1873. Next Steps in Predicting Anti-MRSA Antibiotic Prescribing. (26th November 2018)

• Record Type: Journal Article
• Authors: Holmer, Haley K; Pakyz, Amy; Tallman, Gregory B; Elman, Miriam R; Hohmann, Samuel; Fu, Rochelle; Kuper, Kristi; McGregor, Jessina C
• Is Part Of: Open forum infectious diseases. Volume 5(2018)Supplement 1
• Page Start: S535
• Page End: S536
• Publication Date: 2018-11-26
• Subjects: Communicable diseases -- Periodicals; Medical microbiology -- Periodicals; Infection -- Periodicals; 616.9
• DOI: 10.1093/ofid/ofy210.1529
• Languages: English
• ISSNs: 2328-8957

Abstract

Abstract: Background: Antibiotic use metrics are utilized by antimicrobial stewardship programs to benchmark performance against peer institutions and inform stewardship efforts. Benchmarking requires risk adjustment for patient- and facility-level factors so that remaining differences are attributable only to prescribing practices. Antibiotics for the treatment of methicillin-resistant Staphylococcus aureus (MRSA) are one of the most frequently used drug classes. Our objective was to identify predictors of anti-MRSA antibiotic use in a nationwide network of hospitals. Methods: We used data from inpatient encounters at facilities participating in the Vizient data repository between January 1, 2016 and December 31, 2016. The outcome, anti-MRSA antibiotic use, was calculated as days of therapy per patient-days present for each encounter. We constructed a multivariable negative binomial regression model and assessed the following predictors for inclusion: age, sex, race, ethnicity, diagnosis related groups (DRGs), ICU days, admit month, facility bed size, facility teaching status, and region. A clinical framework was used to categorize DRGs based on risk of anti-MRSA antibiotic use. A backwards stepwise approach was used to identify the final model. We evaluated predictor effect size and significance, and assessed model fit using a deviance-based pseudo- R 2 . Results: One hundred forty-five facilities representing 3, 608, 711 encounters met inclusion criteria. All predictors considered in our model were significant. Predictors with the greatest magnitude of association included DRG categories and patient age. The DRG categories with the strongest associations were DRGs for infections likely due to Staphylococcus aureus (RR = 1.66, P < 0.0001) or for diagnoses likely to receive long-term MRSA coverage (RR = 1.49, P < 0.0001). The age group with the strongest association was age 2–10 years (RR = 1.64; P < 0.001). The deviance-based pseudo- R 2 of the final model was 0.19, indicating good model fit. Conclusion: DRGs and patient-level characteristics can be utilized to account for variability in anti-MRSA antibiotic use beyond what is explained through facility-level characteristics. Incorporation of the significant predictors identified in this study may aid in more meaningful interhospital comparisons of anti-MRSA antibiotic use in both adults and pediatrics. Disclosures: J. C. McGregor, Merck: Grant Investigator, Research grant.