PT  - JOURNAL ARTICLE
AU  - Idan Yelin
AU  - Olga Snitser
AU  - Gal Novich
AU  - Rachel Katz
AU  - Ofir Tal
AU  - Miriam Parizade
AU  - Gabriel Chodick
AU  - Gideon Koren
AU  - Varda Shalev
AU  - Roy Kishony
TI  - Personal clinical history predicts antibiotic resistance in urinary tract infections
AID  - 10.1101/384842
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 384842
4099  - http://biorxiv.org/content/early/2018/08/09/384842.short
4100  - http://biorxiv.org/content/early/2018/08/09/384842.full
AB  - The prevalence of antibiotic resistance in urinary tract infections (UTIs) often renders the prescribed antimicrobial treatment ineffective, highlighting the need for personalized prediction of resistance at time of care. Here, crossing a 10-year longitudinal dataset of over 700,000 community-acquired UTIs with over 6,000,000 personally-linked records of antibiotic purchases, we show that the resistance profile of infections can be predicted based on patient-specific demographics and clinical history. Age, gender, and retirement home residence had strong, yet differential and even non-monotonic, associations with resistance to different antibiotics. Resistance profiles were also associated with the patient’s records of past urine samples and antibiotic usage, with these associations persisting for months and even longer than a year. Drug usage selected specifically for its own cognate resistance, which led indirectly, through genetic linkage, also to resistance to other, even mechanistically unrelated, drugs. Applying machine learning models, these association patterns allowed good personalized predictions of resistance, which could inform and better optimize empirical prescription of antibiotics.