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]