A Clinical Diagnostic Model for Predicting Influenza among Young Adult Military Personnel with Febrile Respiratory Illness in Singapore

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.

Clinical characteristics and outcomes of influenza and other influenza-like illnesses in Mexico City

“…It has been reported in other populations that multiple respiratory viruses such as parainfluenza virus, rhinovirus, adenovirus, metapneumovirus, respiratory syncytial virus (RSV), and coronavirus can cause ILI, 4 but the virologic etiology varies greatly among geographic locales, age groups, seasons, and years. [5][6][7][8][9][10][11][12][13] Some studies have suggested that specific symptoms could be used for the clinical prediction of the etiology of ILI, [14][15][16][17][18] though most of these studies only compared influenza to noninfluenza etiology.…”

The viral etiology of an influenza‐like illness during the 2009 pandemic

“…Differentiating infections caused by influenza viruses from those caused by other respiratory viruses is essential for case management, as illustrated during the 2009 A/H1N1 influenza pandemic (A/H1N1p). Many definitions of influenza-like illness (ILI) have been used worldwide in influenza surveillance; however, the sensitivity and positive predictive value of such definitions significantly vary depending on the co-circulation of other respiratory viruses in the community [Boivin et al, 2000;Lee et al, 2011;Thursky et al, 2003]. The identification of the respiratory viruses that are responsible for influenza-like illness has been reported in many countries, and the percentage of positive swabs for at least one virus ranges from 32% to 65% [Bellei et al, 2008;Laguna-Torres et al, 2009;Ren et al, 2009;Buecher et al, 2010;Renois et al, 2010;Razanajatovo et al, 2011].…”

An Under-Recognized Influenza Epidemic Identified by Rapid Influenza Testing, Southern Sri Lanka, 2013

“…18 Symptoms including nausea/vomiting, myalgias, and headache have all been previously reported more commonly in patients with influenza than in influenza-negative patients. 10,19,20 In our study, children with influenza were older (median age 9.4 years) than children who tested negative for influenza (median age 5.5 years), which may indicate varying patterns of exposure and immunity within age groups. The other etiologies of ILI in this community need to be further explored.…”

“…Predictive models for influenza were constructed separately for children and adults using multivariable logistic regression and generally following previously described methods. 10 In our analyses, any sociodemographic feature or clinical variable that had a P value less than 0.05 on bivariable analysis was included in the predictive model, with the exception of variables related to 1) finances and productivity, 2) the anatomic location of sampling, and 3) diagnoses and treatment received. These variables were not included as they were considered either minimally relevant to a clinical predictive model or unavailable at the time a clinician would be using the model.…”