Algorithm That Detects Sepsis Cut Deaths by Nearly 20 Percent

Mon, 01 Aug 2022 03:45:00 GMT
Scientific American - Technology

Over two years, a machine-learning program warned thousands of health care providers about patients...

Now one algorithm has proved its mettle in real hospitals, helping doctors and nurses treat sepsis cases nearly two hours earlier on average-and cutting the condition's hospital mortality rate by 18 percent.

Roughly 1.7 million adults in the U.S. develop sepsis each year, and about 270,000 of them die, according to the Centers for Disease Control and Prevention.

In a busy hospital, prompt sepsis diagnosis can be difficult.

Under the current standard of care, Saria explains, a health care provider should take notice when a patient displays any two out of four sepsis warning signs, including fever and confusion.

"A lot of these other programs have such a high false-alert rate that providers are turning off that alert without even acknowledging it," says Karin Molander, who is an emergency medicine physician and chair of the nonprofit Sepsis Alliance and was not involved in the development of the new sepsis-detection algorithm.

Putting together all the relevant information takes time, however-time sepsis patients do not have.

In a well-connected electronic-records system, known sepsis risk factors are available but may take time to find.

The program scanned patients' electronic health records for factors that increase sepsis risk and combined this information with current vital signs and lab tests to create a score indicating which patients were likely to develop septic shock.

Mark Sendak, a physician and population health and data science lead at the Duke Institute for Health Innovation, works on a similar program developed by Duke researchers, called Sepsis Watch.

As an emergency room physician, Molander was impressed by the fact that the AI does not make sepsis decisions on behalf of health care providers.

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