Benchmarking Inpatient Antimicrobial Use: Is Risk-Adjustment Possible?

Benchmarking Inpatient Antimicrobial Use:  Is Risk-Adjustment Possible?

 

Kalvin C Yu, MD; Elizabeth Moisan, MS; Gunter Rieg, MD

https://doi.org/10.7812/TPP/17-140-06

Introduction: Antibiotic overuse has become a worldwide concern. Clostridium difficile and drug-resistant bacteria are of increasing clinical importance. California state law requires antimicrobials stewardship programs (ASPs) for all acute care facilities. We describe a risk-adjusted antibiotic exposure ratio that may help facilitate benchmarking of antimicrobial use.
Methods: The study included members admitted to 35 Kaiser Permanente Southern and Northern California hospitals in 24 consecutive months. Potential predictive variables were assessed using linear regression models. Ratios of risk-adjusted antibiotic consumption were calculated comparing: A) a gold standard consisting of all available antibiotic use risk factors and B) a simplified “Encounter Ratio” using only the most significant factors.
Results: Diagnosis-related groups, infection present on admission, patient class, and unit type were the major predictors of antibiotic use. Aside from history of methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant enterococci for anti-MRSA drugs, additional clinical and comorbidity information did not improve the model. Analyses demonstrated high fit between the Encounter Ratio and the gold standard.
Discussion: Metrics of antibiotic use differ when using raw consumption data compared with a risk-adjusted model. The Encounter Ratio model we developed helps analyze consumption data in a risk-adjusted fashion that takes into account the types of patients seen at each facility. This type of metric may therefore better inform ASP operations.
Conclusion: Risk-adjustment of antibiotic use using observed to expected ratios is possible. Diagnosis-related groups, infection present on admission, unit type, and patient class are major determinants of our Encounter Model and are information data sets that can potentially be applied to other hospitals in the nation. 

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