Using Patient History to Guide Empiric Antibiotic Therapy for Urinary Tract Infection

A machine-learned algorithm modeled on a large dataset predicted antibiotic resistance at a single-patient level and reduced the rate of mismatched therapy.

For the individual patient, the efficacy of antibiotic therapy is crucially dependent on matching the correct antibiotic to the pathogen's antibiotic susceptibility. Correct matching is particularly important for urinary tract infections (UTIs), for which empiric therapy is threatened by increasingly frequent resistant infections.

To address this issue, investigators analyzed data on resistance to the six most commonly prescribed antibiotics for UTIs, from all urine cultures obtained from a large Israeli health maintenance organization between 2007 and 2017.

For all six antibiotics, the risk for resistance strongly increased with patient age and with residence in a retirement home and decreased for females and with pregnancy. Among same-patie…

Reviewing Author

Neil M. Ampel, MD

Disclosures

Editorial BoardsUpToDate

Leadership Positions in Professional SocietiesCoccidioidomycosis Study Group (Past President, Secretary, Treasurer)

Neil M. Ampel, MD

Disclosures

Editorial BoardsUpToDate

Leadership Positions in Professional SocietiesCoccidioidomycosis Study Group (Past President, Secretary, Treasurer)

Using Patient History to Guide Empiric Antibiotic Therapy for Urinary Tract Infection

Topics

Reviewing Author