tetano
Editor, Senior Moderator
Travel Med Infect Dis. 2012 Apr 22. [Epub ahead of print]
Distinguishing malaria and influenza: Early clinical features in controlled human experimental infection studies.
Lillie PJ, Duncan CJ, Sheehy SH, Meyer J, O'Hara GA, Gilbert SC, Hill AV.
Source
Centre for Clinical Vaccinology and Tropical Medicine, Churchill Hospital, Oxford OX3 7LJ, UK.
Abstract
During the H1N1 influenza pandemic (pH1N1/09) diagnostic algorithms were developed to guide antiviral provision. However febrile illnesses are notoriously difficult to distinguish clinically. Recent evidence highlights the importance of incorporating travel history into diagnostic algorithms to prevent the catastrophic misdiagnosis of life-threatening infections such as malaria. We applied retrospectively the UK pH1N1/09 case definition to a unique cohort of healthy adult volunteers exposed to Plasmodiumfalciparum malaria or influenza to assess the predictive value of this case definition, and to explore the distinguishing clinical features of early phase infection with these pathogens under experimental conditions. For influenza exposure the positive predictive value of the pH1N1/09 case definition was only 0.38 (95% CI: 0.06-0.60), with a negative predictive value of 0.27 (95% CI: 0.02-0.51). Interestingly, 8/11 symptomatic malaria-infected adults would have been inappropriately classified with influenza by the pH1N1/09 case definition, while 5/8 symptomatic influenza-exposed volunteers would have been classified without influenza (P = 0.18 Fisher's exact). Cough (P = 0.005) and nasal symptoms (P = 0.001) were the only clinical features that distinguished influenza-exposed from malaria-exposed volunteers. An open mind regarding the clinical cause of undifferentiated febrile illness, particularly in the absence of upper respiratory tract symptoms, remains important even during influenza pandemic settings. These data support incorporating travel history into pandemic algorithms.
Copyright ? 2012 Elsevier Ltd. All rights reserved.
PMID:
22531678
[PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/22531678
Distinguishing malaria and influenza: Early clinical features in controlled human experimental infection studies.
Lillie PJ, Duncan CJ, Sheehy SH, Meyer J, O'Hara GA, Gilbert SC, Hill AV.
Source
Centre for Clinical Vaccinology and Tropical Medicine, Churchill Hospital, Oxford OX3 7LJ, UK.
Abstract
During the H1N1 influenza pandemic (pH1N1/09) diagnostic algorithms were developed to guide antiviral provision. However febrile illnesses are notoriously difficult to distinguish clinically. Recent evidence highlights the importance of incorporating travel history into diagnostic algorithms to prevent the catastrophic misdiagnosis of life-threatening infections such as malaria. We applied retrospectively the UK pH1N1/09 case definition to a unique cohort of healthy adult volunteers exposed to Plasmodiumfalciparum malaria or influenza to assess the predictive value of this case definition, and to explore the distinguishing clinical features of early phase infection with these pathogens under experimental conditions. For influenza exposure the positive predictive value of the pH1N1/09 case definition was only 0.38 (95% CI: 0.06-0.60), with a negative predictive value of 0.27 (95% CI: 0.02-0.51). Interestingly, 8/11 symptomatic malaria-infected adults would have been inappropriately classified with influenza by the pH1N1/09 case definition, while 5/8 symptomatic influenza-exposed volunteers would have been classified without influenza (P = 0.18 Fisher's exact). Cough (P = 0.005) and nasal symptoms (P = 0.001) were the only clinical features that distinguished influenza-exposed from malaria-exposed volunteers. An open mind regarding the clinical cause of undifferentiated febrile illness, particularly in the absence of upper respiratory tract symptoms, remains important even during influenza pandemic settings. These data support incorporating travel history into pandemic algorithms.
Copyright ? 2012 Elsevier Ltd. All rights reserved.
PMID:
22531678
[PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/22531678