All Sepsis Is Not the Same

This is a fairly dense informatics evaluation of sepsis, but it boils down to a general hypothesis with some face validity: all sepsis is not the same! This is abundantly obvious from the various clinical manifestations of response to infection, with a spectrum ranging from Group A Streptococcal pharyngitis to gram-negative bacteremia and distributive shock.

This analysis uses genetic expression sampling from whole blood to perform unsupervised machine learning analyses and clustering, and they identify three subtypes the authors term “Inflammopathic, Adaptive, and Coagulopathic”. Whether these are terribly illustrative of the underlying pathology is unclear, but, if you want to be in one of these clusters, you want to be in “Adaptive” with its 8.1% mortality – compared to 29.8% in Inflammopathic and 25.4% in Coagulopathic.

Validity of this specific analysis aside, it’s an interesting example of what may ultimately be a useful approach to treating sepsis – targeting the specific underlying genetic expressions associated with dysregulated immune response or underlying end-organ dysfunction. The best thing about this paper, however, are the acronyms reported for some of the statistical methods: “COmbined Mapping of Multiple clUsteriNg ALgorithms” or COMMUNUAL, and “COmbat CO-Normalization Using conTrols” or COCONUT.

“Unsupervised Analysis of Transcriptomics in Bacterial Sepsis Across Multiple Datasets Reveals Three Robust Clusters”
https://www.ncbi.nlm.nih.gov/pubmed/29537985