Machine learning enables an 'almost perfect' diagnosis of pathogens causing sepsis

Good news! Compelling cutting edge research! 

Though, I am surprised they used the old fashioned, kind of outdated Support Vector Machine for the study. I suppose, it is the small sample size and simplicity for why they used SVM.

"Sepsis, the overreaction of the immune system in response to an infection, causes an estimated 20% of deaths globally and as many as 20 to 50% of U.S. hospital deaths each year. Despite its prevalence and severity, however, the condition is difficult to diagnose and treat effectively. ...
Now, researchers at the Chan Zuckerberg Biohub (CZ Biohub), the Chan Zuckerberg Initiative (CZI), and UC San Francisco (UCSF) have developed a new diagnostic method that applies machine learning to advanced genomics data from both microbe and host—to identify and predict sepsis cases. As reported on October 20, 2022 in Nature Microbiology, the approach is surprisingly accurate, and has the potential to far exceed current diagnostic capabilities. ...

The researchers analyzed whole blood and plasma samples from more than 350 critically ill patients who had been admitted to UCSF Medical Center or the Zuckerberg San Francisco General Hospital between 2010 and 2018.

But rather than relying on cultures to identify pathogens in these samples, a team ... instead used metagenomic next-generation sequencing (mNGS). This method identifies all the nucleic acids or genetic data present in a sample, then compares those data to reference genomes to identify the microbial organisms present. This technique allows scientists to identify genetic material from entirely different kingdoms of organisms—whether bacteria, viruses, or fungi—that are present in the same sample. ...
researchers also performed transcriptional profiling—which quantifies gene expression—to capture the patient's response to infection.

Next they applied machine learning to the mNGS and transcriptional data to distinguish between sepsis and other critical illnesses and thus confirm the diagnosis. ..."

From the abstract:

"We carried out integrated host and pathogen metagenomic RNA and DNA next generation sequencing (mNGS) of whole blood (n = 221) and plasma (n = 138) from critically ill patients following hospital admission. We assigned patients into sepsis groups on the basis of clinical and microbiological criteria. From whole-blood gene expression data, we distinguished patients with sepsis from patients with non-infectious systemic inflammatory conditions using a trained bagged support vector machine (bSVM) classifier (area under the receiver operating characteristic curve (AUC) = 0.81 in the training set; AUC = 0.82 in a held-out validation set). Plasma RNA also yielded a transcriptional signature of sepsis with several genes previously reported as sepsis biomarkers, and a bSVM sepsis diagnostic classifier (AUC = 0.97 training set; AUC = 0.77 validation set). Pathogen detection performance of plasma mNGS varied on the basis of pathogen and site of infection. To improve detection of virus, we developed a secondary transcriptomic classifier (AUC = 0.94 training set; AUC = 0.96 validation set). We combined host and microbial features to develop an integrated sepsis diagnostic model that identified 99% of microbiologically confirmed sepsis cases, and predicted sepsis in 74% of suspected and 89% of indeterminate sepsis cases. In summary, we suggest that integrating host transcriptional profiling and broad-range metagenomic pathogen detection from nucleic acid is a promising tool for sepsis diagnosis."

Machine learning enables an 'almost perfect' diagnosis of an elusive global killer

Integrated host-microbe plasma metagenomics for sepsis diagnosis in a prospective cohort of critically ill adults (open access)

Credits: Last Week in AI

Fig. 1: Study overview