About 1.7 million adults develop sepsis every year in the United States, and more than 250,000 of them die. The faster it’s caught, the better a patient’s chances for survival. A new AI system developed at The Johns Hopkins University catches symptoms hours earlier than traditional methods, a study in Nature Medicine and Nature Digital Medicine demonstrates.
“It is the first instance where AI is implemented at the bedside, used by thousands of providers, and where we’re seeing lives saved,” said Suchi Saria, founding research director of the Malone Center for Engineering in Healthcare at Johns Hopkins and lead author of the study.
Sepsis occurs when an infection triggers a chain reaction throughout the body. Inflammation can lead to blood clots and leaking blood vessels, and ultimately can cause organ damage or organ failure.
It’s easy to miss because symptoms such as fever and confusion are common in other conditions, Saria said.
“One of the most effective ways of improving outcomes is early detection and giving the right treatments in a timely way, but historically this has been a difficult challenge due to lack of systems for accurate early identification,” said Saria, who directs the Machine Learning and Healthcare Lab at Johns Hopkins.
To address the problem, Saria and other Johns Hopkins doctors and researchers developed the Targeted Real-Time Early Warning System. Combining a patient’s medical history with current symptoms and lab results, the machine-learning system shows clinicians when someone is at risk for sepsis and suggests treatment protocols, such as starting antibiotics.
During the study, more than 4,000 clinicians from five hospitals used the AI in treating 590,000 patients. In 82% of sepsis cases, the AI was accurate nearly 40% of the time. Previous attempts to use electronic tools to detect sepsis caught less than half that many cases. In the most severe sepsis cases, in which an hour delay is the difference between life and death, the AI detected it an average of nearly six hours earlier than traditional methods.
Bayesian Health, a company spun off from Johns Hopkins, led and managed the deployment across all testing sites. The team also partnered with the two largest electronic health record system providers, Epic and Cerner, to ensure that the tool can be implemented at other hospitals.
Read the original article: “AI Speeds Sepsis Detection to Prevent Hundreds of Deaths.”