Tapping mothers’ and babies’ medical records to better predict prematurity complications
By sifting through electronic health records of moms and babies using a machine-learning algorithm, scientists can predict how at-risk newborns will fare in their first two months of life. The new method allows physicians to classify, at or before birth, which infants are likely to develop complications of prematurity.
A study describing the method, developed at the Stanford School of Medicine, was published online Feb. 15 in Science Translational Medicine.
This is a new way of thinking about preterm birth, placing the focus on individual health factors of the newborns rather than looking only at how early they are born.”
Nima Aghaeepour, PhD, senior study author, associate professor of anesthesiology, perioperative and pain medicine and of pediatrics
The study’s lead authors are postdoctoral scholar Davide De Francesco, PhD, and Jonathan Reiss, MD, an instructor in pediatrics.
Traditionally defined as birth occurring at least three weeks early, premature birth is linked to complications in babies’ lungs, brains, vision, hearing and digestive system. Although earlier births generally carry higher risks, the timing of birth predicts only approximately how a specific infant will fare. Some infants who are born quite early develop no complications, while others born at the same stage of pregnancy become very ill or die.
“Preterm birth is the single largest cause of death in children under age 5 worldwide, and we haven’t had good solutions,” Aghaeepour said. “By focusing our research on predicting the health of these babies, we can optimize their care.”
Many complications of prematurity take days or weeks after birth to emerge, causing substantial damage to newborns’ health in the meantime. Knowing which infants are at risk could enable preventive measures.
“We look mainly at the baby to make treatment decisions in neonatology, but we are finding that we can get valuable information from the maternal health record, really homing in on how individual babies’ trajectories have been shaped by exposure to their specific maternal environment,” said study coauthor David Stevenson, MD, a neonatologist at Lucile Packard Children’s Hospital Stanford, professor of pediatrics and director of the March of Dimes Prematurity Research Center at the Stanford School of Medicine.
“This is a move toward precision medicine for babies,” he added.
Reading medical records like books
The researchers linked electronic medical records for mothers at Stanford Health Care and for their babies at Stanford Medicine Children’s Health, covering 32,354 live births that occurred between 2014 and 2020. The mothers’ medical records included information from the pregnancy and, for those who had been patients at Stanford Medicine prior to pregnancy, health data from before they became pregnant. The infants’ records started with information recorded at birth, including weight; blood tests; and Apgar score, which is assessed in the delivery room one and five minutes after birth. The Apgar score incorporates factors such as the infant’s pulse, breathing and muscle tone.
Using a machine learning algorithm called a long short-term memory neural network, the researchers built a mathematical model from the medical records and tested whether it could predict 24 possible health outcomes for infants up to two months after birth.
De Francesco, D., et al. (2023) Data-driven longitudinal characterization of neonatal health and morbidity. Science Translational Medicine. doi.org/10.1126/scitranslmed.adc9854.
