Emerging Technologies for Molecular Diagnosis of Sepsis.

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PubMed ID: 29490932

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Sinha M, Jupe J, Mack H, Coleman TP, Lawrence SM, Fraley SI

Clin Microbiol Rev. Apr 2018

COMMENT: Sepsis is a serious and life-threatening clinical condition that generally results from a primary bacterial infection or, less frequently, from a fungal and/or viral infection. 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.

This review outlines the limitations of routine blood culture testing and discuss how emerging sepsis technologies are converging on the characteristics of the ideal sepsis diagnostic test. They also include seven molecular technologies that have been validated on clinical blood specimens or mock samples using human blood. In addition, they discuss advances in machine learning technologies that use electronic medical record data to provide contextual evaluation support for clinical decision-making.

According to the authors, the ideal sepsis diagnostic test should include the following characteristics:

1. rapid detection (the pathogen needs to be identified in less than 3 h)

2. broad-based detection, including bacteria, viruses, and fungi;

3. minimal invasiveness, utilizing clinical samples with low specimen volumes (1 ml blood for pediatric patients, including neonates, and 5 to 10 ml blood for adults);

4. high sensitivity and specificity for the immediate initiation of targeted antibiotic use in the presence of signs and symptoms of systemic inflammation (the diagnostic tests should not compromise on sensitivity with low pathogen levels in the specimen);

5. polymicrobial detection of pathogens in the presence of contaminants across a wide range of pathogen loads (1 to 100,000 CFU/ml blood)

6. detection of drug resistance;

7. integration into the clinical workflow (the process should be easy to use and require minimal technical expertise to process samples and interpret test results; for the greatest impact, the technology must be usable in noncentralized low-resource settings);

8. the ability to detect unknown and emerging pathogens (detection capabilities must be able to easily expand without compromising the robustness of detection and the required specimen volume); and

9. the ability to distinguish the inflammatory response as being either host or pathogen driven. 


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Javier Velasco