Evidence map›Paper›PMID 41527829›Full record

ArticleInfection control and hospital epidemiology2026

Optimizing facility-specific urinary weighted-incidence syndromic antibiograms for nursing homes.

Lindsay Noelle Taylor, Ronald Gangnon, Michael Howe, Federico Perez, Sally Jolles, Jon Furuno, David Nace, Robin Jump, Christopher Crnich

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Article in Infection control and hospital epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Lindsay Noelle Taylorhttps://ror.org/03ydkyb10University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.ORCID https://orcid.org/0000-0002-6622-4379
Ronald Gangnonhttps://ror.org/03ydkyb10University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.ORCID https://orcid.org/0000-0003-2587-6714
Michael HoweDepartment of Wildland Resources, Utah State University, Logan, UT, USA.ORCID https://orcid.org/0000-0002-9694-0835
Federico PerezGeriatric Research Education and Clinical Center (GRECC), Veterans Affairs Northeast Ohio Healthcare System, Cleveland, Ohio, USA.
Sally Jolleshttps://ror.org/03ydkyb10University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
Jon FurunoDepartment of Pharmacy Practice, Oregon State University College of Pharmacy, Portland, OR, USA.ORCID https://orcid.org/0000-0002-8853-5733
David NaceDepartment of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Robin JumpGeriatric Research Education and Clinical Center (GRECC), Veterans Affairs (VA) Pittsburgh Healthcare System, Pittsburgh, PA, USA.ORCID https://orcid.org/0000-0001-5601-8996
Christopher Crnichhttps://ror.org/03ydkyb10University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.ORCID https://orcid.org/0000-0002-9320-9262

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo develop an approach for creating facility-specific urinary antibiograms accounting for the low number of isolates recovered in nursing homes (NHs).

designRetrospective analysis of urine culture data collected in NHs in five states.

settingData on 5097 urine culture isolates collected across 59 study NHs from January 1, 2020 to December 31, 2021. Four consulting microbiology laboratories served the study homes.

methodsWe compared a Clinical and Laboratory Standards Institute (CLSI) standard antibiogram model to four weighted-incidence syndromic antibiogram (WISCA) models utilizing alternate formatting rules. Ability to produce a facility-specific antibiogram with at least 30 isolates and the impact on susceptibility predictions were compared.

resultsOnly one facility could generate a CLSI standard antibiogram for the three most commonly recovered Gram-negative isolates over a one-year period. Ability to generate an antibiogram increased with each of the four WISCA models trialed (36%, 54%, 85%, 85%) with the most successful models combining all Gram-negative isolates over a two-year period. Shortening the definition of duplicate isolates from 12 to 3 months did not improve performance. Using all Gram-negative isolates, rather than the three most recovered pathogens, resulted in meaningful changes in the predicted activity of ampicillin-sulbactam, cefazolin, ceftriaxone, and trimethoprim-sulfamethoxazole in several study NHs.

conclusionsThese results suggest that WISCAs using 2-years of urinary culture data including all gram-negative isolates and excluding duplicate isolates within twelve months maximizes the number of NHs able to create a valid antibiogram.

Indexed as

Anti-Bacterial AgentsNursing HomesUrinary Tract InfectionsHumansIncidenceMicrobial Sensitivity TestsRetrospective StudiesAnti-Bacterial Agents

Identifiers

PMID41527829
PMCPMC12885049

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.