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Research Theme

Modernising Medical Microbiology and Big Infection Diagnostics

Home > Research Themes Overview > Modernising Medical Microbiology and Big Infection Diagnostics > Infections in Oxfordshire Research Database > IORD Projects > The microbiological features associated with greater risk of recurrent UTI

IORD Project

The microbiological features associated with greater risk of recurrent UTI

ONGOING
IORD category: Specific Infections
Chief Investigator: Dr Gail Hayward
Sponsor: OUH
Research location: Oxford University
Approval date: 10 Apr 2020

Around half of all women will suffer from at least one urinary tract infection (UTI) in their lifetime, and it is the most common bacterial infection treated by General Practitioners (GPs). Up to 800,000 women in the UK suffer from frequent UTIs each year. Such ‘recurrent UTI’ causes painful and distressing symptoms, difficulty in managing daily activities, and problems with sexual relationships. However, we still know very little about why women develop recurrent UTIs, which makes it harder to improve treatment options for these women. We don’t yet understand whether the type of bacteria causing the infection can influence whether women get more or less infections in the future or whether these recurrent UTI’s are resistant to antibiotics.

GPs often send urine samples from women with suspected urine infection to a laboratory where they try to grow the bacteria causing infection (called “culture”). We will have all of the results of these culture tests from the last 10 years available to us. In women who have had multiple cultures consistent with recurrent UTIs, we will describe which bacteria most commonly cause the infections. This study should help us see if women get infections with the same type of bacteria multiple times. Finally, we will explore whether infections with certain types of bacteria have worse healthcare outcomes for women than infections with other types of bacteria.

Modernising Medical Microbiology and Big Infection Diagnostics

  • Modernising Medical Microbiology and Big Infection Diagnostics
  • Sub-theme 1: Novel rapid, high-throughput diagnostic workflows for infection
  • Sub-theme 2: Big data-led infection diagnosis and management strategies
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