Abstract
ABSTRACTBackgroundNovel immunisation methods against respiratory syncytial virus (RSV) are emerging, but knowledge of risk factors for severe RSV disease is insufficient for their optimal targeting. We aimed to identify predictors for RSV hospitalisation, and to develop and validate a clinical prediction model to guide RSV immunoprophylaxis for under 1-year-old infants.MethodsIn this retrospective cohort study using nationwide registries, we studied all infants born in 1997-2020 in Finland (n = 1 254 913) and in 2006-2020 in Sweden (n = 1 459 472), and their parents and siblings. We screened 1 510 candidate predictors and we created a logistic regression model with 16 predictors and compared its performance to a machine learning model (XGboost) using all 1 510 candidate predictors.FindingsIn addition to known predictors such as severe congenital heart defects (CHD, adjusted odds ratio (aOR) 2·89, 95% confidence interval 2·28-3·65), we identified novel predictors for RSVH, most notably esophageal malformations (aOR 3·11, 1·86-5·19) and lower complexity CHDs (aOR 1·43, 1·25-1·63).In validation data from 2018-2020, the C-statistic was 0·766 (0·742-0·789) in Finland and 0·737 (0·710-0·762) in Sweden. The clinical prediction model’s performance was similar to the machine learning model (C-statistic in Finland 0·771, 0·754-0·788). Calibration varied according to epidemic intensity. Model performance was similar across different strata of parental income.The infants in the 90th percentile of predicted RSVH probability hospitalisation had 3·3 times higher observed risk than the population’s average. Assuming 60% effectiveness, immunisation in this top 10% of infants at highest risk would have a number needed to treat of 23 in Finland and 40 in Sweden in preventing hospitalisations.InterpretationThe identified predictors and the prediction model can be used in guiding RSV immunoprophylaxis in infants.FundingSigrid Jusélius Foundation, European Research Council, Pediatric Research Foundation (for complete list of funding sources, see Acknowledgements).
Publisher
Cold Spring Harbor Laboratory