Electronic tool accurately assesses disease risk for preterm infants

Stanford University researchers have developed PhysiScore, a revolutionary, non-invasive way of electronically scoring and assessing a baby's well-being and predicting whether future medical treatment might be needed. Pictured in the neonatal intensive care unit at Lucile Packard Children's Hospital are, (left to right), researchers Anand Rajani, Suchi Saria, Anna Penn and Daphne Koller. Photo courtesy of Lucile Packard Children's Hospital

Researchers at Stanford University have developed an electronic assessment and scoring tool called PhysiScore that can predict risks of serious health complications in premature infants with 98 percent accuracy, according to a new study.

In the study, which was published Sept. 8 in Science Translational Medicine, researchers liken PhysiScore to a more reliable, electronic version of an Apgar score. The Apgar, a simple, repeatable check done shortly after birth, has for more than half a century been the standard method of assessing a baby's physical well-being and predicting whether future medical treatment might be needed.

When researchers took into account gestational age and birth weight and used a stream of real-time data routinely collected in neonatal intensive care units – such as heart rate, respiratory rate and oxygen saturation – they were able to develop a probability scoring system for the health of prematurely born infants that outperformed not only the Apgar but three other systems that require invasive laboratory measurements.

"What the PhysiScore does is open a new frontier," said Anna Penn, MD, PhD, an assistant professor of pediatrics at the School of Medicine and a neonatologist at Lucile Packard Children's Hospital. "The national push toward electronic medical records helped us create a tool to detect patterns not readily seen by the naked eye or by conventional monitoring. We're now able to identify potential health problems before they become clinically obvious."

The researchers relied on data recorded during the first three hours after an infant's birth as part of a computer algorithm that predicted the baby's likelihood of developing serious illnesses with an accuracy of between 91 and 98 percent. By comparison, the success of Apgar score predictions for the same conditions ranged from 69 to 74 percent.

Sophisticated computational methods are critical to identifying the subtle patterns in the complex data about these young patients, as well as helping clinicians and researchers accurately discriminate between the different outcomes, said senior author Daphne Koller, PhD, professor of computer science in the School of Engineering.

"Our method is similar to fetal heart-rate monitoring, a tool that has profoundly changed management of labor," added Suchi Saria, the graduate student who led the research as part of her doctoral thesis in computer science. "Rather than observing a single physiological variable, however, we automatically integrate multiple physiological responses to improve accuracy."

"And the beauty is we don't have to stick anybody with a needle or do more expensive tests," said Penn. "Now we have the possibility of using the power of data already available in the intensive care unit to greatly improve care for premature infants."

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