Using Encounter-Level Data for Risk-Adjustment of Antimicrobial Use Comparisons: Feasibility and Variable Selection.

Rebekah W Moehring¹, Michael E Yarrington¹, Elizabeth Dodds Ashley¹, Rachel M Addison¹, Whitney R Buckel², Sara E Cosgrove³, Danielle Doughman⁴, Eili Klein⁵, Carlos A Q Santos⁶, Emily S Spivak⁷, William Trick⁸, Michael J Smith⁹, David J Weber⁴, Congwen Zhao¹⁰, Deverick J Anderson¹, Benjamin A Goldstein¹⁰

¹Department of Medicine, Division of Infectious Diseases, Duke Center for Antimicrobial Stewardship and Infection Prevention, Duke University, Durham, North Carolina, USA.

²Department of Pharmacy, Intermountain Health, Salt Lake City, Utah, USA.

³Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.

⁴Department of Medicine, University of North Carolina Hospitals, Chapel Hill, North Carolina, USA.

⁵Department of Emergency Medicine, Johns Hopkins University, Baltimore, Maryland, USA.

⁶Department of Internal Medicine, Rush University, Chicago, Illinois, USA.

⁷Department of Medicine, Division of Infectious Diseases, University of Utah, Salt Lake City, Utah, USA.

⁸Department of Internal Medicine, Cook County Health, Chicago, Illinois, USA.

⁹Department of Pediatrics, Division of Infectious Diseases, Duke Center for Antimicrobial Stewardship and Infection Prevention, Duke University, Durham, North Carolina, USA.

¹⁰Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA.

PubMed DOI

Abstract

BACKGROUND: External comparisons of hospital antimicrobial use (AU), risk-adjusted using encounter characteristics, may better inform antimicrobial stewardship program strategy. Barriers to encounter-level modeling include feasibility of data collection and defining optimal methods for selecting input variables for risk-adjustment purposes. METHODS: We measured achievements in sharing validated,…

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