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.

The disease can cause decreased blood flow to vital organs, inflammation throughout the body, and abnormal blood clotting. Therefore, if sepsis isn’t recognized and treated quickly, it can lead to shock, organ failure, and death. But it can be difficult to identify which pathogen is causing sepsis, or whether an infection is in the bloodstream or elsewhere in the body. And in many patients with symptoms that resemble sepsis, it can be challenging to determine whether they truly have an infection at all.

AN ALMOST PERFECT SEPSIS DIAGNOSIS
Diagnosing sepsis is challenging. However, a new method involving metagenomic next-generation sequencing (mNGS) and machine learning was able to identify 99% of confirmed bacterial sepsis cases, 92% of confirmed viral sepsis cases, and could predict sepsis in 74% of clinically suspected cases.

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.

“Sepsis is one of the top 10 public health issues facing humanity,” said senior author Chaz Langelier, MD, PhD, an associate professor of medicine in UCSF’s Division of Infectious Diseases and a CZ Biohub Investigator. “One of the key challenges with sepsis is diagnosis. Existing diagnostic tests are not able to capture the dual-sided nature of the disease – the infection itself and the host’s immune response to the infection.”

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.

“This results in clinicians being unable to identify the cause of sepsis in an estimated 30 to 50% of cases,” Langelier said. “This also leads to a mismatch in terms of the antibiotic treatment and the pathogen causing the problem.”

In the absence of a definitive diagnosis, doctors often prescribe a cocktail of antibiotics in an effort to stop the infection, but the overuse of antibiotics has led to increased antibiotic resistance worldwide. “As physicians, we never want to miss a case of infection,” said Carolyn Calfee, MD, MAS, a professor of medicine and anesthesia at UCSF and co-senior author of the new study. “But if we had a test that could help us accurately determine who doesn’t have an infection, then that could help us limit antibiotic use in those cases, which would be really good for all of us.”

Source: https://www.ucsf.edu/news/2022/10/424116/machine-learning-enables-diagnosis-sepsis-elusive-global-killer