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Genetic Pattern Allows Early, Accurate Sepsis Diagnosis
Investigators at the Stanford University School of Medicine have identified a pattern of gene activity that could help scientists create a blood test to quickly and accurately detect whether patients are experiencing a deadly immune-system panic attack.
Sepsis is a whole-body inflammation syndrome set off when the immune system overreacts to the presence of infectious pathogens. It is the leading cause of U.S. hospital deaths, accounting for nearly half, and it is tied to the early deaths of at least 750,000 Americans a year. Its estimated annual cost to the health care system exceeds $24 billion.
Most sepsis cases are caused by bacterial rather than viral infections and are best treated with antibiotics. But antibiotics are unhelpful — and can be counterproductive — when a patient has an outwardly similar but infection-free syndrome called sterile inflammation, an intense, systemic inflammatory response to traumatic injuries, surgery, blood clots, or other noninfectious causes.
“It’s critical for clinicians to diagnose sepsis accurately and quickly because the risk of death from this condition increases with every passing hour it goes untreated,” said Purvesh Khatri, PhD, Assistant Professor of Biomedical Informatics Research at Stanford.
In practice, distinguishing sepsis from sterile inflammation is a toss-up. Right now, the only diagnostics that can help do this are too slow, too inaccurate, or both, Khatri said. As a result, hospital clinicians are pressured to treat anybody showing signs of systemic inflammation with antibiotics. That can encourage bacterial drug resistance and, by killing harmless bacteria in the gut, lead to colonization by pathogenic bacteria, such as Clostridium difficile.
The inability to easily distinguish sepsis from sterile inflammation makes it tough for pharmaceutical companies to conduct clinical trials of drugs aimed at treating sepsis; patients may be mistakenly assumed to have sepsis when they actually have sterile inflammation, and vice versa, Khatri said.
“We think we’ve got the makings of a diagnostic blood test that will allow clinicians to distinguish between these two types of inflammation,” he said.
Khatri is the senior author of a study published May 13 in Science Translational Medicine in which a meta-analysis of publicly available data sets allowed him and his associates to identify a gene-activation pattern that distinguishes septic from sterile systemic inflammation.
Numerous studies have been conducted to find differences in the activation levels of immune-response genes between infection-related inflammation and sterile inflammation, but these studies have yielded conflicting results. One reason is that both infectious and noninfectious tissue trauma activate many of the same immune-system components and pathways.
More than 80% of a person’s roughly 25,000 genes change their activity levels substantially, and mostly in the same direction, in response to massive inflammation, whether due to sepsis or sterile causes. That overlap obscures any easily detectable changes attributable solely to infection.
Further confounding attempts to identify patterns of increases or decreases in gene activity is the fact that some patients are already experiencing sepsis when they’re admitted to the hospital, while others become infected during their hospital stay. Therefore, two different sepsis patients admitted at the same time may be at very different stages of a complex inflammatory process.
The solution may consist of a signature formed by consistent changes in the activity levels of 11 genes amid the more than 20,000 genes whose activity levels fluctuate markedly over the course of systemic inflammation and recovery.
The Stanford investigators zeroed in on these 11 genes after a painstaking, multistep analysis of publicly available data sets containing results of studies that had assessed activity levels for the entire human genome in sepsis cases, as well as in cases of sterile inflammation. They looked at more than 2,900 blood samples from nearly 1,600 patients in 27 different data sets containing medical information on diverse patient groups in addition to healthy control subjects.
Then the researchers confirmed the 11-gene signature in an additional 18 cohorts comprising more than 1,800 patient samples. “We were able to identify a slight bump in activity of these 11 genes in patients two to five days prior to their clinical diagnosis,” said Khatri. That could mean getting an earlier diagnosis than can be achieved with current approaches.
Source: Stanford University School of Medicine; May 13, 2015.