Chan Zuckerberg Initiative, UCSF researchers use ML to accurately diagnose sepsis


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.

Current sepsis diagnostics focus on detecting bacteria by growing them in culture, a process that is “essential for appropriate antibiotic therapy, which is critical for sepsis survival,” according to the researchers behind the new method.

But culturing these pathogens is time-consuming and doesn’t always correctly identify the bacterium that is causing the infection. Similarly for viruses, PCR tests can detect that viruses are infecting a patient but don’t always identify the particular virus that’s causing sepsis.

Rather than relying on cultures to identify pathogens in these samples, a team led by CZ Biohub scientists Norma Neff and Angela Pisco 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.

The researchers found that the mNGS method and their corresponding model worked better than expected: They were able to identify 99% of confirmed bacterial sepsis cases, 92% of confirmed viral sepsis cases, and were able to predict sepsis in 74% of clinically suspected cases that hadn’t been definitively diagnosed.

The team hopes to build upon this successful diagnostic technique by developing a model that can also predict antibiotic resistance from pathogens detected with this method.

[Image courtesy: Chan Zuckerberg Biohub]