Emerging Technologies for Molecular Diagnosis of Sepsis

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.

Keywords: DNA sequencing; biomedical engineering; diagnostic; infectious disease; microbiology techniques.

FIG 1

Workflow for the analysis of a single whole-blood specimen for pathogen identification. Even though U-dHRM shows promise as the fastest technology, it could benefit from parallelizing for multiple loads in the future.

FIG 2

Sensitivity plotted against specificity of test results compared against the gold standard of blood culture for Iridica, SeptiFast, and SepsiTest. The marker/symbol area is proportional to the number of paired blood tests in the study. Darker shades of color signify larger blood volumes used for the test. (A) For Iridica, we included data from 6 publications found by a PubMed literature search. (B) For SeptiFast, we included data from 2 meta-analyses (summary statistics from analyses are shown in black, along with the confidence intervals) in addition to data from 8 new relevant studies. (C) For SepsiTest, we included data from 5 publications found by a PubMed literature search.

FIG 3

Digitization and melting of genomes after amplification with PCR technology. A melt curve corresponding to the individual genome is generated for identification and absolute load quantification of the pathogen and contaminants.

FIG 4

Timeline of sepsis technologies and where they fall compared to the gold standard of blood culture and the 1- to 3-h critical time for affecting clinical decision-making. SeptiCyte and U-dHRM may be further optimized to provide results in a shorter time frame.