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Academic Emergency Medicine Volume 10, Number 5 440-441,
© 2003 Society for Academic Emergency Medicine
 

Clinical Predictors of Influenza in Children

Marla J Friedman and Magdy W Attia

Alfred I. duPont Hospital for Children: Wilmington, DE

ABSTRACT

Background: The classic symptoms of influenza in older children and adults are not easily identified in young children. Rapid diagnostic tests used to diagnose influenza are limited by their sensitivity and specificity. A prediction model that identifies children likely to have influenza can enhance the predictive values of these tests. Objective: To identify clinical features predictive of influenza infection in children. Methods: Patients who presented to a pediatric emergency department with a febrile respiratory illness from January to March 2002 were eligible. Children suspected of having influenza infection based on a predetermined set of criteria were enrolled. Each patient received a nasal wash for both rapid influenza testing and viral culture. Data was collected prospectively using a standardized form. Influenza patients were compared to those with negative test results. Logistic regression analysis was performed to determine clinical features significantly associated with influenza. Results: Samples were collected on 128 patients. Median age of patients was 4.8 years; 54% were male. Viral isolates included:influenza A 45 (35%), influenza B 13 (10%), other viruses 10 (8%), no virus 60 (47%). Seventeen cases (14%) were removed from the regression analysis due to incomplete data. Cough (p = 0.004), headache (p = 0.011), and pharyngitis (p = 0.018) were independently associated with influenza infection. Notably, fever and myalgias were not significant. Hosmer-Lemeshow goodness of fit test was not significant (² 7.7; p = 0.45). The model's sensitivity is 78% (95%CI: 63,88); the specificity is 79% (95%CI: 65,88), with a likelihood ratio of 3.6 (95%CI: 2,6). Conclusion: The clinical triad of cough, headache, and pharyngitis could be a useful clinical predictor of influenza infection in children. Utilizing a pediatric prediction model that selects patients likely to have influenza may enhance the diagnostic abilities of available laboratory tests.

Copyright © 2003 by the Society for Academic Emergency Medicine.

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