| Autor: |
Margaret Carrel, Qianyi Shi, Gosia S. Clore, Shinya Hasegawa, Matthew Smith, Eli N. Perencevich, Michihiko Goto |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Antimicrobial Resistance and Infection Control, Vol 13, Iss 1, Pp 1-6 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2047-2994 |
| DOI: |
10.1186/s13756-024-01388-3 |
| Popis: |
Abstract Background While the use of cumulative susceptibility reports, antibiograms, is recommended for improved empiric therapy and antibiotic stewardship, the predictive ability of antibiograms has not been well-studied. While enhanced antibiograms have been shown to better capture variation in susceptibility profiles by characteristics such as infection site or patient age, the potential for seasonal or spatial variation in susceptibility has not been assessed as important in predicting likelihood of susceptibility. Methods Utilizing Staphylococcus aureus isolates obtained in outpatient settings from a nationwide provider of care, the Veterans Health Administration, and a local provider of care, the University of Iowa Hospitals and Clinics, standard, seasonal and spatial antibiograms were created for five commonly used antibiotic classes: cephalosporins, clindamycin, macrolides, tetracycline, trimethoprim/sulfamethoxazole. Results A total of 338,681 S. aureus isolates obtained in VHA outpatient settings from 2010 to 2019 and 6,817 isolates obtained in UIHC outpatient settings from 2014 to 2019 were used to generate and test antibiograms. Logistic regression modeling determined the capacity of these antibiograms to predict isolate resistance to each antibiotic class. All models had low predictive capacity, with areas under the curve of |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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