A clinical diagnostic model for predicting influenza among young adult military personnel with febrile respiratory illness in Singapore

Introduction

Influenza infections present with wide-ranging clinical features. We aim tocompare the differences in presentation between influenza and non-influenzacases among those with febrile respiratory illness (FRI) to determinepredictors of influenza infection.

Methods

Personnel with FRI (defined as fever≥37.5°C, with cough or sorethroat) were recruited from the sentinel surveillance system in theSingapore military. Nasal washes were collected, and tested using theResplex II and additional PCR assays for etiological determination.Interviewer-administered questionnaires collected information on patientdemographics and clinical features. Univariate comparison of the variousparameters was conducted, with statistically significant parameters enteredinto a multivariate logistic regression model. The final multivariate modelfor influenza versus non-influenza cases was used to build a predictiveprobability clinical diagnostic model.

Results

821 out of 2858 subjects recruited from 11 May 2009 to 25 Jun 2010 hadinfluenza, of which 434 (52.9%) had 2009 influenza A (H1N1), 58(7.1%) seasonal influenza A (H3N2) and 269 (32.8%) influenzaB. Influenza-positive cases were significantly more likely to present withrunning nose, chills and rigors, ocular symptoms and higher temperature, andless likely with sore throat, photophobia, injected pharynx, andnausea/vomiting. Our clinical diagnostic model had a sensitivity of65% (95% CI: 58%, 72%), specificity of69% (95% CI: 62%, 75%), and overall accuracy of68% (95% CI: 64%, 71%), performing significantlybetter than conventional influenza-like illness (ILI) criteria.

Conclusions

Use of a clinical diagnostic model may help predict influenza better than theconventional ILI definition among young adults with FRI.