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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 > Antibiotic use – if you use less antibiotic in general medicine are there adverse consequences?

IORD Project

Antibiotic use – if you use less antibiotic in general medicine are there adverse consequences?

COMPLETED
IORD category: Antimicrobial Resistance and Antibiotics
Chief Investigator: Profs Sarah Walker & Tim Peto
Sponsor: OUH
Research location: OUH NHS Trust
Approval date: 19 Mar 2015

Reducing antibiotic use to combat antimicrobial resistance is a priority. We want to know if we can do this safely and without putting patients at undue risk. Antibiotic use was measured over 1 week in an intensive audit of practice in the Acute/General Medicine service at the John Radcliffe. This showed that one Consultant team (Consultant A) used substantially less antibiotics compared to other teams. There was no difference in poor outcomes (death or admission to hospital) over this 1 week audit, but this is a short period of time. Every time a patient is admitted, a large amount of routine electronic data is recorded, including when they were admitted, what they were treated for, if the patient died, and which Consultant team they were treated by. We want to use this information over a longer 3 year period to see if more patients died or were admitted to hospital when they were managed under Consultant A compared to other consultant teams. If there is no difference between Consultant A and other teams, it suggests that their strategy of using substantially fewer antibiotics is safe, and can be used to reduce antibiotic use in General Medicine without risk to patients.

See manuscript on BMJ
See also blog on Modernising Medical Microbiology

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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