Improving the Clinical Diagnosis of Influenza—a Comparative Analysis of New Influenza A (H1N1) Cases

BACKGROUND: The presentation of new influenza A(H1N1) is broad and evolving as it continues to affect different geographic locations and populations. To improve the accuracy of predicting influenza infection in an outpatient setting, we undertook a comparative analysis of H1N1(2009), seasonal influenza, and persons with acute respiratory illness (ARI) in an outpatient setting. METHODOLOGY/PRINCIPAL FINDINGS: Comparative analyses of one hundred non-matched cases each of PCR confirmed H1N1(2009), seasonal influenza, and ARI cases. Multivariate analysis was performed to look for predictors of influenza infection. Receiver operating characteristic curves were constructed for various combinations of clinical and laboratory case definitions. The initial clinical and laboratory features of H1N1(2009) and seasonal influenza were similar. Among ARI cases, fever, cough, headache, rhinorrhea, the absence of leukocytosis, and a normal chest radiograph positively predict for both PCR-confirmed H1N1-2009 and seasonal influenza infection. The sensitivity and specificity of current WHO and CDC influenza-like illness (ILI) criteria were modest in predicting influenza infection. However, the combination of WHO ILI criteria with the absence of leukocytosis greatly improved the accuracy of diagnosing H1N1(2009) and seasonal influenza (positive LR of 7.8 (95%CI 3.5-17.5) and 9.2 (95%CI 4.1-20.3) respectively). CONCLUSIONS/SIGNIFICANCE: The clinical presentation of H1N1(2009) infection is largely indistinguishable from that of seasonal influenza. Among patients with acute respiratory illness, features such as a temperature greater than 38 degrees C, rhinorrhea, a normal chest radiograph, and the absence of leukocytosis or significant gastrointestinal symptoms were all positively associated with H1N1(2009) and seasonal influenza infection. An enhanced ILI criteria that combines both a symptom complex with the absence of leukocytosis on testing can improve the accuracy of predicting both seasonal and H1N1-2009 influenza infection

Forecasting daily attendances at an emergency department to aid resource planning

<p>Abstract</p> <p>Background</p> <p>Accurate forecasting of emergency department (ED) attendances can be a valuable tool for micro and macro level planning.</p> <p>Methods</p> <p>Data for analysis was the counts of daily patient attendances at the ED of an acute care regional general hospital from July 2005 to Mar 2008. Patients were stratified into three acuity categories; i.e. P1, P2 and P3, with P1 being the most acute and P3 being the least acute. The autoregressive integrated moving average (ARIMA) method was separately applied to each of the three acuity categories and total patient attendances. Independent variables included in the model were public holiday (yes or no), ambient air quality measured by pollution standard index (PSI), daily ambient average temperature and daily relative humidity. The seasonal components of weekly and yearly periodicities in the time series of daily attendances were also studied. Univariate analysis by t-tests and multivariate time series analysis were carried out in SPSS version 15.</p> <p>Results</p> <p>By time series analyses, P1 attendances did not show any weekly or yearly periodicity and was only predicted by ambient air quality of PSI > 50. P2 and total attendances showed weekly periodicities, and were also significantly predicted by public holiday. P3 attendances were significantly correlated with day of the week, month of the year, public holiday, and ambient air quality of PSI > 50.</p> <p>After applying the developed models to validate the forecast, the MAPE of prediction by the models were 16.8%, 6.7%, 8.6% and 4.8% for P1, P2, P3 and total attendances, respectively. The models were able to account for most of the significant autocorrelations present in the data.</p> <p>Conclusion</p> <p>Time series analysis has been shown to provide a useful, readily available tool for predicting emergency department workload that can be used to plan staff roster and resource planning.</p

Receiver operating characteristic curves.

<p>(A) H1N1 versus acute respiratory illness, where Area under the receiver operating characteristic curve (AUROC) values are 0.839 for multivariate logistic regression model using symptoms and signs only, and 0.874 when adding laboratory and chest radiograph (CXR) findings. (B) seasonal influenza versus acute respiratory illness. AUROC values are 0.842 for symptoms and signs only, and 0.893 when adding laboratory and CXR findings.</p

Demographics, co-morbidities, and history of travel.

*<p>2-sided p-value by Student's t test.</p>†<p>2-sided p-value by chi-squared test or Fisher's exact test</p>‡<p>Includes diabetes, asthma (excluding childhood asthma), COPD (chronic obstructive pulmonary disease), chronic bronchitis, cardiovascular disease (excluding hypertension) and conditions possibly causing an immuno-compromised state</p

Date of presentation of adult H1N1(2009) and seasonal influenza cases by epidemiological week.

<p>Note that there was inconsistent testing for influenza strains other than H1N1(2009) after 11 June 2009 (Week 23), and the data for seasonal influenza is hence censored after week 23.</p

Sensitivity and specificity of combinations of symptoms, signs, and investigations for distinguishing H1N1(2009).

<p>*AUROC: Area under Receiver Operating Characteristic curve.</p>†<p>LR: likelihood ratio.</p

Univariate and multivariate analyses comparing H1N1(2009) and ARI patients.

<p>Univariate and multivariate analyses comparing H1N1(2009) and ARI patients.</p