RSV Severe Infection Risk Stratification in a French 5-Year Birth Cohort Using Machine-learning

Abstract

Background: Respiratory syncytial virus (RSV) poses a substantial threat to infants, often leading to challenges in hospital capacity. With recent pharmaceutical developments to be used during the prenatal and perinatal periods aimed at decreasing the RSV burden, there is a pressing need to identify infants at risk of severe disease. We aimed to stratify the risk of developing a clinically severe RSV infection in infants under 1 year of age. Methods: This retrospective observational study was conducted at the Hospices Civils de Lyon, France, involving infants born between 2014 and 2018. This study focused on infants hospitalized with severe and very severe acute lower respiratory tract infections associated with RSV (SARI-WI group). Data collection included perinatal information and clinical data, with machine-learning algorithms used to discriminate SARI-WI cases from nonhospitalized infants. Results: Of 42,069 infants, 555 developed SARI-WI. Infants born in November were very likely (>80%) predicted SARI-WI. Infants born in October were very likely predicted SARI-WI except for births at term by vaginal delivery and without siblings. Infants were very unlikely (<10%) predicted SARI-WI when all the following conditions were met: born in other months, at term, by vaginal delivery and without siblings. Other infants were possibly (10–30%) or probably (30–80%) predicted SARI-WI. Conclusions: Although RSV preventive measures are vital for all infants, and specific recommendations exist for patients with high-risk comorbidities, in situations where prioritization becomes necessary, infants born just before or within the early weeks of the epidemic should be considered as a risk group.