Hostname: page-component-7b7567c8c-wxvwp Total loading time: 0 Render date: 2026-11-15T12:07:35.243Z Has data issue: false hasContentIssue false
  • English
  • Français

Improved empiric antibiotic prescribing for acute cystitis with use of local urinary antibiogram and clinical decision support system

Published online by Cambridge University Press:  19 August 2020

Christopher J. ShoffOpen the ORCID record for Christopher J. Shoff[Opens in a new window],
Mary L. Townsend,
L. Gayani Tillekeratne,
Ryan D. Schulteis,
Michael E. Yarrington,
Nicholas A. Turner,
Christopher W. Woods and
Christopher J. Hostler
Christopher J. Shoff*
Affiliation:
Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, North Carolina
Mary L. Townsend
Affiliation:
Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, North CarolinaInfectious Diseases Section, Medicine Service, Durham Veterans’ Affairs Health Care System, Durham, North Carolina
L. Gayani Tillekeratne
Affiliation:
Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, North CarolinaInfectious Diseases Section, Medicine Service, Durham Veterans’ Affairs Health Care System, Durham, North Carolina
Ryan D. Schulteis
Affiliation:
Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, North CarolinaInfectious Diseases Section, Medicine Service, Durham Veterans’ Affairs Health Care System, Durham, North Carolina
Michael E. Yarrington
Affiliation:
Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, North Carolina
Nicholas A. Turner
Affiliation:
Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, North Carolina
Christopher W. Woods
Affiliation:
Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, North CarolinaInfectious Diseases Section, Medicine Service, Durham Veterans’ Affairs Health Care System, Durham, North Carolina
Christopher J. Hostler
Affiliation:
Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, North CarolinaInfectious Diseases Section, Medicine Service, Durham Veterans’ Affairs Health Care System, Durham, North Carolina
*
Author for correspondence: Christopher J. Shoff, E-mail: christopher.shoff@duke.edu
Rights & Permissions [Opens in a new window]

Abstract

An abstract is not available for this content. As you have access to this content, full HTML content is provided on this page. A PDF of this content is also available in through the ‘Save PDF’ action button.

Information

Type
Research Brief
Information
Infection Control & Hospital Epidemiology , Volume 41 , Issue 11 , November 2020, pp. 1351 - 1353
Check for updates
Copyright
© 2020 by The Society for Healthcare Epidemiology of America. All rights reserved.

Acute cystitis accounts for a significant proportion of ambulatory care visits every year in the United States. Empiric antibiotic selection varies widely among providers, even those working within the same health system. Current Infectious Diseases Society of America guidelines emphasize the use of local susceptibility data for determining initial antimicrobial therapy. Reference Gupta, Hooton and Naber1 Previous findings suggest that site-specific antibiograms (ie, urine, blood, etc) may result in improved empirical therapy. Reference Marsh, Mundy, Holter and Johnson2 We collated a local urinary antibiogram to promote recommended empiric antibiotic therapies for the treatment of cystitis. A clinical decision support system (CDSS) and order set were nested within the electronic medical record to encourage guideline-concordant prescribing. Using a quasi-experimental time series analysis, we assessed the impact of this intervention on β-lactam and fluoroquinolone prescribing rates for outpatient acute cystitis.

Methods

We performed a quasi-experimental, interrupted time series analysis to evaluate the effect of a local antibiogram-guided order set on antibiotic class selection for the treatment of outpatient urinary tract infections. Patients were treated in the Durham Veterans’ Affairs Health Care System (DVAHCS) from April 2016 through October 2019. Encounters were identified retrospectively using the following criteria: outpatient visits with appropriate International Classification of Disease, Ninth Revision (ICD-9) or ICD-10 documentation for acute cystitis, a collected urine culture, and an antibiotic prescription filled within 24 hours of visit through the DVAHCS pharmacy. Prescribing data were associated with the appropriate encounter. In total, 5,517 prescriptions were analyzed.

The study consisted of a preintervention phase (April 2016 through July 2018), an intervention period (August 2018), and a postintervention phase (September 2018 through October 2019). During the preintervention phase, a local urinary antibiogram, stratified by admission status, was created from all urine cultures across DVAHCS to inform empiric antibiotic choices. Escherichia coli was the most commonly isolated pathogen (n = 531 isolates, 41% of all outpatient urine cultures), with the following susceptibility profile: cefazolin 91%, ciprofloxacin 71%, and trimethoprim-sulfamethoxazole 77%. We then developed a CDSS highlighting nitrofurantoin and β-lactams (cephalexin, amoxicillin-clavulanate) as preferred agents over fluoroquinolones for the treatment of uncomplicated acute cystitis. In August 2018, the CDSS was integrated within the facility-wide computerized patient record system (CPRS).

The primary outcomes measured were changes in monthly proportions of antibiotic classes prescribed for cystitis. Classes were defined as fluoroquinolones (ciprofloxacin and levofloxacin), β-lactams (cephalexin, cefuroxime, amoxicillin, and amoxicillin-clavulanate), nitrofurantoin, and trimethoprim-sulfamethoxazole. Median monthly proportions and interquartile ranges were compared by unpaired Wilcoxon rank-sum test. Antibiotic concordance was compared in a subset of prescriptions with cultures using Fisher’s exact test. To control for unmeasured factors, we performed a segmented interrupted time series (ITS) analysis using a Poisson generalized linear regression model to assess changes in antibiotic prescribing associated with the intervention. All statistical analyses were performed using R version 3.6.3 software (R Foundation for Statistical Computing, Vienna, Austria).

Results

Patient age, race, and gender were similar before and after the intervention (Supplementary Table 1 online). Prior to implementation, monthly fluoroquinolone prescriptions accounted for a median of 45.0% of all antibiotics prescribed for outpatient treatment of acute cystitis (IQR, 41.0–49.0). After the intervention, fluoroquinolone prescriptions comprised a median of 32.0% (IQR, 28.9–35.1) of all antibiotics prescribed for cystitis monthly (P < .001). Conversely, prescriptions for β-lactams increased from a monthly median of 14.0% (IQR, 11.5%–16.5%) of all chosen antibiotics preintervention to a median of 24.5% of antibiotic prescriptions (IQR, 21.6%–27.4%; P < .001). Nitrofurantoin and trimethoprim-sulfamethoxazole prescribing were unaltered by implementation of the acute cystitis order set. In the ITS, CDSS implementation resulted in a −20.7% level change (95% CI, −33.8% to −7.5%; P = .002) and −1.4% change in slope (95% CI, −3.0% to 0.2%; P = .09) in fluoroquinolone prescribing for cystitis. The ITS demonstrated a 28.5% level change (95% CI, 15.5% to 41.7%; P < .001) and 1.2% change in slope (95% CI, −0.3% to 2.8%; P = .13) with regard to β-lactam prescriptions (Fig. 1). In a subset of patients treated in the emergency department, microbiological concordance was unchanged (79.1% vs 77.1%; P = .6514) (Supplementary Table 2 online).

Fig. 1.

Monthly antibiotic selection as a proportion of all antibiotics prescribed for cystitis. The data points represent the observed proportions and the solid line represents the Poisson regression model.

Discussion

Prior to our intervention, fluoroquinolones accounted for the plurality of antibiotics prescribed for acute cystitis, despite an unfavorable susceptibility profile. CDSS implementation resulted in significant changes in prescribing patterns of fluoroquinolones and β-lactams for treatment of acute cystitis, resulting in greater concordance with the local urinary antibiogram. As nitrofurantoin and trimethoprim-sulfamethoxazole prescribing remained constant, we hypothesize that β-lactam therapies replaced fluoroquinolones for the treatment of uncomplicated cystitis. This intervention most likely succeeded by leveraging order entry, allowing providers to easily select guideline-recommended antibiotics, while simultaneously discouraging fluoroquinolone use.

Our results are consistent with those of prior studies demonstrating a positive effect of CDSS on antibiotic prescribing. Reference Hecker, Fox and Son3,Reference Patros, Sabol, Paniagua and Lans4 To our knowledge, however, our study is the first to assess the impact of this intervention across the entire ambulatory network of a healthcare system.

This study has several limitations: (1) It was conducted at a single institution with a predominantly male cohort, these findings lack generalizability to other settings. (2) The provider-level data regarding utilization of the CDSS were not available, preventing a direct linkage between CDSS usage and antibiotic choice. (3) The use of ICD codes may have over- or underestimated the true frequency of acute cystitis associated with antibiotic prescriptions; however, any discrepancy likely remained constant throughout the study period, and the observed trends are not likely related to this limitation.

In summary, CDSS combined with local urine antibiograms, even without prescriber education or audit and feedback, can be an effective tool for antimicrobial stewardship.

Supplementary material

To view supplementary material for this article, please visit https://doi.org/10.1017/ice.2020.357

Acknowledgments

We would like to recognize all the support from information technology, infection prevention, microbiology, nursing, and leadership at the Durham Veterans’ Affairs Medical Center while implementing this initiative.

Financial support

No financial support was provided relevant to this article.

Conflicts of interest

All authors report no conflicts of interest relevant to this article.

Footnotes

PREVIOUS PRESENTATION: A preliminary version of this work was accepted for presentation at the European Congress of Clinical Microbiology and Infectious Disease on April 19, 2020, in Paris, France.

References

Gupta, K, Hooton, TM, Naber, KG, et al. International clinical practice guidelines for the treatment of acute uncomplicated cystitis and pyelonephritis in women: a 2010 update by the Infectious Diseases Society of America and the European Society for Microbiology and Infectious Diseases. Clin Infect Dis 2011;52(5):e103–e120.10.1093/cid/ciq257CrossRefGoogle Scholar
Marsh, KJ, Mundy, L, Holter, JJ, Johnson, JR. Analysis of urine-specific antibiograms from veterans to guide empiric therapy for suspected urinary tract infection. Diagn Microbiol Infect Dis 2019;95:114874.10.1016/j.diagmicrobio.2019.114874CrossRefGoogle ScholarPubMed
Hecker, MT, Fox, CJ, Son, AH, et al. Effect of a stewardship intervention on adherence to uncomplicated cystitis and pyelonephritis guidelines in an emergency department setting. PLoS One 2014;9(2):e87899.10.1371/journal.pone.0087899CrossRefGoogle Scholar
Patros, C, Sabol, M, Paniagua, A, Lans, D. Implementation and evaluation of an algorithm-based order set for the outpatient treatment of urinary tract infections in the spinal cord injury population in a VA Medical Center. J Spinal Cord Med 2018;41:192–198.10.1080/10790268.2017.1324355CrossRefGoogle Scholar
Figure 0

Fig. 1.Monthly antibiotic selection as a proportion of all antibiotics prescribed for cystitis. The data points represent the observed proportions and the solid line represents the Poisson regression model.

Supplementary material: File

Shoff et al. supplementary material

Tables S1-S2

Download Shoff et al. supplementary material(File)
File 13.6 KB