Title: Precision antibiotic prescribing for urinary tract infection in hospital
Funder: Department of Health and Social Care

Abstract: Background: Reducing inappropriate antibiotic prescribing to tackle antimicrobial resistance (AMR) is a global health priority. This is widely acknowledged, yet physicians continue to prescribe antibiotics for patients in whom bacterial infection is unlikely. Antimicrobial stewardship is the only approach with potential to immediately impact on AMR by preserving the lifespan of existing antibiotics. The major burden of drug-resistant infection is in hospital, but antibiotic prescribing in this setting is poorly understood due to lack of individual-level prescribing data. Without this information we cannot estimate the number of patients who could be safely treated without antibiotics, or devise evidence based stewardship initiatives to improve prescribing quality. Aims: I propose a novel, precision medicine approach to antibiotic prescribing in hospital, using high resolution electronic health records (EHRs) and host transcriptomic bio-signatures to guide prescribing decisions. I propose to test the safety and acceptability of this approach in a proof of concept study. I focus on urinary tract infection (UTI) syndromes as an exemplar of antibiotic overuse in hospital. Plan of investigation: I will analyse electronic health records (EHRs) from more than 9000 patients at University Hospital Birmingham to describe the clinical course, management and outcomes of patients with suspected UTI syndromes in order to estimate antibiotic overuse. Applying conventional statistical and machine learning approaches in parallel to these EHRs, I will develop a series of risk models to predict each patient's need for antibiotic treatment. For individuals who require antibiotics, I will predict the choice and duration of antibiotic treatment based on the patient's clinical syndrome, their risk of drug-resistance and the severity of their illness. In order to determine whether inclusion of omic data can improve predictive accuracy, I will collect RNA samples from peripheral blood from 300 patients with UTI syndromes. I will investigate whether previously defined host transcriptomic bio-signatures can discriminate between patients with different UTI syndromes which merit different types and duration of antibiotic treatment. Provided my models can adequately discriminate patients who are unlikely to require antibiotic treatment for UTI, I will use them to identify a target population of patients for inclusion in a proof of concept study. This trial will be designed in collaboration with patients, members of the public and healthcare professionals to test the safety and acceptability of an intervention (such as antibiotic discontinuation at 24 hours following hospital admission) to reduce unnecessary antibiotic prescribing. If this study suggests the intervention is feasible, my work will lay the foundation for a future randomised controlled trial of risk-assessed antibiotic prescribing beyond this fellowship. Benefits to patients and the NHS: This work will inform the development of novel diagnostic and risk prediction tools to reduce inappropriate antibiotic prescribing in hospital. It will benefit patients by reducing the number of people who get side effects from unnecessary antibiotics and will benefit society by reducing antibiotic resistance. In addition, we will develop a unique resource of clinical samples linked to high-quality electronic health records as the basis for a future program of research on infection and AMR in hospital.

Lay abstract: Many infections do not need to be treated with antibiotics, yet doctors still prescribe antibiotics for patients who are unlikely to need them. This causes antibiotic resistance, and it makes antibiotics that we rely on for many medical treatments potentially useless. My goal is to use big data to make it easier for doctors to work out which patients really need an antibiotic and which antibiotics to prescribe. This will benefit patients by reducing the number of people who get side effects from unnecessary antibiotics. It will also benefit society by reducing antibiotic resistance. To test whether my approach works, I am focusing on patients with urinary tract infection (UTI). This is a common reason for patients to be admitted to hospital. Most patients in hospital with possible UTI are treated with antibiotics, but many do not need them because UTI is actually frequently mis-diagnosed. My first step is to analyse detailed computerised medical records from more than 9000 patients with possible urinary tract infection at University Hospital Birmingham (UHB). I am using data from Birmingham hospital because they have very detailed and high quality electronic medical records. I will learn about what happens to patients with possible urinary tract infection in hospital and estimate how often antibiotics are prescribed when they are not needed. Next I will develop computer simulations to predict who really needs an antibiotic when they come into hospital, and which antibiotic to choose. Because we don't know the best way to create this model I will use two different approaches side-by-side: a conventional approach (statistics) and a new approach (machine learning). I will then compare the two approaches. Machine learning lets the computer identify patterns in the data to make predictions and this means it can be really useful for complicated datasets like electronic medical records. In the NHS we are moving towards personalised medicine where patients can receive tailor-made treatments that are based not only on their medical history but also on their genes. This can be done by collecting genetic measurements, like DNA from patient blood samples. Incorporating these genetic measurements could improve my risk prediction models. To test this idea, I will collect blood samples from 300 patients with possible urinary tract infection and look for gene signatures or patterns that help identify which patients need antibiotics. Patients with bacterial infection that needs antibiotic treatment would be expected to express (or activate) different genes to patients without bacterial infection. So this could provide a potential means to identify patients who do not require antibiotics. For patients who do not have a bacterial infection antibiotic treatment does more harm than good. However, doctors are understandably reluctant to delay antibiotic treatment in the emergency department, in case they fail to treat a sick patient. An alternative approach is to stop antibiotics as soon as possible after they have been started in patients who do not need them. Provided my research can identify patients who are unlikely to need antibiotics, I will test whether patients and doctors think it is safe and acceptable to stop antibiotics early in these patients. To design this feasibility study I will work with a group of patients and healthcare staff. Patients who have been started on antibiotics (but are unlikely to need them) will be selected by chance to either a) stop or to b) continue antibiotics. We will then measure what happens to all of these patients over the following 48 hours in hospital. If there is no difference between the two groups it means that it is likely to be safe to stop antibiotics early. If this is the case, we will apply for more research funding to test this approach in a large clinical trial. This work will develop new ways to diagnose infection and to reduce antibiotic resistance. This will help to preserve our existing and future antibiotics benefitting or patients and society.

Research activity codes: 6.1 PHARMACEUTICALS
Health categories: INFECTION