Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2024
A Novel Risk-Adjusted Metric to Compare Hospitals on Their Antibiotic Prescribing at Hospital Discharge.
СтатьяСравнительное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Livorsi Daniel J, Merchant James A, Cho Hyunkeun, Goetz Matthew Bidwell, Alexander Bruce, Beck Brice, Goto Michihiko
Аннотация
BACKGROUND: Antibiotic overuse at hospital discharge is common, but there is no metric to evaluate hospital performance at this transition of care. We built a risk-adjusted metric for comparing hospitals on their overall post-discharge antibiotic use.
METHODS: This was a retrospective study across all acute-care admissions within the Veterans Health Administration during 2018-2021. For patients discharged to home, we collected data on antibiotics and relevant covariates. We built a zero-inflated, negative, binomial mixed model with 2 random intercepts for each hospital to predict post-discharge antibiotic exposure and length of therapy (LOT). Data were split into training and testing sets to evaluate model performance using absolute error. Hospital performance was determined by the predicted random intercepts.
RESULTS: 1 804 300 patient-admissions across 129 hospitals were included. Antibiotics were prescribed to 41.5% while hospitalized and 19.5% at discharge. Median LOT among those prescribed post-discharge antibiotics was 7 (IQR, 4-10) days. The predictive model detected post-discharge antibiotic use with fidelity, including accurate identification of any exposure (area under the precision-recall curve = 0.97) and reliable prediction of post-discharge LOT (mean absolute error = 1.48). Based on this model, 39 (30.2%) hospitals prescribed antibiotics less often than expected at discharge and used shorter LOT than expected. Twenty-eight (21.7%) hospitals prescribed antibiotics more often at discharge and used longer LOT.
CONCLUSIONS: A model using electronically available data was able to predict antibiotic use prescribed at hospital discharge and showed that some hospitals were more successful in reducing antibiotic overuse at this transition of care. This metric may help hospitals identify opportunities for improved antibiotic stewardship at discharge.
METHODS: This was a retrospective study across all acute-care admissions within the Veterans Health Administration during 2018-2021. For patients discharged to home, we collected data on antibiotics and relevant covariates. We built a zero-inflated, negative, binomial mixed model with 2 random intercepts for each hospital to predict post-discharge antibiotic exposure and length of therapy (LOT). Data were split into training and testing sets to evaluate model performance using absolute error. Hospital performance was determined by the predicted random intercepts.
RESULTS: 1 804 300 patient-admissions across 129 hospitals were included. Antibiotics were prescribed to 41.5% while hospitalized and 19.5% at discharge. Median LOT among those prescribed post-discharge antibiotics was 7 (IQR, 4-10) days. The predictive model detected post-discharge antibiotic use with fidelity, including accurate identification of any exposure (area under the precision-recall curve = 0.97) and reliable prediction of post-discharge LOT (mean absolute error = 1.48). Based on this model, 39 (30.2%) hospitals prescribed antibiotics less often than expected at discharge and used shorter LOT than expected. Twenty-eight (21.7%) hospitals prescribed antibiotics more often at discharge and used longer LOT.
CONCLUSIONS: A model using electronically available data was able to predict antibiotic use prescribed at hospital discharge and showed that some hospitals were more successful in reducing antibiotic overuse at this transition of care. This metric may help hospitals identify opportunities for improved antibiotic stewardship at discharge.