A new approach to sepsis diagnosis that combines host transcriptional profiling and broad-range metagenomic pathogen detection is presented in Nature Microbiology. Early diagnosis and the identification of causative pathogens are key to successful sepsis treatment yet doing so remains difficult due to the limitations of current culture-based microbiologic diagnostics. Adding additional complexity is the need to differentiate sepsis effectively from non-infectious systemic illnesses, which can have similar presentations clinically. To overcome these challenges, a group led by scientists from the University of California, San Francisco, developed a diagnostic method that combines integrated host and pathogen metagenomic RNA and DNA sequencing of whole blood and plasma with machine learning. In a prospective cohort of critically ill adults who had been admitted to the hospital, the researchers showed that the approach identified 99 percent of microbiologically confirmed sepsis cases, and predicted sepsis in 74 percent of suspected and 89 percent of indeterminate sepsis cases. "We suggest that integrating host transcriptional profiling and broad-range metagenomic pathogen detection from nucleic acid is a promising tool for sepsis diagnosis," the authors write.
Sequencing-Based Approach Shows Promise for Sepsis Diagnosis
Oct 21, 2022