Identifying Kids at High Risk for RSV Disease

A new study helps to identify children who are at the highest risk of a severe respiratory syncytial virus (RSV) infection and who would thus benefit most from new RSV prevention measures.

A registry study covering all Finnish and Swedish children and their family members identified 16 major risk factors for a severe RSV infection. The researchers created a clinical prediction model to predict the risk of hospitalisation from an RSV infection and showed that the model performed well in both countries.

In recent years, both a long-acting antibody that protects against an RSV infection and a vaccine given to mothers during pregnancy have been developed to prevent RSV infections. When targeted properly, such drugs can prevent a large number of complications in young children and decrease the number of hospital and intensive care stays, but it is not yet clear how widely these approaches should be used.

The research team utilised different national registries to investigate the factors that increase the risk of hospitalisation for RSV infections in children under one year of age. The study included 1.25 million children born in Finland between 1997 and 2020 and 1.4 million children born in Sweden between 2006 and 2020, and their parents and siblings. The simple 16-variable clinical prediction model created in the study performed equally well as did the 1,511-variable AI-based model.

For creating the prediction model, health data were harmonised and coded for AI use as part of the Finnish FinRegistry study. The resulting model was replicated in the corresponding Swedish registry data.

“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. There are few countries where such a study can be done,” says Andrea Ganna, Associate Professor at the University of Helsinki, who led the study.

“This study is an example on how nationwide registry-based studies can help to target preventive efforts. The aim of the FinRegistry project is to produce scientific knowledge on risk factors and trajectories leading to various diseases, also those not observable with traditional methods,” says Research Professor Markus Perola from the Finnish Institute for Health and Welfare (THL).


Pekka Vartiainen, Sakari Jukarainen, Samuel Arthur Rhedin, Alexandra Prinz, Tuomo Hartonen, Andrius Vabalas, Essi Viippola, Rodosthenis S Rodosthenous, Sara Koskelainen, Aoxing Liu, Cecilia Lundholm, Awad I Smew, Emma Caffrey Osvald, Emmi Helle, Markus Perola, Catarina Almqvist, Santtu Heinonen, Andrea Ganna. Risk factors for severe respiratory syncytial virus infection during the first year of life: development and validation of a clinical prediction model. The Lancet Digital Health, 2023; 5 (11): e821 DOI: 10.1016/S2589-7500(23)00175-9

University of Helsinki. (2023, October 26). More accurate identification of children at high risk for RSV disease. ScienceDaily. Retrieved October 26, 2023 from
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