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New tool may help prioritize RSV vaccination in high-risk children

Following a shortage of nirsevimab for the prevention of respiratory syncytial virus (RSV) lower respiratory tract infections (LRTIs), a new tool may help identify newborns at highest risk of developing severe RSV LRTIs, according to research published in ATS 2024 International. Conference.

“Timely identification of infants at highest risk for RSV-related morbidity is key to prevention,” said senior author Brittney M. Snyder, PhD, assistant professor in the Center’s Division of Allergy, Pulmonary and Critical Care Medicine. Doctor of Vanderbilt University. “Our personalized risk prediction tool may have applications in allocating costly and/or limited immunoprophylaxis (immunization with nirsevimab or palivizumab) to achieve the greatest benefit and in promoting RSV prevention among families with high-risk infants” .

More than half of RSV LRTIs occur among healthy, full-term infants who are generally considered low risk. In fact, these babies are at risk of needing care in intensive care units and some may die from their illness. The Centers for Disease Control and Prevention recommends early immunization with nirsevimab for all infants, however, as of October 2023, nirsevimab was in short supply and the CDC recommended giving it only to high-risk infants who were not eligible for immunization with nirsevimab. palivizumab. Products that prevent RSV LRTIs in newborns and young children are monoclonal antibodies (nirsevimab is long-acting and requires only one dose, while palivizumab is short-acting and requires monthly injections during RSV season).

In the population-based study by Dr. Snyder and colleagues that included children insured by the Tennessee Medicaid Program, researchers evaluated infants who did not receive RSV immunoprophylaxis in the first year of life. They collected demographic and clinical data from administrative healthcare encounters and linked birth certificates. “To predict whether these infants developed severe RSV LRTI requiring ICU admission during the first year of life, we developed a multivariable logistic regression model. The model includes demographic and clinical variables collected at or shortly after birth; 19 variables in total, such as prenatal variables, smoking, delivery method, maternal age, and assisted breathing (ventilation) during hospitalization for delivery,” said senior biostatistician Tebeb Gebretsadik, MPH, Department of Biostatistics, Medical Center. Vanderbilt University.

Among 429,365 babies in the study, 713 had severe RSV LRTI requiring ICU admission. The tool had good predictive accuracy and internal validation indicating good fit.

“Our goal was to develop a personalized tool for use in all newborns using readily available birth and postnatal data to predict the risk of RSV LRTI requiring ICU admission, useful for prioritizing RSV prevention products with limited availability. “said principal investigator Tina V. Hartert, MD, MPH, professor of medicine and pediatrics at Vanderbilt University Medical Center. Although the recent shortage of nirsevimab has fortunately eased, it is unknown if there will be shortages in the future. “This tool may be particularly useful for prioritizing which infants should be immunized during times of limited availability of RSV prevention medications. Use the tool to identify if your infant is at high risk for RSV infection requiring ICU care as well.” can persuade vaccine-hesitant families to accept RSV immunoprophylaxis by showing them that their newborn is at high risk,” he added.

“To ensure compatibility with nirsevimab and maternal vaccination, our tool was developed for use in all infants,” concluded co-author Niek Achten, MD, postdoctoral fellow in pediatrics, Erasmus University Medical Center, Rotterdam, Netherlands. who imagined the need for such a tool. “In addition to its use in the United States during times of limited availability, our tool may be useful in countries with budget constraints that need to prioritize administration to higher-risk infants.”

The authors note that next steps to ensure optimal utility include validation of the tool in external populations, additional cost-effectiveness analyses, and decision curve analysis.