Infection Control & Hospital Epidemiology (2018), 39, 1473–1475 doi:10.1017/ice.2018.246
Concise Communication
Utilizing the electronic health record to construct antibiograms for previously healthy children with urinary tract infections
Yusuf Y. Chao MD1,2, Larry K. Kociolek MD, MSCI1,2, Xiaotian T. Zheng MD, PhD1,2, Tonya Scardina PharmD2 and Sameer J. Patel MD, MPH1,2 1Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, Illinois and 2Ann & Robert H. Lurie Children’s Hospital of Chicago,
Chicago, Illinois Abstract
Traditional antibiograms can guide empiric antibiotic therapy, but they may miss differences in resistance across patient subpopulations. In this retrospective descriptive study, we constructed and validated antibiograms using International Classification of Disease, Tenth Revision (ICD-10) codes and other discrete data elements to define a cohort of previously healthy children with urinary tract infections. Our results demonstrate increased antibiotic susceptibility. This methodology may be modified to create other syndrome-specific antibiograms.
(Received 30 May 2018; accepted 29 August 2018; electronically published October 10, 2018)
Institutional antibiograms are recommended to guide empiric antibiotic therapy,1,2 but they have several limitations. Because traditional antibiograms provide organism-specific suscept- ibilities, they do not estimate the risk of organism-drug mismatch related to empiric antibiotic selection prior to culture identifica- tion and susceptibilities. Second, composite antimicrobial sus- ceptibility data reported in traditional antibiograms may miss differences in resistance across clinical syndromes (eg, respiratory tract vs bloodstream infections). Furthermore, antibiograms in the ambulatory setting do not exclude patients with chronic medical conditions. Thus, antibiograms may overestimate resis- tance for patients who were previously healthy, which is parti- cularly important in the ambulatory setting because microbiological susceptibility data may be less accessible. The objective of our study was to use the electronic health record (EHR) to generate and validate syndrome-specific antibiograms for urinary tract infections (UTIs) in previously healthy children in the outpatient setting.
Methods
This retrospective descriptive study of antimicrobial susceptibilities was conducted at Ann & Robert H. Lurie Children’s Hospital in
Author for correspondence: Yusuf Y. Chao, MD, Ann & Robert H. Lurie Children’s
Hospital of Chicago, Department of Pediatrics, Division of Infectious Diseases, North- western University Feinberg School of Medicine, 225 E. Chicago Avenue, Chicago, IL 60611. E-mail:
yusuf.chao@
phhs.org PREVIOUS PRESENTATION: Some findings reported in this manuscript were
presented as preliminary data in an abstract/poster at the IDWeek 2017 conference on October 4, 2017, in San Diego, California. The abstract was included in Open Forum Infectious Diseases, Volume 4, on October 4, 2017.
Cite this article: Chao YY, et al. (2018). Utilizing the electronic health record to
construct antibiograms for previously healthy children with urinary tract infections Infection Control & Hospital Epidemiology 2018, 39, 1473–1475. doi: 10.1017/ice.2018.246
© 2018 by The Society for Healthcare Epidemiology of America. All rights reserved.
Chicago, Illinois, a 288-bed tertiary-care children’s hospital with more than 12,000 admissions and 60,000 emergency department visits each year. Study subjects included all pediatric patients aged ≤21 years with urine isolates positive for gram-negative bacilli (GNB) collected from all outpatient locations, including the emergency department, from October 2, 2016, to May 1, 2017. If a patient had duplicate isolates for the same organism during this period, only the first isolate was included for the study. We defined a ‘healthy’ outpatient cohort by excluding children
who had (1) urine collected from pediatric subspecialty clinics, (2) complex chronic conditions defined by International Classifica- tion of Disease, Tenth Revision (ICD-10) codes,3 (3) prior hospital admissions within the prior 12 months, or (4) antibiotic use within 90 days. Patients not included in this EHR-derived healthy cohort were classified as ‘complex.’ Patient identification and data extraction were automated using Vigilanz software (Vigilanz, Minneapolis, MN), a web-based clinical support tool, using dis- crete data queries from administrative, pharmacy, and micro- biology records.
Weighted-incidence syndromic antibiograms (WISAs) for
urinary GNB were created by calculating a weighted average of individual species susceptibilities, similar to the methodology used to create combination antibiograms described previously.4 Antimicrobial susceptibilities were determined for the following study antibiotics: amoxicillin/clavulanate, ampicillin, cefazolin, ceftriaxone, and trimethoprim/sulfamethoxazole. WISAs were generated for healthy patients using our EHR-derived definition, complex patients, and for all patients combined. The sensitivity and specificity of the EHR-derived definition to distinguish between healthy and complex patients were determined by a sequential manual review of 50% of the patient charts. Suscept- ibility differences between various antibiograms (all outpatients, healthy cohort, and complex cohort) were analyzed using the Fisher exact test.
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