Personal clinical history predicts antibiotic resistance of urinary tract infections

  08 July 2019

Antibiotic resistance is prevalent among the bacterial pathogens causing urinary tract infections. However, antimicrobial treatment is often prescribed ‘empirically’, in the absence of antibiotic susceptibility testing, risking mismatched and therefore ineffective treatment. Here, linking a 10-year longitudinal data set of over 700,000 community-acquired urinary tract infections with over 5,000,000 individually resolved records of antibiotic purchases, we identify strong associations of antibiotic resistance with the demographics, records of past urine cultures and history of drug purchases of the patients. When combined together, these associations allow for machine-learning-based personalized drug-specific predictions of antibiotic resistance, thereby enabling drug-prescribing algorithms that match an antibiotic treatment recommendation to the expected resistance of each sample. Applying these algorithms retrospectively, over a 1-year test period, we find that they greatly reduce the risk of mismatched treatment compared with the current standard of care. The clinical application of such algorithms may help improve the effectiveness of antimicrobial treatments.

Further reading: Nature Medicine
Author(s): Idan Yelin, Olga Snitser, Gal Novich, Rachel Katz, Ofir Tal, Miriam Parizade, Gabriel Chodick, Gideon Koren, Varda Shalev & Roy Kishony
Healthy Patients   Smart Innovations  
Back

OUR UNDERWRITERS

Unrestricted financial support by:

Antimicrobial Resistance Fighter Coalition

Bangalore Bioinnovation Centre

INTERNATIONAL FEDERATION PHARMACEUTICAL MANUFACTURERS & ASSOCIATIONS

BD





AMR NEWS

Every two weeks in your inbox

Because there should be one newsletter that brings together all One Health news related to antimicrobial resistance: AMR NEWS!

Subscribe

What is going on with AMR?
Stay tuned with remarkable global AMR news and developments!

Keep me informed