A new algorithm more accurately identifies patients at risk for septic shock, an infection-related condition that kills more Americans every year than AIDS, breast cancer, and prostate cancer combined.
In a new study, the complex formula correctly predicted septic shock in 85 percent of cases, without increasing the false positive rate from screening methods that are common now.
“But the critical advance our study makes is to detect these patients early enough that clinicians have time to intervene,” says Suchi Saria, assistant professor of computer science at Johns Hopkins University. For a patient with sepsis, she says, hours can make the difference between life and death.
‘Sepsis is invisible’
The new method was able to predict septic shock before any organ dysfunction occurred more than two-thirds of the time, researchers report in the journal Science Translational Medicine. That’s a 60 percent improvement over existing screening protocols.
The research promises significant progress in treating a condition that is estimated to affect about a million Americans and kill about 200,000 every year—many of them in hospitals and nursing homes, says coauthor Peter J. Pronovost, a critical care physician who directs the Armstrong Institute for Patient Safety and Quality.
“We know a lot of those deaths would likely be preventable” if sepsis were diagnosed well before it develops into septic shock and organ failure, he says.
“Right now, much of sepsis is invisible until someone is on death’s door.” Every hour before sepsis patients receive antibiotics, he says, “correlates strongly with risk of death.”
Sepsis is caused by a powerful immune system reaction to infection that, untreated, causes inflammation throughout the body; the inflammation can trigger blood clots and leaking blood vessels. That hinders blood flow, which in the worst cases causes organ failure. The condition is a significant problem among vulnerable populations in hospitals and nursing homes. It can be triggered by invasive procedures, including catheterization.
The study drew on electronic health records of 16,234 patients admitted to intensive care units—including medical, surgical, and cardiac units—at Boston’s Beth Israel Deaconess Medical Center. Researchers created an algorithm that combines results on 27 variables into a Targeted Real-time Early Warning Score—TREWScore—measuring the risk of septic shock.
“One strength of this approach,” notes Katharine Henry, a graduate student in Saria’s lab and first author of the study, “is that all of our inputs are routinely collected. You don’t need specialized new measurements.”
Larger data pool
Compared with previous methods for predicting septic shock, TREWScore is based on a larger data pool, takes account of more health indicators, and compensates for several elements that could have confounded the results.
David Hager, a coauthor and director of the Medical Progressive Care Unit, says TREWScore can be programmed into hospital electronic health records systems to alert doctors and nurses about a patient at risk for septic shock.
The study is part of a broad effort at Johns Hopkins to help clinicians and patients by providing continuous, insightful monitoring, Saria says.
“Our methods are reaching a point where they can be a real aid to clinicians, especially in noticing subtle hints, buried deep in a chart, that a problem is developing.”
The National Science Foundation, Google Research, the Gordon and Betty Moore Foundation, and the Johns Hopkins Whiting School of Engineering funded the work.
Source: Johns Hopkins University