intervention within an LHS implementation improve antibiotic prescribing without increasing the risk of complications? Methods and analysis A pragmatic cluster randomised controlled trial will be conducted, with randomisation of at least 112 general practices in North-West England. General practices participating in the trial will be randomised to the following interventions: periodic practice-level and individual prescriber feedback using dashboards; or the same dashboards plus a KSS. Data from large databases of healthcare records are used to characterise heterogeneity in antibiotic uses, and to calculate risk scores for clinical outcomes and for the effectiveness of different treatment strategies. The results provide the baseline content for the dashboards and KSS. The KSS comprises a display within the electronic health record used during the consultation; the prescriber (general practitioner or allied health professional) will answer standard questions about the patient’s presentation and will then be presented with information (eg, patient’s risk of complications from the infection) to guide decision making. The KSS can generate information sheets for patients, conveyed by the clinicians during consultations. The primary outcome is the practice-level rate of antibiotic prescribing (per 1000 patients) with secondary safety outcomes. The data from practices participating in the trial and the dashboard infrastructure will be held within regional shared care record systems of the National Health Service in the UK. Ethics and dissemination Approved by National Health Service Ethics Committee IRAS 290050. The research results will be published in peer-reviewed journals and also disseminated to participating clinical staff and policy and guideline developers. Trial registration number ISRCTN16230629 . 

Knowledge support for optimising antibiotic prescribing for common infections in general practices: evaluation of the effectiveness of periodic feedback, decision support during consultations and peer comparisons in a cluster randomised trial (BRIT2) – study protocol

Abstract Background With the global challenge of antimicrobial resistance intensified during the COVID-19 pandemic, evaluating adverse events (AEs) post-antibiotic treatment for common infections is crucial. This study aims to examines the changes in incidence rates of AEs during the COVID-19 pandemic and predict AE risk following antibiotic prescriptions for common infections, considering their previous antibiotic exposure and other long-term clinical conditions. Methods With the approval of NHS England, we used OpenSAFELY platform and analysed electronic health records from patients aged 18–110, prescribed antibiotics for urinary tract infection (UTI), lower respiratory tract infections (LRTI), upper respiratory tract infections (URTI), sinusitis, otitis externa, and otitis media between January 2019 and June 2023. We evaluated the temporal trends in the incidence rate of AEs for each infection, analysing monthly changes over time. The survival probability of emergency AE hospitalisation was estimated in each COVID-19 period (period 1: 1 January 2019 to 25 March 2020, period 2: 26 March 2020 to 8 March 2021, period 3: 9 March 2021 to 30 June 2023) using the Kaplan–Meier approach. Prognostic models, using Cox proportional hazards regression, were developed and validated to predict AE risk within 30 days post-prescription using the records in Period 1. Results Out of 9.4 million patients who received antibiotics, 0.6% of UTI, 0.3% of URTI, and 0.5% of LRTI patients experienced AEs. UTI and LRTI patients demonstrated a higher risk of AEs, with a noted increase in AE incidence during the COVID-19 pandemic. Higher comorbidity and recent antibiotic use emerged as significant AE predictors. The developed models exhibited good calibration and discrimination, especially for UTIs and LRTIs, with a C-statistic above 0.70. Conclusions The study reveals a variable incidence of AEs post-antibiotic treatment for common infections, with UTI and LRTI patients facing higher risks. AE risks varied between infections and COVID-19 periods. These findings underscore the necessity for cautious antibiotic prescribing and call for further exploration into the intricate dynamics between antibiotic use, AEs, and the pandemic. 

Risk of emergency hospital admission related to adverse events after antibiotic treatment in adults with a common infection: impact of COVID-19 and derivation and validation of risk prediction models

Background: To assess whether antibiotics for common infections are prescribed in a risk-based manner and how this changed during the COVID-19 pandemic.Methods: With the approval of NHS England, we accessed pseudonymised patient-level electronic health records of primary care data from The Phoenix Partnership (TPP) through OpenSAFELY. We included adults registered at general practices in England with a record of common infection, including lower respiratory tract infection (LRTI), upper respiratory tract infections (URTI), and lower urinary tract infection (UTI). Patients with a record of COVID-19 were excluded. Patient-specific risks of infection-related hospital admission were estimated for each infection cohort (not prescribed an antibiotic) using predictors. These predicted non-antibiotic risks were then applied to the antibiotic users and cohorts split into risk deciles.Findings: We found 15,719,750 diagnoses of common infections from January 2019 to March 2023. Of them, 450,215 (2·86%) were hospitalised in the 30 days after the diagnosis and 10,429,060 (66·34%) prescribed an antibiotic. There were substantial differences in observed rates of hospital admissions between the lowest and highest risk deciles. The probability of prescribed antibiotic was unrelated for LRTI and UTI’s admission risks and weakly for URTI. During the COVID-19 pandemic, the level of risk-based antibiotic prescribing reduced. Predictors were not or only weakly associated with the probability of antibiotic prescribing.Interpretation: There is a need to better target antibiotics in primary care to patients with worse prognosis and strengthen treatment guidelines in personalisation of prescribing.Funding: Health Data Research UK, National Institute for Health and Care Research.Declaration of Interest: BG and OpenSAFELY has received research funding from the Laura and John Arnold Foundation, the NHS National Institute for Health Research (NIHR), the NIHR School of Primary Care Research, NHS England, the NIHR Oxford Biomedical Research Centre, the Mohn-Westlake Foundation, NIHR Applied Research Collaboration Oxford and Thames Valley, the Wellcome Trust, the Good Thinking Foundation, Health Data Research UK, the Health Foundation, the World Health Organisation, UKRI MRC, Asthma UK, the British Lung Foundation, and the Longitudinal Health and Wellbeing strand of the National Core Studies programme; he is a Non-Executive Director at NHS Digital; he also receives personal income from speaking and writing for lay audiences on the misuse of science. AM has received consultancy fees (from https://inductionhealthcare.com) and is member of RCGP health informatics group and the NHS Digital GP data Professional Advisory Group that advises on access to GP Data for Pandemic Planning and Research (GDPPR). For the latter, he received payment for the GDPPR role.Ethical Approval: This study was approved by the Health Research Authority and NHS Research Ethics Committee [REC reference 21/SC/0287]. NHS England is the data controller of the NHS England OpenSAFELY COVID-19 Service; TPP is the data processor; all study authors using OpenSAFELY have the approval of NHS England. 

Antibiotics for Common Infections in Primary Care Before, During and after the COVID-19 Pandemic and Extent of Risk-Based Prescribing: Need for Personalised Guidelines

Background: Antimicrobial resistance (AMR) is a multifaceted global challenge, partly driven by inappropriate antibiotic prescribing. The COVID-19 pandemic impacted antibiotic prescribing for common bacterial infections. This highlights the need to examine risk of hospital admissions related to common infections, excluding COVID-19 infections during the pandemic. Methods: With the approval of NHS England, we accessed electronic health records from The Phoenix Partnership (TPP) through OpenSAFELY platform. We included patients with primary care diagnosis of common infections, including lower respiratory tract infection (LRTI), upper respiratory tract infections (URTI), and lower urinary tract infection (UTI), from January 2019 to August 2022. We excluded patients with a COVID-19 record 90 days before to 30 days after the infection diagnosis. Using Cox proportional-hazard regression models, we predicted risk of infection-related hospital admission in 30 days follow-up period after the diagnosis. Results: We found 12,745,165 infection diagnoses from January 2019 to August 2022. Of them, 80,395 (2.05%) cases were admitted to hospital in the follow-up period. Counts of hospital admission for infections dropped during COVID-19, e.g., LRTI from 3,950 in December 2019 to 520 in April 2020. Comparing those prescribed an antibiotic to those without, reduction in risk of hospital admission were largest with LRTI (adjusted odds ratio (OR) of 0.35; 95% CI, 0.35-0.36) and UTI (adjusted OR 0.45; 95% CI, 0.44-0.46), compared to URTI (adjusted OR 1.04; 95% CI, 1.03-1.06). Conclusion: Large effectiveness of antibiotics in preventing complications related to LRTI and UTI can support better targeting of antibiotics to patients with higher complication risks.

Evaluation of the impact of COVID-19 pandemic on hospital admission related to common infections

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Evaluation of the impact of COVID-19 pandemic on hospital admission related to common infections