The NIH-funded study aims to establish disease subtypes that would help predict longer-term cardiorespiratory outcomes early in the course of neonatal care.
RT’s Three Key Takeaways:
- AI and Genetic Data for Precision: The study will leverage artificial intelligence and genetic data to classify subtypes of bronchopulmonary dysplasia, aiming to offer a more precise approach to diagnosing and managing the disease in premature infants.
- Early Intervention Goals: By identifying genetic pathways linked to specific outcomes, researchers aim to develop predictive tools that could lead to earlier, targeted interventions in neonatal care.
- Improved Long-Term Outcomes: Understanding the genetic mechanisms behind bronchopulmonary dysplasia may help prevent or mitigate long-term respiratory and cardiac complications, according to investigators.
Infants born more than three months prematurely are at high risk for lung disease—called bronchopulmonary dysplasia—that often persists through childhood, manifesting as wheezing or abnormalities in lung or heart function.
Currently this imprecise diagnosis is based solely on clinical features, and it is impossible to predict how the disease will evolve in the long term, which limits opportunities for early intervention.
To improve diagnosis and treatment, a new study at Ann & Robert H. Lurie Children’s Hospital of Chicago, funded by $7.6 million from the National Institutes of Health (NIH), will use artificial intelligence/machine learning to identify disease subtypes that are based on genetic data and associated outcomes.
“Our study will investigate genetic influence on long-term cardiac and respiratory outcomes of premature infants in order to identify genetic pathways that correspond to high likelihood for specific outcomes, such as asthma or cardiac dysfunction,” says principal investigator Aaron Hamvas, MD, division head of neonatology at Lurie Children’s and professor of pediatrics at Northwestern University Feinberg School of Medicine, in a release. “We hope that our results will lead to genetic testing in the neonatal intensive care unit and allow earlier interventions according to the disease subtype. This advance may transform the trajectory of lung disease in premature infants.”
Assessing Premature Infants Over Four Years
The four-year study will assemble almost 2,000 patients who were premature infants and enrolled in studies while in neonatal care, who have genetic data available, and who are now enrolled in studies at school age. Researchers will use artificial intelligence/machine learning to analyze the genetic and clinical data from these patients, in order to establish disease subtypes that would help predict longer-term cardiorespiratory outcomes early in the course of neonatal care.
“Our results also will allow more insight into the genetic mechanisms that determine outcomes, so that we might develop more effective means to treat or prevent these lung diseases early on,” says Hamvas, who holds the Raymond & Hazel Speck Berry Board Designated Professorship in Neonatology at Lurie Children’s, in a release.
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