Abstract
Bloodstream infections are augean diseases characterized by a high morbidity and mortality, related with the lag in administration of the first adequate anti-infectious agent. Clinical and epidemiological data guide the clinicians towards empirical anti-infectious treatments whose effectualness remains questionable especially in the present day milieu of multidrug-resistant organisms. Early microbiological evidence of the causative agent is hierophantic of antimicrobial stewardship. We evaluated three rapid methods for the direct identification of S. aureus from 720 positive blood cultures in comparison to Vitek 2 automated microbial identification system. Early distinction S. aureus from CoNS was attempted with Direct Tube Coagulase. Multiplex PCR assay was used to separate MRSA from MSSA by the presence of mecA gene. Direct antibiotic susceptibility (DST) from positive blood culture bottles was performed in an endeavour to reduce time and compared to the routine Vitek 2 compact antimicrobial susceptibility. For Direct tube coagulase at 4 hrs of incubation sensitivity was 79.6% while specificity was 100% however, on overnight incubation sensitivity increased to 97.9% with 100% specificity. The positive predictive value and negative predictive values were 100% and 96.9% respectively when compared to Vitek 2 identification of staphylococcal species. By DST Categorical agreement of 95% was seen for coagulase negative staphylococci microorganism-antibiotic combinations. Categorical agreement of 89.7% was seen for S. aureus microorganism-antibiotic combinations. Direct multiplex PCR testing did not misidentify any S. aureus isolate compared to Vitek 2. In 12 methicillin resistant CoNS isolates mecA gene was not detected by PCR. In total 13(3.5%) strains of staphylococci identified by DST as methicillin resistant were not identified by PCR analysis. Each of the tests has positive qualities and all may have a place in a Gram positive cocci algorithm for testing blood cultures depending on the laboratory setting, workload volume and staffing. However, rapid detection methods are a pressing priority.
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