Over two years, a machine-learning program warned thousands of health care providers about patients at high risk of sepsis, allowing them to begin treatments nearly two hours sooner
Hospital patients are at risk of a number of life-threatening complications, especially sepsis—a condition that can kill within hours and contributes to one out of three in-hospital deaths in the U.S. Overworked doctors and nurses often have little time to spend with each patient, and this problem can go unnoticed until it is too late. Academics and electronic-health-record companies have developed automated systems that send reminders to check patients for sepsis, but the sheer number of alerts can cause health care providers to ignore or turn off these notices. Researchers have been trying to use machine learning to fine-tune such programs and reduce the number of alerts they generate. 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.
Sepsis, which happens when the body’s response to an infection spirals out of control, can lead to organ failure, limb loss and death. 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. Although most cases originate outside the hospital, the condition is a major cause of patient mortality in this setting. Catching the problem as quickly as possible is crucial to preventing the worst outcomes. “Sepsis spirals extremely fast like in a matter of hours if you don’t get timely treatment,” says Suchi Saria, CEO and founder of Bayesian Health, a company that develops machine-learning algorithms for medical use. “I lost my nephew to sepsis. And in his case, for instance, sepsis wasn’t suspected or detected until he was already in late stages of what’s called septic shock…, where it’s much harder to recover.”
But 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. Some existing warning systems alert physicians when this happens—but many patients display at least two of the four criteria during a typical hospital stay, Saria says, adding that this can give warning programs a high false-positive rate. “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. Because of how commonly the warning signs occur, physicians must also consider factors such as a person’s age, medical history and recent lab test results. Putting together all the relevant information takes time, however—time sepsis patients do not have.
Source: This news is originally published by scientificamerican