Data mining derived treatment algorithms from the electronic medical record improve theoretical empirical therapy for outpatient urinary tract infections

Hannah Alphs Jackson, John Cashy, Ophir Frieder, Anthony J. Schaeffer*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Purpose: We determined whether data mining derived algorithms from electronic databases can improve empirical antimicrobial therapy in outpatients with a urinary tract infection. Materials and Methods: The electronic medical records from 3,308 visits associated with a positive urine culture at Northwestern's outpatient Urology and Internal Medicine clinics and Emergency Department from 2005 to 2009 were interrogated. Bacterial species and susceptibility rates for trimethoprim-sulfamethoxazole, ciprofloxacin and nitrofurantoin were compared. Using data mining techniques we created algorithms for empirical therapy of urinary tract infections and compared the theoretical outcomes from data mining derived therapy to those from conventional therapy. Results: Patients were significantly older in the Department of Urology vs Internal Medicine vs Emergency Department, and more patients in the Department of Urology were male. During the 5-year period the susceptibility rates for ciprofloxacin in the Department of Urology and trimethoprim-sulfamethoxazole in Internal Medicine decreased significantly. In the Department of Urology the susceptibility rate for nitrofurantoin was greater than for ciprofloxacin, which was greater than for trimethoprim-sulfamethoxazole. In all departments, bacteria were more resistant to trimethoprim-sulfamethoxazole than to ciprofloxacin or nitrofurantoin. All drugs were more effective in the Emergency Department and Internal Medicine than the Department of Urology. Prior resistance patterns were the strongest predictor of current susceptibility profiles. In the Department of Urology the algorithms for patients with or without prior cultures theoretically outperformed conventional therapy in men (13.2%) and women (10.1%). Conclusions: Antimicrobial resistance patterns in outpatient urinary tract infections are time dependent, and drug and site specific. Data mining directed therapy significantly improved theoretical outcomes compared to conventional therapy for Department of Urology outpatients and for female patients in the Emergency Department.

Original languageEnglish (US)
Pages (from-to)2257-2262
Number of pages6
JournalJournal of Urology
Volume186
Issue number6
DOIs
StatePublished - Dec 1 2011

Keywords

  • anti-bacterial agents
  • bacteria
  • data mining
  • drug resistance
  • urinary tract infections

ASJC Scopus subject areas

  • Urology

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