Retrospective analysis of clinical data associated with patients enrolled in a molecular diagnostic feasibility study highlights the potential utility for rapid detection of bloodstream infection

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Measurement of pathogen DNA polymerase activity by enzymatic template generation and amplification (ETGA) has shown promise in detecting pathogens in bloodstream infection (BSI). We perform an in-depth analysis of patients with clinical BSI enrolled in ETGA feasibility experiments.


In addition to hospital blood cultures, 1 study aerobic culture bottle was drawn from patients with suspected BSI. The study bottle was split into 2 bottles and was additionally subjected to ETGA analysis. Enzymatic template generation and amplification sensitivity/specificity for BSI detection was determined against the Centers for Disease Control BSI definition. When split cultures were both positive, time course analysis was performed to determine time to detection. The records of patients with BSI were reviewed for presence of systemic inflammatory response syndrome, antibiotic timing and appropriateness, and organism identification.


Of 307 enrollees, 38 met the Centers for Disease Control BSI definition. Seventy-four percent met systemic inflammatory response syndrome criteria on admission. Antibiotic coverage was adequate in 76% of patients. Antibiotics were more often delayed in afebrile patients (odds ratio, 5).


Twenty-seven of the split study culture bottles were positive in at least 1 sample, and ETGA detected microbes within all samples (sensitivity/specificity, 70.3%/99.3%). Of these, 22 were culture positive in both split study bottles and underwent ETGA time course analysis. Enzymatic template generation and amplification detected microbes within these 3-fold faster than culture.


Patients with BSI often have diagnostic and treatment delays. Enzymatic template generation and amplification provides clinically meaningful data more rapidly than cultures. Future development should focus on real-time application of assays that detect microbes at the molecular level.

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