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http://www.aemj.org/cgi/content/abstract/10/5/440-b
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Academic Emergency Medicine Volume 10, Number 5 440-441,
© 2003 Society for Academic
Emergency Medicine
PEDIATRICS |
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.
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Copyright © 2003 by the Society for Academic Emergency Medicine.
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