Improved diagnostic prediction of the pathogenicity of bloodstream isolates of Staphylococcus epidermidis.

ABSTRACT: With an estimated 440,000 active cases occurring each year, medical device associated infections pose a significant burden on the US healthcare system, costing about $9.8 billion in 2013. Staphylococcus epidermidis is the most common cause of these device-associated infections, which typically involve isolates that are multi-drug resistant and possess multiple virulence factors. S. epidermidis is also frequently a benign contaminant of otherwise sterile blood cultures. Therefore, tests that distinguish pathogenic from non-pathogenic isolates would improve the accuracy of diagnosis and prevent overuse/misuse of antibiotics. Attempts to use multi-locus sequence typing (MLST) with machine learning for this purpose had poor accuracy (~73%). In this study we sought to improve the diagnostic acc

SUBMITTER: VanAken SM 

PROVIDER: S-EPMC7997010 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature