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The impact of implementation of rapid blood culture identification panels on antimicrobial optimization: a retrospective cohort study

Published online by Cambridge University Press:  16 April 2024

Tyler Martin
Affiliation:
Department of Pharmacy, Grady Health System, Atlanta, GA, USA
Eli Wilber*
Affiliation:
Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
Shreena Advani
Affiliation:
Department of Pharmacy, Grady Health System, Atlanta, GA, USA
Joseph Torrisi
Affiliation:
Department of Pharmacy, Grady Health System, Atlanta, GA, USA
Manish Patel
Affiliation:
Department of Pharmacy, Grady Health System, Atlanta, GA, USA
Paulina A. Rebolledo
Affiliation:
Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USAHubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USAGrady Healthcare System, Atlanta, GA, USA
Yun F. Wang
Affiliation:
Clinical Microbiology Laboratory, Grady Health System, Atlanta, GA, USADepartment of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA, USA
Sheetal Kandiah
Affiliation:
Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USAGrady Healthcare System, Atlanta, GA, USA
*
Corresponding author: Eli Wilber; Email: eli.wilber@emory.edu

Abstract

Objective:

Determine the impact of limited implementation of a rapid blood culture identification (BCID) panel.

Design:

Retrospective cohort study.

Methods:

From February to April 2022, positive blood cultures identified via e-Plex BCID (Roche, Carlsbad, CA) were compared to those identified using standard microbial identification techniques. The primary outcomes assessed were time to optimal therapy, time to de-escalation of anti-MRSA (methicillin-resistant Staphylococcus aureus) agents, and time to de-escalation of anti-pseudomonal agents. Additional analysis investigated the impact of the availability of antimicrobial stewardship program support. This study was conducted at Grady Health System, a large metropolitan safety-net hospital in the southeastern United States.

Results:

A total of 253 blood cultures were included in this study (153 BCID and 100 standard). Blood culture identification use was associated with a reduction in median time to optimal antimicrobial therapy (43.4 vs 72.1 h, P < .001) and median time to de-escalation of anti-MRSA agents (27.7 vs 46.7 h, P = .006), and a trend towards reduction of median time to de-escalation of anti-pseudomonal agents (38.8 vs 54.8 h, P = .07). These reductions persisted when controlling for patient age, sex, intensive care unit status, Charlson Comorbidity Index, and antimicrobial stewardship program availability.

Conclusions:

Despite restricted use and lack of 24/7 antimicrobial stewardship program availability, BCID panel utilization was associated with earlier initiation of optimal therapy and pathogen identification with subsequent de-escalation of broad-spectrum antimicrobials, as compared to standard antimicrobial techniques. This suggests the potential for benefit from adopting novel diagnostic technologies outside of idealized fully-resourced settings.

Information

Type
Original Article
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
Figure 0

Figure 1.Study flow diagram of the included and excluded positive blood cultures. The subgroup analysis excluded patients receiving antimicrobials targeted at a different infection site.

Figure 1

Table 1.Baseline characteristics of the standard identification and blood culture identification groups

Figure 2

Figure 2.Primary outcomes of the primary and subgroup analyses. Primary outcomes of the primary analysis (Panel A) and the subgroup analysis (Panel B) with associated p-values. The subgroup analysis excluded patients receiving antimicrobials targeted at a different infection site.

Figure 3

Table 2.Secondary outcomes of the primary and subgroup analyses

Figure 4

Table 3.Multivariablea regression analysis of blood culture identification impact

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