Electronically Available Patient Claims Data Improve Models for Comparing Antibiotic Use Across Hospitals: Results From 576 US Facilities

Katherine E. Goodman, Lisa Pineles, Laurence S. Magder, Deverick J. Anderson, Elizabeth Dodds Ashley, Ronald E. Polk, Hude Quan, William E. Trick, Keith F. Woeltje, Surbhi Leekha, Sara E. Cosgrove, Anthony D. Harris

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

BACKGROUND: The Centers for Disease Control and Prevention (CDC) uses standardized antimicrobial administration ratios (SAARs)-that is, observed-to-predicted ratios-to compare antibiotic use across facilities. CDC models adjust for facility characteristics when predicting antibiotic use but do not include patient diagnoses and comorbidities that may also affect utilization. This study aimed to identify comorbidities causally related to appropriate antibiotic use and to compare models that include these comorbidities and other patient-level claims variables to a facility model for risk-adjusting inpatient antibiotic utilization. METHODS: The study included adults discharged from Premier Database hospitals in 2016-2017. For each admission, we extracted facility, claims, and antibiotic data. We evaluated 7 models to predict an admission's antibiotic days of therapy (DOTs): a CDC facility model, models that added patient clinical constructs in varying layers of complexity, and an external validation of a published patient-variable model. We calculated hospital-specific SAARs to quantify effects on hospital rankings. Separately, we used Delphi Consensus methodology to identify Elixhauser comorbidities associated with appropriate antibiotic use. RESULTS: The study included 11 701 326 admissions across 576 hospitals. Compared to a CDC-facility model, a model that added Delphi-selected comorbidities and a bacterial infection indicator was more accurate for all antibiotic outcomes. For total antibiotic use, it was 24% more accurate (respective mean absolute errors: 3.11 vs 2.35 DOTs), resulting in 31-33% more hospitals moving into bottom or top usage quartiles postadjustment. CONCLUSIONS: Adding electronically available patient claims data to facility models consistently improved antibiotic utilization predictions and yielded substantial movement in hospitals' utilization rankings.

Original languageEnglish
Pages (from-to)e4484-e4492
JournalClinical infectious diseases : an official publication of the Infectious Diseases Society of America
Volume73
Issue number11
DOIs
StatePublished - Dec 6 2021

Keywords

  • antibiotic stewardship
  • antimicrobial use
  • benchmarking
  • risk adjustment

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