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Novel method of calculating adjusted antibiotic use by microbiological burden

Published online by Cambridge University Press:  28 January 2021

Hana R. Winders*
Affiliation:
University of South Carolina College of Pharmacy, Columbia, South Carolina
Majdi N. Al-Hasan
Affiliation:
University of South Carolina School of Medicine, Columbia, South Carolina Prisma Health-Midlands, Columbia, South Carolina
Bruce M. Jones
Affiliation:
St Joseph’s/Candler Health System, Savannah, Georgia
Darrell T. Childress
Affiliation:
East Alabama Medical Center, Opelika, Alabama
Kayla R. Stover
Affiliation:
University of Mississippi Medical Center and School of Pharmacy, Jackson, Mississippi
Benjamin B. Britt
Affiliation:
Lexington Medical Center, West Columbia, South Carolina
Elias B. Chahine
Affiliation:
Palm Beach Atlantic University Gregory School of Pharmacy, West Palm Beach, Florida
Suetping Lau
Affiliation:
Orlando Health, Orlando, Florida
Pamela D. Andrews
Affiliation:
Orlando Health, Orlando, Florida
Shauna Jacobson Junco
Affiliation:
Orlando Health, Orlando, Florida
Rebekah H. Wrenn
Affiliation:
Duke University Hospital, Durham, North Carolina
Brad J. Crane
Affiliation:
Blount Memorial Hospital, Maryville, Tennessee
Jordan R. Wong
Affiliation:
Grady Health System, Atlanta, Georgia
Megan M. Seddon
Affiliation:
Sarasota Memorial Health Care System, Sarasota, Florida
Christopher M. Bland
Affiliation:
University of Georgia College of Pharmacy, Savannah, Georgia
Shawn H. MacVane
Affiliation:
Medical University of South Carolina, Charleston, South Carolina
Geneen M. Gibson
Affiliation:
St Joseph’s/Candler Health System, Savannah, Georgia
P. Brandon Bookstaver
Affiliation:
University of South Carolina College of Pharmacy, Columbia, South Carolina Prisma Health-Midlands, Columbia, South Carolina
*
Author for correspondence: Hana R. Winders, E-mail: hana.winders@prismahealth.org

Abstract

Objective:

To determine the usefulness of adjusting antibiotic use (AU) by prevalence of bacterial isolates as an alternative method for risk adjustment beyond hospital characteristics.

Design:

Retrospective, observational, cross-sectional study.

Setting:

Hospitals in the southeastern United States.

Methods:

AU in days of therapy per 1,000 patient days and microbiologic data from 2015 and 2016 were collected from 26 hospitals. The prevalences of Pseudomonas aeruginosa, extended-spectrum β-lactamase (ESBL)–producing bacteria, methicillin-resistant Staphylococcus aureus (MRSA), and vancomycin-resistant enterococci (VRE) were calculated and compared to the average prevalence of all hospitals in the network. This proportion was used to calculate the adjusted AU (a-AU) for various categories of antimicrobials. For example, a-AU of antipseudomonal β-lactams (APBL) was the AU of APBL divided by (prevalence of P. aeruginosa at that hospital divided by the average prevalence of P. aeruginosa). Hospitals were categorized by bed size and ranked by AU and a-AU, and the rankings were compared.

Results:

Most hospitals in 2015 and 2016, respectively, moved ≥2 positions in the ranking using a-AU of APBL (15 of 24, 63%; 22 of 26, 85%), carbapenems (14 of 23, 61%; 22 of 25; 88%), anti-MRSA agents (13 of 23, 57%; 18 of 26, 69%), and anti-VRE agents (18 of 24, 75%; 15 of 26, 58%). Use of a-AU resulted in a shift in quartile of hospital ranking for 50% of APBL agents, 57% of carbapenems, 35% of anti-MRSA agents, and 75% of anti-VRE agents in 2015 and 50% of APBL agents, 28% of carbapenems, 50% of anti-MRSA agents, and 58% of anti-VRE agents in 2016.

Conclusions:

The a-AU considerably changes how hospitals compare among each other within a network. Adjusting AU by microbiological burden allows for a more balanced comparison among hospitals with variable baseline rates of resistant bacteria.

Type
Original Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America

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Footnotes

PREVIOUS PRESENTATION. The preliminary results of this study were presented in part as an oral abstract at the SHEA Annual Spring Meeting on April 19, 2018, in Portland, Oregon.

References

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Supplementary material: PDF

Winders et al. supplementary material

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