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New tool aims to predict influenza risk :

October 5, 2017

Researchers have created a tool to help determine a child’s risk of having influenza and reduce the number being treated unnecessarily.

The Suspected Pediatric Influenza Risk-Stratification Algorithm “aids clinicians in determining who to test versus who to treat empirically, saving children from costly viral testing or unnecessary antiviral exposure,” authors wrote.

During peak influenza season, the Centers for Disease Control and Prevention (CDC) recommends treating children who may have the virus and are at high risk for complications. Physicians also may consider treating previously healthy children. Because these groups may be treated before laboratory confirmation of influenza, children may be overtreated, according to the study.

Researchers set out to create an algorithm to help determine the likelihood a child has influenza. They analyzed medical records for 818 children presenting to an emergency department with influenza-like symptoms in the 2012-’13 influenza season.

Samples were collected from patients meeting CDC protocol, and patients were given a prescription for oseltamivir, then discharged with recommendations for supportive care. Later in the day, families were notified of the test results and whether to start treatment.

About 35% tested positive for influenza. Researchers determined children younger than 2 years were at high risk for influenza if they were in contact with someone who had influenza. They also were more likely to have influenza during high-incidence periods if they were unimmunized or if they were immunized but had a cough.

Children 2 and older were more likely to have influenza during low-incidence periods if they were unimmunized. During high-incidence periods, children were more likely to have influenza if they presented with myalgia or if neither myalgia nor diarrhea were present.

Authors noted the algorithm is not meant to diagnose flu but to quantify the risk and serve as a companion to CDC guidelines.

“… we believe that a tool that gives clinicians, patients, and families more precise information regarding the likelihood of influenza infection will help patients and providers to more effectively make testing and treatment decisions,” they wrote.

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