P-905. Applying Encounter-level Risk-Adjustment for Standardized Antimicrobial Administration Ratios (SAAR): Is it meaningful?

Abstract Background Whether encounter-level risk-adjustment impacts antimicrobial stewardship program (ASP) assessments and perceptions of standardized antimicrobial administration ratio (SAAR) comparisons is unknown. Methods This retrospective analysis included patient encounter-level data among 50 US hospitals participating in the CDC Epicenters collaborative, including 2022 antimicrobial use (AU), demographics, and diagnosis and procedure categories. We developed two-staged, xgboost machine learning models on 2020-2021 training sets for adult and pediatrics (aged 1-17) to produce expected days of therapy (DOT) for each NHSN antimicrobial group and each 2022 encounter. Observed and expected DOT were aggregated to create ratios (O:E), summarized as “robust” SAARs (R-SAARs) for hospital and unit. Methods and interpretation guidance were provided to ASPs in documents and recorded videos. Two hospital-specific reports were delivered sequentially over a 3-month period in 2024. Part 1 included unadjusted hospital-level AU comparisons and 2017 unit-level SAARs using existing methods. Part 2 included newly derived R-SAARs. Hospital ASP leads were surveyed to assess their team’s response after each part. Results Data included 2.5 million DOT and 5.6 million days present among 50 hospitals in 2022. Unit-level SAARs were calculated for 272 adult and 32 pediatric units. R-SAARs were derived for 474 adult and 45 pediatric units as a larger variety of unit types could be included. Data feedback included hospital- and unit-level O:E values and percentiles (Figures 1 and 2). R-SAARs produced extreme values for antimicrobial groups and units with infrequent AU, but tighter distributions for commonly used groups as compared to SAARs (Figure 3). Forty-five of 50 ASPs voluntarily participated in data feedback; half actively used SAARs at baseline. ASPs perceived Part 2 as being more helpful in developing goals but did not score Part 2 higher in other questions of value (Table 1). Most ASPs (88%) indicated preference for R-SAARs. Conclusion When encounter-level risk adjustment was applied, O:E and percentile estimates changed ASP assessments. ASPs preferred encounter-level risk adjustment when asked about desire for future AU comparisons. Disclosures Rebekah W. Moehring, MD, MPH, FIDSA, FSHEA, UpToDate, Inc.: Author Royalties Elizabeth Dodds Ashley, PharmD, MHS, HealthtrackRx: Advisor/Consultant|UpToDate, Inc.: Author Royalties Eili Klein, PhD, Beckman-Coulter: Grant/Research Support|Diasorin: Grant/Research Support|MeMed: Grant/Research Support Carlos Santos, MD, MPHS, UpToDate, Inc.: Author Royalties Michael J. Smith, M.D., M.S.C.E, Pfizer: Grant/Research Support

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Journal
Open Forum Infectious Diseases
Published
2026-01-01
DOI
https://doi.org/10.1093/ofid/ofaf695.1111
Primary Topic
Antibiotic Use and Resistance
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article
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article

P-905. Applying Encounter-level Risk-Adjustment for Standardized Antimicrobial Administration Ratios (SAAR): Is it meaningful?

Eili Klein, Sara E. Cosgrove, Whitney R. Buckel, Anderson Dm et al.
Open Forum Infectious Diseases
Antibiotic Use and Resistance
article

P-905. Applying Encounter-level Risk-Adjustment for Standardized Antimicrobial Administration Ratios (SAAR): Is it meaningful?

Eili Klein, Sara E. Cosgrove, Whitney R. Buckel, Anderson Dm, Congwen Zhao, Emily S Spivak, Carlos A.Q. Santos, William E Trick, Ben Goldstein, Michael E Yarrington, Rebekah W Moehring, Rachel Addison, Michael J Smith, David J Weber
article en

Abstract

Abstract Background Whether encounter-level risk-adjustment impacts antimicrobial stewardship program (ASP) assessments and perceptions of standardized antimicrobial administration ratio (SAAR) comparisons is unknown. Methods This retrospective analysis included patient encounter-level data among 50 US hospitals participating in the CDC Epicenters collaborative, including 2022 antimicrobial use (AU), demographics, and diagnosis and procedure categories. We developed two-staged, xgboost machine learning models on 2020-2021 training sets for adult and pediatrics (aged 1-17) to produce expected days of therapy (DOT) for each NHSN antimicrobial group and each 2022 encounter. Observed and expected DOT were aggregated to create ratios (O:E), summarized as “robust” SAARs (R-SAARs) for hospital and unit. Methods and interpretation guidance were provided to ASPs in documents and recorded videos. Two hospital-specific reports were delivered sequentially over a 3-month period in 2024. Part 1 included unadjusted hospital-level AU comparisons and 2017 unit-level SAARs using existing methods. Part 2 included newly derived R-SAARs. Hospital ASP leads were surveyed to assess their team’s response after each part. Results Data included 2.5 million DOT and 5.6 million days present among 50 hospitals in 2022. Unit-level SAARs were calculated for 272 adult and 32 pediatric units. R-SAARs were derived for 474 adult and 45 pediatric units as a larger variety of unit types could be included. Data feedback included hospital- and unit-level O:E values and percentiles (Figures 1 and 2). R-SAARs produced extreme values for antimicrobial groups and units with infrequent AU, but tighter distributions for commonly used groups as compared to SAARs (Figure 3). Forty-five of 50 ASPs voluntarily participated in data feedback; half actively used SAARs at baseline. ASPs perceived Part 2 as being more helpful in developing goals but did not score Part 2 higher in other questions of value (Table 1). Most ASPs (88%) indicated preference for R-SAARs. Conclusion When encounter-level risk adjustment was applied, O:E and percentile estimates changed ASP assessments. ASPs preferred encounter-level risk adjustment when asked about desire for future AU comparisons. Disclosures Rebekah W. Moehring, MD, MPH, FIDSA, FSHEA, UpToDate, Inc.: Author Royalties Elizabeth Dodds Ashley, PharmD, MHS, HealthtrackRx: Advisor/Consultant|UpToDate, Inc.: Author Royalties Eili Klein, PhD, Beckman-Coulter: Grant/Research Support|Diasorin: Grant/Research Support|MeMed: Grant/Research Support Carlos Santos, MD, MPHS, UpToDate, Inc.: Author Royalties Michael J. Smith, M.D., M.S.C.E, Pfizer: Grant/Research Support

Open Forum Infectious DiseasesVol. 13(Supplement_1)
Intermountain Healthcare (US), University of North Carolina at Chapel Hill (US), Rush University Medical Center (US), Johns Hopkins University (US), Duke University (US), University of Utah (US), Duke University Health System (US), Cook County Health and Hospitals System (US)
Openalex Percentile: Top 90%
Antibiotic Use and Resistance
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