Title

Predicting influenza A and 2009 H1N1 influenza in patients admitted to hospital with acute respiratory illness

Date of this Version

3-25-2011

Document Type

Journal Article

Publication Details

Citation only.

Keijzers, G. B., Vossen, C. N. K.-L., Zhang, P., MacBeth, D., Derrington, P., Gerrard, J. G. & Doust, J. (2011). Predicting influenza A and 2009 H1N1 influenza in patients admitted to hospital with acute respiratory illness. Emergency medicine journal, 28(6), 500-506.

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2011 HERDC submission. FoR code: 110300

© Copyright BMJ Publishing Group Ltd , 2011 and the College of Emergency Medicine. All rights reserved.

Abstract

Objective: To create a clinical decision tool for suspected influenza A (including 2009 H1N1) to facilitate treatment and isolation decisions for patients admitted to hospital with an acute respiratory illness from the emergency department (ED) during a 2009 H1N1 pandemic.

Methods: Cross-sectional study conducted in two hospitals in Queensland, Australia. All patients admitted to hospital from the ED between 24 May and 16 August 2009 with an acute respiratory illness were included. All had nasal and throat swabs taken. Data were collected from clinical chart review regarding clinical symptoms, co-morbidities, examination findings, pathology and radiology results. Influenza A status was detected by reverse transcription - PCR assay. Univariate and multivariate regression analyses were performed to identify independent predictors of influenza A status.

Results: 346 consecutive patients were identified, of which 106 were positive for 2009 H1N1 influenza; an additional 11 patients were positive for other influenza A viruses. Independent clinical predictors (with points allocated using weighted scoring) for all types of influenza A in patients admitted with acute respiratory illness were: age 18-64 years (2 points); history of fever (2); cough (1); normal level of consciousness (2); C-reactive protein >5 and ≤100 mg/l (2) and normal leucocyte count (1). A clinical score of 5 (presence of two or three predictors) gave a sensitivity of 93% (95% CI 87% to 96%), specificity of 36% (95% CI 30% to 42%), resulting in a negative-predictive value of 91% (95% CI 83% to 95%).

Conclusion: A clinical prediction tool was developed that may be able to assist in making appropriate isolation decisions during future 2009 H1N1 outbreaks.

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This document has been peer reviewed.