Hostname: page-component-7b7567c8c-2n9c9 Total loading time: 0 Render date: 2026-11-14T01:54:07.500Z Has data issue: false hasContentIssue false
  • English
  • Français

Effect of automated identification of antimicrobial stewardship opportunities for suspected urinary tract infections

Published online by Cambridge University Press:  03 October 2024

Connor R. DeriOpen the ORCID record for Connor R. Deri[Opens in a new window],
Rebekah W. Moehring,
Nicholas A. TurnerOpen the ORCID record for Nicholas A. Turner[Opens in a new window],
Justin Spivey,
Sonali D. AdvaniOpen the ORCID record for Sonali D. Advani[Opens in a new window],
Rebekah H. Wrenn and
Michael E. YarringtonOpen the ORCID record for Michael E. Yarrington[Opens in a new window]
Connor R. Deri*
Affiliation:
Department of Pharmacy, Duke University Medical Center, Durham, NC, USADuke Center for Antimicrobial Stewardship and Infection Prevention, Durham, NC, USA
Rebekah W. Moehring
Affiliation:
Duke Center for Antimicrobial Stewardship and Infection Prevention, Durham, NC, USADivision of Infectious Diseases, Department of Medicine, Duke University Medical Center, Durham, NC, USA
Nicholas A. Turner
Affiliation:
Duke Center for Antimicrobial Stewardship and Infection Prevention, Durham, NC, USADivision of Infectious Diseases, Department of Medicine, Duke University Medical Center, Durham, NC, USA
Justin Spivey
Affiliation:
Department of Pharmacy, McLeod Health Seacoast, Little River, SC, USA
Sonali D. Advani
Affiliation:
Duke Center for Antimicrobial Stewardship and Infection Prevention, Durham, NC, USADivision of Infectious Diseases, Department of Medicine, Duke University Medical Center, Durham, NC, USA
Rebekah H. Wrenn
Affiliation:
Department of Pharmacy, Duke University Medical Center, Durham, NC, USADuke Center for Antimicrobial Stewardship and Infection Prevention, Durham, NC, USA
Michael E. Yarrington
Affiliation:
Duke Center for Antimicrobial Stewardship and Infection Prevention, Durham, NC, USADivision of Infectious Diseases, Department of Medicine, Duke University Medical Center, Durham, NC, USA
*
Corresponding author: Connor R. Deri; Email: connor.deri@duke.edu

Abstract

Objective:

We aimed to determine whether automated identification of antibiotic targeting suspected urinary tract infection (UTI) shortened the time to antimicrobial stewardship (AS) intervention.

Design:

Retrospective before-and-after study.

Setting:

Tertiary and quaternary care academic medical center.

Patients:

Emergency department (ED) or admitted adult patients meeting best practice alert (BPA) criteria during pre- and post-BPA periods.

Methods:

We developed a BPA to alert AS pharmacists of potential ASB triggered by the following criteria: ED or admitted status, antibiotic order with genitourinary indication, and a preceding urinalysis with ≤ 10 WBC/hpf. We evaluated the median time from antibiotic order to AS intervention and overall percent of UTI-related interventions among patients in pre-BPA (01/2020–12/2020) and post-BPA (04/15/2021–04/30/2022) periods.

Results:

774 antibiotic orders met inclusion criteria: 355 in the pre- and 419 in the post-BPA group. 43 (35 UTI-related) pre-BPA and 117 (94 UTI-related) post-BPA interventions were documented. The median time to intervention was 28 hours (IQR 18–65) in the pre-BPA group compared to 16 hours (IQR 2–34) in the post-BPA group (P < 0.01). Despite absent pyuria, there were six cases with gram-negative bacteremia presumably from a urinary source.

Conclusions:

Automated identification of antibiotics targeting UTI without pyuria on urinalysis reduced the time to stewardship intervention and increased the rate of UTI-specific interventions. Clinical decision support aided in the efficiency of AS review and syndrome-targeted impact, but cases still required AS clinical review.

Information

Type
Original Article
Information
Antimicrobial Stewardship & Healthcare Epidemiology , Volume 4 , Issue 1 , 2024, e158
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America

Introduction

To optimize antibiotic prescribing and prevent antibiotic-associated harm, antimicrobial stewardship programs (ASPs) comprehensively review antibiotic use and prescribing, evaluating the drug, dose, duration, and indication of therapy. However, ASPs are limited in personnel resources and many centralized, expert ASP teams cannot feasibly review all patients on antibiotics each day for prospective audit and feedback. Thus, stewards benefit from electronic tools to streamline workflows, prioritize cases that are more likely to get a successful stewardship intervention, and target areas of need for their hospital’s patients. Reference Doernberg, Abbo and Burdette1

Asymptomatic bacteriuria (ASB) is a common target for ASPs given its high prevalence in vulnerable patient populations, including the elderly, long-term care residents, and those with indwelling urinary catheters. Reference Warren, Tenney, Hoopes, Muncie and Anthony2,Reference Nicolle, Gupta and Bradley3 The 2019 Infectious Diseases Society of America guidelines on the management of ASB recommend against screening and treating ASB except in patients who are pregnant or will undergo an endoscopic urologic procedure associated with mucosal trauma. Reference Nicolle, Gupta and Bradley3 Numerous studies have demonstrated a lack of benefit among ASB-treated patients with potential for harm; ASB treatment leads to an increased risk of antibiotic-associated harms, antibiotic resistance, and subsequent urinary tract infections (UTIs). Reference Nicolle, Mayhew and Bryan4–Reference Coussement, Kamar and Matignon8 Nevertheless, ASB treatment remains common. Thus, innovative antimicrobial stewardship (AS) initiatives are needed to combat unnecessary antibiotic use in ASB. Reference James and Lopez9–Reference Hitchins, Bouchard, Ingram and Orvin11

Clinical decision support (CDS) systems (CDSS) are computer applications incorporated into the electronic health record (EHR) to provide alerts and/or guidance to frontline clinicians. Reference Sim, Gorman and Greenes12 The high negative predictive value (NPV) of absent pyuria for UTIs makes the urinalysis white blood cell (WBC) count result an appealing discrete electronic trigger for CDSS. Reference Advani, Turner and Schmader13 However, to our knowledge, data are limited regarding utilizing CDSS to rapidly identify possible ASB-treated patients to optimize prescribing.

Therefore, we created an automated BPA for cases with new antibiotic orders, a genitourinary indication, and recent urinalysis showing the absence of pyuria (UA-BPA) directed to an AS pharmacist for review. This study aimed to evaluate the impact of the UA-BPA on time to AS intervention, the percent of AS interventions that were UTI-focused, and antibiotic length of therapy.

Methods

Study setting

This study was conducted at Duke University Hospital (DUH) in Durham, North Carolina (1,048 inpatient beds). DUH utilizes Epic© (2023 Epic Systems Corporation, Verona, Wisconsin; www.epic.com) as its EHR. The Antimicrobial Stewardship Evaluation Team (ASET) at DUH consists of three inpatient AS pharmacists and 1 physician FTE (shared among five adult ID physicians and 1 pediatric ID physician). ASP staffing and resource allocation did not change during the pre- and post-intervention periods. AS pharmacists and physicians routinely perform prospective audit and feedback weekdays on inpatients receiving antimicrobials but are unable to review all antibiotic patients in the facility (approximately 550 antibiotic exposed patients/day). AS pharmacists also routinely optimize therapy for patients with positive blood cultures, review customized BPAs for AS interventions (eg, bug-drug mismatch, de-escalation opportunities, etc), and perform allergy assessments with penicillin skin testing, as applicable. AS interventions are documented within the EHR.

Urinalysis and urine culture orders were separate at DUH without a reflexive urinalysis to urine culture order during the study period. Additional diagnostic stewardship interventions focused on UTIs were performed at DUH during the study period: (1.) creation of a urine culture order panel to assist in appropriate urine culture indications on 04/01/2021 and (2.) reporting of microscopic urinalysis bacteria and yeast results was discontinued on 8/30/2021. Reference Yarrington, Reynolds and Dunkerson14

Study design and participants

The UA-BPA identified ED or admitted patients with a new antibiotic order with an electronically associated “Genitourinary” indication and a preceding urinalysis (within 7 calendar days) with ≤ 5 white blood cell per high power field (WBC/hpf) (01/19/2021–04/14/2021). Antimicrobial indication is a required field within the EHR at the time of order entry. The WBC cutoff was modified to ≤ 10 WBC/hpf (04/15/2021–04/30/2022) to increase the sensitivity of the BPA. When criteria were met, a message was delivered to an ASP messaging pool which was reviewed on weekdays.

We analyzed antibiotic orders with associated AS interventions in a cohort of pre-BPA (01/01/2020–12/31/2020) and post-BPA intervention groups (04/15/2021–04/30/2022). Antibiotic orders were included for analysis if they met the following criteria: (1.) included “Genitourinary” indication, (2.) the associated patient had a preceding urinalysis with <10 WBC/hpf within 7 days (pre-BPA cohort) or the order triggered the CDS built on the same electronic criteria (post-BPA), and (3.) the associated patient’s age was ≥ 18 years. As antibiotic orders were the unit of analysis, multiple orders for an individual patient within the study period could be included. The initial post-BPA time frame using ≤ 5 WBC/hpf (01/19/2021–04/14/2021) was not included for analysis due to the brief time these criteria were used. This study was reviewed by the Institutional Review Board at DUH and determined exempt with a waiver for informed consent.

Outcomes

The primary outcome was the time, in hours, from the antibiotic order entry (timestamp of electronic signature of ordering clinician) to any AS intervention occurring within 7 days. The seven-day period for interventions was chosen as this is a commonly selected duration of antibiotic therapy for many inpatient “Genitourinary” indications, and interventions beyond this duration may have been unrelated to the initial antibiotic order. A secondary outcome was the total percent of eligible patients that underwent intervention. Additional secondary outcomes among only intervened patients included type of AS intervention, the percent of AS interventions that were UTI-related, antibiotic length of therapy from criteria “trigger,” and development of bacteremia within 30 days.

Data collection and statistical analysis

Patient demographics and BPA data were collected from Epic’s Clarity database. Electronic medication administration record (eMAR) data were obtained via the ASET operational database. Reference Moehring, Yarrington and Davis15

We used descriptive statistics to report baseline demographic characteristics. Time to intervention and length of therapy data were nonparametric with rightward skew. To assess the incidence of antibiotic stewardship intervention, cumulative incidence plots were utilized for a time-to-event analysis, where the event was time to first antibiotic stewardship intervention. Patients were censored at discharge if this occurred within 7 days of CDS “trigger,” or censored at 7 days after antibiotic order entry. A Kaplan-Meier estimator was utilized for generation of cumulative incidence curves, with differences between curves assessed using the Log-rank test. The median time from antibiotic order entry to AS intervention within seven days and antibiotic length of therapy were compared pre- to post-BPA using the Mann Whitney U test. Rates of UTI-related interventions were compared with Fisher’s exact test. All statistical analyses were performed using Python v3.11.

Results

Primary outcome

774 antibiotic orders across 768 patients met criteria and were analyzed: 355 orders in the pre-BPA group and 419 in the post-BPA group. Six patients had an antibiotic order in both the pre- and post-BPA groups, while no patients had multiple orders within each group. Baseline characteristics were similar between cohorts (Table 1). The rate of AS interventions increased from 43/355 (12.1%) in the pre-BPA group to 117/419 (27.9%) in the post-BPA group [odds ratio (OR) = 0.36; 95% confidence interval (CI), 0.24–0.52]. The Kaplan-Meier log-rank test comparing time-to-event distributions indicated a statistically significant difference (log-rank p <0.005, Figure 1).

Table 1.

Baseline patient characteristics among eligible antibiotic orders

BPA, best practice alert; IQR, interquartile range; eGFR, estimated glomerular filtration rate; WBC, white blood cell.

* Time from meeting clinical decision support criteria.

Figure 1.

Time-to-event cumulative incidence plot.

The median time to AS intervention was significantly longer in the pre-BPA group compared to the post-BPA group [28 hours (IQR 18–65) vs 16 hours (IQR 2–34), P = < 0.01, 95% CI for the difference in medians, 5–36] (Figure 2).

Figure 2.

Time to AS intervention and UTI-related interventions.

Secondary outcomes

AS interventions were categorized by type of AS intervention. Interventions could have multiple AS intervention types (eg, de-escalation and IV to PO), if applicable. “Discontinue therapy” was the most common AS intervention type (n = 102, 63.4%) (Table 2). Furthermore, the pre-BPA group had a lower rate of UTI-related AS interventions compared to the post-BPA group (9.9% vs 22.4%, OR = 2.64, 95% CI, 1.74–4.02) (Figure 1). Among intervened patients, median antibiotic length of therapy was numerically longer in the pre-BPA group compared to post-BPA although did not meet statistical significance [4 days (IQR 2.5–6.0) vs 3 days (IQR 2.0–5.0), P = 0.052, 95% CI for the difference in medians, 0.0–2.0] (Figure 3). Bacteremia within 30 days of meeting criteria was compared between groups as a balancing measure. The pre-BPA group had 16/355 (4.5%) antibiotic orders with subsequent positive blood cultures within 30 days compared to 30/419 (7.2%) in the post-BPA group (OR = 1.63, 95% CI, 0.88–3.05). Among intervened patients, 7/43 (16.3%) in the pre-BPA group and 10/117 (8.5%) in the post-BPA group had positive blood cultures within 30 days of meeting criteria (OR = 0.48, 95% CI, 0.17–1.36). Most abnormal blood culture results were unrelated to a urinary source of infection and often consistent with blood culture contamination (Supplementary Material). However, we identified six patients in our cohort with gram-negative bacteremia [Escherichia coli (n = 3), Proteus mirabilis (n = 1), Klebsiella pneumoniae (n = 1), and a polymicrobial infection with Escherichia coli and Klebsiella pneumoniae (n = 1)] presumably from a urinary source within 3 days of urinalysis despite absence of pyuria. All six patients were in the post-BPA cohort. Two (33.3%) patients received an AS intervention at the time of bacteremia: (1.) change in therapy/escalation and (2.) antimicrobial de-escalation. Three (50%) of these patients had evidence of an obstructing ureteral calculus on computed tomography scan; five (83.3%) patients had at least 10 red blood cells/hpf and all six patients had signs or symptoms of infection (eg, fever, leukocytosis, dysuria, hematuria, and/or costovertebral angle tenderness).

Table 2.

Antimicrobial stewardship intervention type by intervention period

AS, antimicrobial stewardship; BPA, best practice alert; IV, intravenous; PO, oral.

Figure 3.

Length of therapy in intervened patients among pre- and post-BPA groups.

Discussion

CDSS implementation is an effective strategy to improve stewardship-related outcome measures, including decreased antibiotic consumption, narrowed spectrum of antibiotic usage, and faster time to stewardship intervention. Reference Rittmann and Stevens16–Reference Khadem, Ergen and Salata18 Prior studies utilizing CDSS targeted at ASB have focused efforts on reducing urine testing in asymptomatic patients. Reference Yarrington, Reynolds and Dunkerson14,Reference Keller, Feldman, Smith, Pahwa, Cosgrove and Chida19 To our knowledge, use of CDSS to rapidly identify possible ASB-treated patients for AS review has not previously been reported despite the fact that ASB is a common target for hospital ASPs. We created an automated UA-BPA targeted to AS pharmacists and assessed its impact on the time to AS intervention among 774 antibiotic orders. We observed a significant reduction in the time to AS intervention in the post-BPA group compared to the pre-BPA group. In addition, we observed a reduction in antibiotic length of therapy in the post-BPA group compared to the pre-BPA group, although this was not statistically significant. The automated UA-BPA message to AS personnel more efficiently identified high-yield patients for AS review and intervention as compared with the prior AS workflow.

Antibiotic review in the form of prospective audit and feedback (PAF) is a core strategy among ASPs. In a recent survey of ASPs, a large majority of ASPs (84%) reported performing PAF with only 64% of programs having information technology add-ons to assist with stewardship reviews. Reference Doernberg, Abbo and Burdette1 ASPs are often understaffed and under-resourced to perform robust PAF and other stewardship activities for all patients receiving antimicrobials. Reference Greene, Nesbitt and Nelson20 Given these resource limitations, CDSS play a key role in ASPs to improve workflow efficiencies and overall AS reach. Prior studies have demonstrated the benefit of CDSS within AS through reduced broad-spectrum antibiotic use, Reference Thursky21 improved antibiotic susceptibilities, Reference Yong, Buising, Cheng and Thursky22 and improved clinical outcomes. Reference Leibovici, Kariv and Paul23 However, data is limited in the use of CDSS to target ASB. Alternative ASB intervention strategies, including diagnostic stewardship and reflexive urine cultures, have also been successfully performed and described. Reference Vaughn, Gupta and Petty24 There is likely more than one way to tackle inappropriate treatment of ASB and a multipronged approach may be warranted. The inappropriate treatment of ASB remains a target for ASPs across the country, including our institution. In this study, the UA-BPA enabled our AS team to rapidly identify patients for review and increase AS interventions, specifically targeting cases with possible ASB.

Among intervened patients, “discontinue therapy” was the most common recommendation from the AS team and we observed reduced antibiotic use in the post-BPA group compared to the pre-BPA group. Our study was not powered to detect a difference in median antibiotic duration; however, the observed values suggest statistical significance may be seen in a larger cohort.

Furthermore, the UA-BPA was purposefully designed to alert an AS pharmacist for review as opposed to a front-facing alert at the time of ordering by the clinician. Although the lack of pyuria has a strong NPV for ruling out UTI, it is not perfect for every clinical scenario. Though we considered making the BPA a front-facing alert, we wanted to test the criteria with an AS review back up to detect cases where criteria might falsely identify patients who have real infection rather than ASB. For instance, we observed six clinical cases of gram-negative bacteremia presumably from a urinary source despite absent pyuria. We also encountered additional cases with absent pyuria without bacteremia that necessitated antibiotic therapy (eg, obstructing stone with systemic symptoms, upper tract infection, and neutropenia). Thus, AS review provided a buffer between the alert and clinician and an opportunity for direct stewardship intervention with provider education. A front-facing alert based on these criteria might have reduced impact if ignored or, on the other hand, potentially lead to inappropriate antibiotic changes in the falsely identified cases as described above. Additionally, these cases provide a few examples where reflexive urine culturing using only pyuria would not have reflexed to a urine culture. Clinical correlation is imperative; symptomatic patients with obstructing ureteral stones likely still warrant urine cultures despite absent pyuria. Future investigations could determine the efficacy and safety of a front-facing UA-BPA on prescribing habits at the time antibiotic ordering without AS intervention.

This study had limitations. First, this was a retrospective, single-center before-and-after study with associated design limitations. Future studies might consider randomization techniques within the EHR to collect data on concurrent controls instead of a historical group. Additionally, this study was conducted at a large academic medical center with robust AS support and resources which may limit generalizability at other institutions depending on available EHR support, AS resources, and hospital size. We also removed bacteria and yeast reporting from urinalyses and created a urine culture order panel to facilitate appropriate urine culture ordering practices during the study period which may have decreased the number of ASB-treated patients, potentially biasing the results to the null hypothesis. Lastly, this study focused only on patients with absent pyuria and a genitourinary antibiotic indication. Thus, the UA-BPA alone would not capture all ASB-treated patients and should be used in conjunction with additional stewardship strategies. In addition to future studies to evaluate the BPA on front-facing alerts mentioned above, its impact in the outpatient setting may also show benefit as outpatient ASPs develop.

Automated identification of antibiotics targeting UTI with urinalysis showing absence of pyuria reduced the time to stewardship intervention and increased rate of UTI-specific interventions. The use of clinical decision support may aid in efficiency of AS review and syndrome-targeted AS impact.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/ash.2024.437.

Data availability

The data underlying this article will be shared on reasonable request to the corresponding author.

Acknowledgements

We would like to acknowledge Tray Dunkerson for his efforts and assistance with data acquisition.

Financial support

S.D.A. received support by the Duke Claude D. Pepper Older Americans Independence Center (National Institute on Aging grant no. P30AG028716) for this work.

R.W.M. has received grants to her institution from Centers for Disease Control and Prevention, and author royalties from UpToDate, Inc, unrelated to the presented work.

Competing interests

S.D.A. reports support from the Centers from Disease Control and Prevention (grant nos. 5U54CK000616-02 and SHEPheRD 75D30121D12733-D5-E003), National Institutes of Health (National Institute of Diabetes and Digestive and Kidney Diseases grant no. K12DK100024), the Society for Healthcare Epidemiology of America, and as well as prior consulting fees from Locus Biosciences, Sysmex America, GlaxoSmithKline, bioMérieux, and the Infectious Diseases Society of America. Additionally, S.D.A is an employee of GSK/ViiV Health care as of July 1, 2024 and holds stock in GlaxoSmithKline.

References

Doernberg, SB, Abbo, LM, Burdette, SD, et al. Essential resources and strategies for antibiotic stewardship programs in the acute care setting. Clin Infect Dis 2018;67:1168–1174.10.1093/cid/ciy255CrossRefGoogle ScholarPubMed
Warren, JW, Tenney, JH, Hoopes, JM, Muncie, HL, Anthony, WC. A prospective microbiologic study of bacteriuria in patients with chronic indwelling urethral catheters. J Infect Dis 1982;146:719–23.10.1093/infdis/146.6.719CrossRefGoogle ScholarPubMed
Nicolle, LE, Gupta, K, Bradley, SF, et al. Clinical practice guideline for the management of asymptomatic bacteriuria: 2019 update by the infectious diseases society of America. Clin Infect Dis 2019;68:e83–75.10.1093/cid/ciz021CrossRefGoogle ScholarPubMed
Nicolle, LE, Mayhew, WJ, Bryan, L. Prospective randomized comparison of therapy and no therapy for asymptomatic bacteriuria in institutionalized elderly women. Am J Med 1987;83:27–33.10.1016/0002-9343(87)90493-1CrossRefGoogle ScholarPubMed
Harding, GKM, Zhanel, GG, Nicolle, LE, Cheang, M; manitoba diabetes urinary tract infection study group. Antimicrobial treatment in diabetic women with asymptomatic bacteriuria. N Eng J Med 2002;347:1576–83.10.1056/NEJMoa021042CrossRefGoogle Scholar
Warren, JW, Anthony, WC, Hoopes, JM, Muncie, HL. Jr Cephalexin for susceptible bacteriuria in afebrile, long-term catheterized patients. JAMA 1982;248:454–8.10.1001/jama.1982.03330040042029CrossRefGoogle ScholarPubMed
Petty, LA, Vaughn, VM, Flanders, SA, et al. Risk factors and outcomes associated with treatment of asymptomatic bacteriuria in hospitalized patients. JAMA Intern Med 2019;179:1519–1527.CrossRefGoogle ScholarPubMed
Coussement, J, Kamar, N, Matignon, M, et al. Antibiotics versus no therapy in kidney transplant recipients with asymptomatic bacteriuria (BiRT): a pragmatic, multicentre, randomized, controlled trial. Clin Microbiol Infect 2021;27:398–405.10.1016/j.cmi.2020.09.005CrossRefGoogle Scholar
James, D, Lopez, L. Impact of a pharmacist-driven education initiative on treatment of asymptomatic bacteriuria. Am J Health Syst Pharm 2019;76:S41–S48.CrossRefGoogle ScholarPubMed
Chowdhury, F, Sarkar, K, Branche, A, et al. Preventing the inappropriate treatment of asymptomatic bacteriuria at a community teaching hospital. J Community Hosp Intern Med Perspect 2012;2(2):1–6.Google Scholar
Hitchins, MR, Bouchard, JL, Ingram, CW, Orvin, AI. Implementation of an asymptomatic bacteriuria assessment protocol for patients discharged from the emergency department. Antimicrob Steward Healthc Epidemiol 2023;3;e37.1–6.CrossRefGoogle ScholarPubMed
Sim, I, Gorman, P, Greenes, RA, et al. Clinical decision support systems for the practice of evidence-based medicine. J Am Med Inform Assoc 2001;8:527–534.10.1136/jamia.2001.0080527CrossRefGoogle ScholarPubMed
Advani, SD, Turner, NA, Schmader, KE, et al. Optimizing reflex urine cultures: Using a population-specific approach to diagnostic stewardship. Infect Control Hosp Epidemiol 2023;44:206–209.10.1017/ice.2022.315CrossRefGoogle ScholarPubMed
Yarrington, ME, Reynolds, SS, Dunkerson, T, et al. Using cl inical decision support to improve urine testing and antibiotic utilization. Infect Control Hosp Epidemiol 2023;44(10):1582–1586.Google Scholar
Moehring, RW, Yarrington, ME, Davis, AE, et al. Effects of a collaborative, community hospital network for antimicrobial stewardship program implementation. Clin Infect Dis 2021;73:1656–1663.CrossRefGoogle Scholar
Rittmann, B, Stevens, MP. Clinical decision support systems and their role in antibiotic stewardship: a systematic review. Curr Infect Dis Rep 2019;21:29.CrossRefGoogle ScholarPubMed
Ghamrawi, RJ, Kantorovich, A, Bauer, SR, et al. Evaluation of antimicrobial stewardship-related alerts using a clinical decision support system. Hosp Pharm 2017;57:679–684.CrossRefGoogle Scholar
Khadem, TM, Ergen, HJ, Salata, HJ, et al. Impact of clinical decision support system implementation at a community hospital with an existing tele-antimicrobial stewardship program. Open Forum Infect Dis 2022;9:1–8.10.1093/ofid/ofac235CrossRefGoogle Scholar
Keller, SC, Feldman, L, Smith, J, Pahwa, A, Cosgrove, SE, Chida, N. the use of clinical decision support in reducing diagnosis of and treatment of asymptomatic bacteriuria. J Hosp Med 2018;13:392–395.CrossRefGoogle ScholarPubMed
Greene, MH, Nesbitt, WJ, Nelson, GE. Antimicrobial stewardship staffing: How much is enough? Infect Control Hosp Epidemiol 2020;41:102–112.CrossRefGoogle Scholar
Thursky, K. Use of computerized decision support systems to improve antibiotic prescribing. Expert Rev Anti Infect Ther 2006;4:491–507.CrossRefGoogle ScholarPubMed
Yong, MK, Buising, KL, Cheng, AC, Thursky, KA. Improved susceptibility of Gram-negative bacteria in an intensive care unit following implementation of a computerized antibiotic decision support system. J Antimicrob Chemother 2010;65:1062–9.10.1093/jac/dkq058CrossRefGoogle Scholar
Leibovici, L, Kariv, G, Paul, M. Long-term survival in patients included in a randomized controlled trial of TREAT, a decision support system for antibiotic treatment. J Antimicrob Chemother 2013;68:2664–6.CrossRefGoogle Scholar
Vaughn, VM, Gupta, A, Petty, LA, et al. A Statewide quality initiative to reduce unnecessary antibiotic treatment of asymptomatic bacteriuria. JAMA Intern Med 2023;183:933–941.CrossRefGoogle ScholarPubMed
Figure 0

Table 1.Baseline patient characteristics among eligible antibiotic orders

Figure 1

Figure 1.Time-to-event cumulative incidence plot.

Figure 2

Figure 2.Time to AS intervention and UTI-related interventions.

Figure 3

Table 2.Antimicrobial stewardship intervention type by intervention period

Figure 4

Figure 3.Length of therapy in intervened patients among pre- and post-BPA groups.

Supplementary material: File

Deri et al. supplementary material

Deri et al. supplementary material
Download Deri et al. supplementary material(File)
File 15.7 KB