Study identifies 16 predictors of infant hospitalization for RSV
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Key takeaways:
- A large study of Finnish and Swedish children and their family members identified 16 predictors of severe RSV among infants.
- Researchers developed a model that Includes a calculator to help predict hospitalizations.
Researchers identified 16 predictors of infant hospitalization for respiratory syncytial virus and developed a model that clinicians can use to predict an infant’s risk, according to findings published in The Lancet Digital Health.
RSV can cause serious illness, especially in infants and older adults, and is the leading cause of infant hospitalizations in the United States, according to the CDC. In the U.S., the CDC has recommended two prevention methods for RSV in infants: a maternal vaccine and a monoclonal antibody.
The new study was conducted by researchers using data on RSV hospitalization in Finland and Sweden over the course of 2 decades.
“RSV causes severe infections, especially in children under 1 year of age,” Santtu Heinonen, PhD, a pediatric specialist at the HUS New Children's Hospital in Helsinki, said in a press release. “In Finland, it is one of the most common causes of hospitalization of young children and a major cause of infant mortality worldwide.”
Heinonen and colleagues studied a cohort of more than 1.25 million children born in Finland between 1997 and 2020 and more than 1.5 million children born in Sweden between 2006 and 2020, and their parents and siblings.
“There are few countries where such a study can be done,” Andrea Ganna, PhD, an associate professor at the University of Helsinki, said in the release. “In our study, we applied high-quality data and methodological expertise to solve a clinically important problem. The Nordic countries have exceptionally extensive and reliable registry data.”
The authors created an AI-based model with 1,511 variables as part of the Finnish FinRegistry, which they compared against the 16-variable clinical prediction model — a calculator that asks an infant’s gestational age at birth, the next estimated RSV epidemic peak month, birth weight, mother’s age at birth, and other questions on family history and neonatal conditions.
The researchers identified known predictors of RSV hospitalization such as severe congenital heart defects (adjusted OR = 2.89; 95% CI, 2.28-3.65) but also some lesser known predictors, like esophageal malformations (aOR = 3.11; 95% CI, 1.86-5.19) and lower complexity congenital heart defects (aOR = 1.43; 95% CI, 1.25-1.63).
“It may not be possible to offer these new preventive measures to all children,” Pekka Vartiainen, MD, a postdoctoral researcher from the University of Helsinki, said in the release. “Our research helps to identify the children who need them most, both at the individual level and in the population.”
References:
More accurate identification of children at high risk for RSV disease. https://www.helsinki.fi/en/news/public-health/more-accurate-identification-children-high-risk-rsv-disease. Published Oct. 26, 2023. Accessed Oct. 9, 2023.
RSV hospitalization risk calculator. https://rsv-risk.org/. Accessed Nov. 9, 2023.
Vartiainen P, et al. Lancet Digital Health. 2023;doi:10.1016/S2589-7500(23)00175-9.
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