A clinical diagnostic model for predicting influenza among young adult military personnel with febrile respiratory illness in Singapore |
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Authors: | Lee Vernon J Yap Jonathan Cook Alex R Tan Chi Hsien Loh Jin-Phang Koh Wee-Hong Lim Elizabeth A S Liaw Jasper C W Chew Janet S W Hossain Iqbal Chan Ka Wei Ting Pei-Jun Ng Sock-Hoon Gao Qiuhan Kelly Paul M Chen Mark I Tambyah Paul A Tan Boon Huan |
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Institution: | Biodefence Centre, Ministry of Defence, Singapore, Singapore. vernonljm@hotmail.com |
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Abstract: | IntroductionInfluenza infections present with wide-ranging clinical features. We aim to
compare the differences in presentation between influenza and non-influenza
cases among those with febrile respiratory illness (FRI) to determine
predictors of influenza infection.MethodsPersonnel with FRI (defined as fever≥37.5°C, with cough or sore
throat) were recruited from the sentinel surveillance system in the
Singapore military. Nasal washes were collected, and tested using the
Resplex II and additional PCR assays for etiological determination.
Interviewer-administered questionnaires collected information on patient
demographics and clinical features. Univariate comparison of the various
parameters was conducted, with statistically significant parameters entered
into a multivariate logistic regression model. The final multivariate model
for influenza versus non-influenza cases was used to build a predictive
probability clinical diagnostic model.Results821 out of 2858 subjects recruited from 11 May 2009 to 25 Jun 2010 had
influenza, of which 434 (52.9%) had 2009 influenza A (H1N1), 58
(7.1%) seasonal influenza A (H3N2) and 269 (32.8%) influenza
B. Influenza-positive cases were significantly more likely to present with
running nose, chills and rigors, ocular symptoms and higher temperature, and
less likely with sore throat, photophobia, injected pharynx, and
nausea/vomiting. Our clinical diagnostic model had a sensitivity of
65% (95% CI: 58%, 72%), specificity of
69% (95% CI: 62%, 75%), and overall accuracy of
68% (95% CI: 64%, 71%), performing significantly
better than conventional influenza-like illness (ILI) criteria.ConclusionsUse of a clinical diagnostic model may help predict influenza better than the
conventional ILI definition among young adults with FRI. |
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