Machine-learning-based risk assessment tool to rule out empirical use of ESBL-targeted therapy in endemic areas.

Journal: The Journal of hospital infection

PMID: 38679390

Abstract

BACKGROUND: Antimicrobial stewardship focuses on identifying patients who require extended-spectrum beta-lactamase (ESBL)-targeted therapy. 'Rule-in' tools have been researched extensively in areas of low endemicity; however, such tools are inadequate for areas with high prevalence of ESBL-producing pathogens, as almost all patients will be selected.

Authors

H D Ravkin

Goldman Medical School, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel.
R M Ravkin

Department of Medical Applications, Clalit Health Services, Tel-Aviv, Israel.
E Rubin

Shraga Segal Department of Microbiology, Immunology and Genetics, Ben-Gurion University of the Negev, Beer Sheva, Israel.
L Nesher

Goldman Medical School, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel; Infectious Diseases Institute, Soroka University Medical Centre, Beer-Sheva, Israel. Electronic address: nesherke@bgu.ac.il.

Keywords

Adult Aged Aged, 80 and over Anti-Bacterial Agents Antimicrobial Stewardship beta-Lactam Resistance beta-Lactamases Escherichia coli Escherichia coli Infections Female Humans Klebsiella Infections Klebsiella pneumoniae Machine Learning Male Middle Aged Risk Assessment

External Resources

View on PubMed Access via DOI PubMed (38679390)

Machine-learning-based risk assessment tool to rule out empirical use of ESBL-targeted therapy in endemic areas.

Abstract

Authors

Keywords

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