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Published on: August 30, 2018
Assessment of Antimicrobial Transition Errors from Hospitals to Skilled Nursing Facilities
Amy Y Kang1,2,3, Guarina A Garcia Delgado3, Ashley Nguyen3
1Department of Pharmacy Practice, Chapman University School of Pharmacy, Irvine, California, USA.
Background:
More than 1 million Americans reside in skilled nursing facilities (SNFs). Antimicrobial transition errors among patients transferred from hospital to SNFs pose safety risks and may lead to poor outcomes, but data on such errors are limited.
Methods:
We conducted a retrospective cohort study of infectious diseases clinics from 1 June 2020 through 30 November 2023 at the Los Angeles County Department of Health Services, a large safety-net health system. We performed logistic regression analyses to identify factors associated with antimicrobial transition errors and poor infection outcomes.
Results:
We screened records of 6865 clinic patients, among whom 112 were SNF residents who were receiving post-hospital discharge antimicrobials. Mean age was 62 years, 37% were female, and 57% were Hispanic/Latino. Transition errors occurred in 32 (29%) patients. Common medications associated with errors were penicillin class (39%), tetracycline class (38%), and daptomycin (36%). In our multivariable model, age, Charlson Comorbidity Index score, number of medications, Centers for Medicare & Medicaid Services SNF rating, and therapy duration were not significantly associated with transition errors. Older age was the only independent predictor of poor infection outcome (P & .02). There was a nonsignificant trend between antimicrobial transition errors and poor infection outcome (odds ratio, 1.63 [95% confidence interval, .58-4.81]).
Conclusions:
Nearly one-third of patients transitioning from hospitals to SNFs on antimicrobials experienced ≥1 antimicrobial transition error. We did not identify risk factors for antimicrobial transition errors. The trend toward an association between antibiotic transition errors and poor infection outcomes warrants further investigation in more robust data sets.
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