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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 > Can we estimate resistance in bloodstream infections from resistance in other types of specimen?

IORD Project

Can we estimate resistance in bloodstream infections from resistance in other types of specimen?

COMPLETED
IORD category: Antimicrobial Resistance and Antibiotics, Specific Infections
Chief Investigator: Prof Tim Peto
Sponsor: OUH
Research location: Oxford University
Approval date: 17 Sep 2018

In order to reduce the threat of antimicrobial resistance, a first key step is to understand how much resistance there is. This is typically done by looking at bacteria causing serious infections, particularly bloodstream infections. However, growing bacteria from blood usually requires sophisticated machines which heat samples of blood in special nutrients. These machines are not always available in low-income countries where the effects of increasing antimicrobial resistance are being felt most.

It is much easier to grow bacteria from other types of patient sample. What we want to do is work out how similar the patterns of resistance to common antibiotics that we see in bacteria grown from blood are to bacteria grown from other types of samples. We plan to look at this for two very common types of bacteria causing serious infections, Staphylococcus aureus and Escherichia coli. If we can use resistance patterns from bacteria grown from common types of specimens to predict resistance in bacteria grown from blood, then this would provide a new method for low-income countries to monitor antimicrobial resistance.

See publication: Antimicrobial resistance in commensal opportunistic pathogens isolated from non-sterile sites can be an effective proxy for surveillance in bloodstream infections.

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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