Predicting Future Health

Stanford University researchers have developed a revolutionary, non-invasive way of quickly predicting the future health of premature infants, an innovation that could better target specialized medical intervention and reduce health-care costs.

“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.”

Penn is a co-senior author of the research, published Sept. 8 in Science Translational Medicine. The other senior author is Daphne Koller, PhD, professor of computer science in the School of Engineering.

The paper's authors likened their 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.

But by taking into account gestational age and birth weight and using a stream of real-time data routinely collected in neonatal intensive care units — such as heart rate, respiratory rate and oxygen saturation — the Stanford researchers developed 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.

Koller noted that 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.

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