Please use this identifier to cite or link to this item: https://www.arca.fiocruz.br/handle/icict/27466
Title: Reliable classifier to differentiate primary and secondary acute dengue infection based on IgG ELISA
Authors: Cordeiro, Marli Tenório
Braga-Neto, Ulisses
Nogueira, Rita Maria Ribeiro
Marques, Ernesto T. A.
Affilliation: Fundação Oswaldo Cruz. Instituto Aggeu Magalhães. Laboratório de Virologia e Terapia Experimental. Recife, PE, Brasil / Secretaria de Saúde do Estado de Pernambuco. Laboratório Central de Saúde Pública. Recife, PE, Brasil.
Fundação Oswaldo Cruz. Instituto Aggeu Magalhães. Laboratório de Virologia e Terapia Experimental. Recife, PE, Brasil / Texas A&M University. College Station. Department of Electrical and Computer Engineering. Texas, United States of America.
Fiocruz. Instituto Oswaldo Cruz. Laboratório de Flavivirus. Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Instituto Aggeu Magalhães. Laboratório de Virologia e Terapia Experimental. Recife, PE, Brasil / The Johns Hopkins School of Medicine. Division of Infectious Diseases. Department of Medicine. Baltimore, Maryland, United States of America / The Johns Hopkins School of Medicine. Department of Pharmacology and Molecular Sciences. Baltimore, Maryland, United States of America.
Abstract: Dengue virus infection causes a wide spectrum of illness, ranging from sub-clinical to severe disease. Severe dengue is associated with sequential viral infections. A strict definition of primary versus secondary dengue infections requires a combination of several tests performed at different stages of the disease, which is not practical. Go to: Methods and Findings We developed a simple method to classify dengue infections as primary or secondary based on the levels of dengue-specific IgG. A group of 109 dengue infection patients were classified as having primary or secondary dengue infection on the basis of a strict combination of results from assays of antigen-specific IgM and IgG, isolation of virus and detection of the viral genome by PCR tests performed on multiple samples, collected from each patient over a period of 30 days. The dengue-specific IgG levels of all samples from 59 of the patients were analyzed by linear discriminant analysis (LDA), and one- and two-dimensional classifiers were designed. The one-dimensional classifier was estimated by bolstered resubstitution error estimation to have 75.1% sensitivity and 92.5% specificity. The two-dimensional classifier was designed by taking also into consideration the number of days after the onset of symptoms, with an estimated sensitivity and specificity of 91.64% and 92.46%. The performance of the two-dimensional classifier was validated using an independent test set of standard samples from the remaining 50 patients. The classifications of the independent set of samples determined by the two-dimensional classifiers were further validated by comparing with two other dengue classification methods: hemagglutination inhibition (HI) assay and an in-house anti-dengue IgG-capture ELISA method. The decisions made with the two-dimensional classifier were in 100% accordance with the HI assay and 96% with the in-house ELISA. Go to: Conclusions Once acute dengue infection has been determined, a 2-D classifier based on common dengue virus IgG kits can reliably distinguish primary and secondary dengue infections. Software for calculation and validation of the 2-D classifier is made available for download.
Keywords: Dengue
PCR
Infection
keywords: Dengue
PCR
Infecção
DeCS: Doença Aguda
Dengue / classificação
Dengue / imunologia
Ensaio de Imunoadsorção Enzimática
Fêmea
Testes de hemaglutinação
Humanos
Imunoglobulina G / imunologia
Masculino
Reação em Cadeia da Polimerase
Padrões de referência
Sensibilidade e Especificidade
Issue Date: 2009
Citation: CORDEIRO, M. T. et al. Reliable classifier to differentiate primary and secondary acute dengue infection based on IgG ELISA. PloS One, v. 4, n. 4, p. e4945, 2009.
DOI: 10.1371/journal.pone.0004945
ISSN: 1932-6203
Copyright: open access
Appears in Collections:PE - IAM - Artigos de Periódicos

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