New AI Tools Can Now Predict Severe RSV Cases in Children

RSV’s deadly unpredictability: Respiratory syncytial virus (RSV) infects nearly all children before age 2 and can lead to severe lung disease, causing up to 80,000 hospitalizations annually in the US and over 100,000 infant deaths globally.

• RSV symptoms typically resemble a cold, but the disease can rapidly escalate, making it challenging for healthcare providers to predict which children will be most severely affected.
• Asunción Mejías, a pediatric infectious diseases specialist at St. Jude Children’s Research Hospital, notes that 80% of children hospitalized with RSV appear healthy and lack obvious risk factors.

Innovative risk assessment tools: Researchers worldwide are developing machine learning algorithms and statistical models to identify children most vulnerable to severe RSV infections.

• These tools analyze vast databases of electronic health records to pinpoint groups of risk factors that can predict which children are more likely to be hospitalized with RSV.
• The goal is to help healthcare providers prioritize at-risk children for vaccines and other preventative measures.

Vanderbilt University’s statistical model: Respiratory epidemiologist Tina Hartert and her team have created a tool that identifies 19 RSV risk factors based on data from over 400,000 infants in the Tennessee Medicaid program.

• The model calculates an individual infant’s risk at birth, considering factors such as prenatal smoking and low birth weight.
• Hartert emphasizes that assessing combinations of risk factors is crucial, as focusing on individual factors alone may overlook many at-risk infants.

New RSV prevention measures: In 2023, US regulators approved two significant interventions to combat RSV.

• Abrysvo, a vaccine administered to pregnant mothers between weeks 32 and 36 of pregnancy, aims to ensure babies are born with protective antibodies against RSV.
• Beyfortus, a monoclonal antibody drug, can be given as a single injection to provide protection before the winter RSV season.

Financial barriers to widespread immunization: The high cost of RSV immunizations poses challenges, especially in low- and middle-income countries where 97% of RSV fatalities occur.

• Pekka Vartiainen, an RSV researcher at the University of Helsinki, highlights that access to new immunization methods is most limited in areas where children suffer from RSV the most.

RSV Risk tool: Vartiainen developed a machine learning-based tool called RSV Risk, utilizing data from over 2.6 million children in Finland and Sweden.

• The tool considers factors such as birth weight, mother’s age, family history, and congenital conditions to assess RSV vulnerability.
• Even in high-income nations, such tools can help prioritize resources by identifying the most at-risk children.

Future developments in RSV screening: Researchers are working on more advanced options to screen children upon hospital admission for severe RSV infections.

• A study led by Mejías found that more severe RSV-related disease is associated with lower production of mucosal interferons, proteins that alert immune cells to viral presence.
• Companies are developing nasal swabs to assess a child’s interferon response at hospital admission, potentially providing vital information about disease trajectory and immune system functioning.

Implications for patient care and family support: Advanced screening tools could significantly improve RSV management and reduce anxiety for families.

• These tests may help doctors triage children more effectively, provide more informed assessments, and ensure closer monitoring for high-risk cases.
• By offering clearer predictions of a child’s disease course, these tools could alleviate some of the uncertainty and stress experienced by families during hospitalization.