Emerging Molecular Technologies for Sepsis Diagnosis: Toward the 1-to-3-Hour Ideal Test
This review outlines the limitations of conventional blood culture for sepsis and evaluates seven emerging molecular diagnostic technologies validated on clinical blood specimens. The ideal sepsis test must capture relevant organisms and resistance markers within 1 to 3 hours using small sample volumes. The authors also discuss machine learning approaches using electronic medical records to support clinical decision-making alongside rapid diagnostics.
The original study
Emerging Technologies for Molecular Diagnosis of Sepsis.
- Authors
- Sinha M, Jupe J, Mack H, Coleman TP, Lawrence SM, Fraley SI
- Journal
- Clinical microbiology reviews
- Type
- Journal Article, Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, Non-P.H.S., Review
- PMID
- 29490932
Original abstract
Rapid and accurate profiling of infection-causing pathogens remains a significant challenge in modern health care. Despite advances in molecular diagnostic techniques, blood culture analysis remains the gold standard for diagnosing sepsis. However, this method is too slow and cumbersome to significantly influence the initial management of patients. The swift initiation of precise and targeted antibiotic therapies depends on the ability of a sepsis diagnostic test to capture clinically relevant organisms along with antimicrobial resistance within 1 to 3 h. The administration of appropriate, narrow-spectrum antibiotics demands that such a test be extremely sensitive with a high negative predictive value. In addition, it should utilize small sample volumes and detect polymicrobial infections and contaminants. All of this must be accomplished with a platform that is easily integrated into the clinical workflow. In this review, we outline the limitations of routine blood culture testing and discuss how emerging sepsis technologies are converging on the characteristics of the ideal sepsis diagnostic test. We include seven molecular technologies that have been validated on clinical blood specimens or mock samples using human blood. In addition, we discuss advances in machine learning technologies that use electronic medical record data to provide contextual evaluation support for clinical decision-making.