Predictive model for the diagnosis of tuberculous pleural effusion

Detalhes bibliográficos
Autor(a) principal: Neves,Denise Duprat
Data de Publicação: 2007
Outros Autores: Dias,Ricardo Marques, Cunha,Antônio José Ledo A. da
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Brazilian Journal of Infectious Diseases
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1413-86702007000100019
Resumo: This study developed a predictive model to identify pleural tuberculosis. A consecutive cases study of patients investigating the cause of pleural effusion, in an area of high prevalence of tuberculosis (Rio de Janeiro, Brazil). Clinical and laboratory variables were compared among patients with tuberculosis (TB) and without tuberculosis (NTB), individually and using logistic regression. The performance was described as diagnostic accuracy, compared to a gold standard in a masked way. We have studied 104 TB patients, 41 with malignant, 29 transudates, 28 parapneumonic, 13 with miscellaneous diseases. After identification of individual discrimination power aided by clinical, radiological and laboratory variables, the following ones were included in a multivariate analysis: ADA, total leukocytes, percentile of lymphocytes, protein, lactate dehydrogenase, duration of disease, age and gender. A logistic regression model to predict pleural tuberculosis including the five first variables showed the best performance. A receiver operating characteristic curve identified the best cutoff at 0.7, resulting in a sensitivity and specificity of more then 95%. The predictive model improved the specificity of ADA alone, keeping its sensitivity. This model seems helpful when a microbiological or histological diagnosis of pleural tuberculosis could not be established. External validation of these results is necessary before recommendation for routine application.
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spelling Predictive model for the diagnosis of tuberculous pleural effusionSensitivity and specificitytuberculosispleural effusionspredictive modeldiagnosisThis study developed a predictive model to identify pleural tuberculosis. A consecutive cases study of patients investigating the cause of pleural effusion, in an area of high prevalence of tuberculosis (Rio de Janeiro, Brazil). Clinical and laboratory variables were compared among patients with tuberculosis (TB) and without tuberculosis (NTB), individually and using logistic regression. The performance was described as diagnostic accuracy, compared to a gold standard in a masked way. We have studied 104 TB patients, 41 with malignant, 29 transudates, 28 parapneumonic, 13 with miscellaneous diseases. After identification of individual discrimination power aided by clinical, radiological and laboratory variables, the following ones were included in a multivariate analysis: ADA, total leukocytes, percentile of lymphocytes, protein, lactate dehydrogenase, duration of disease, age and gender. A logistic regression model to predict pleural tuberculosis including the five first variables showed the best performance. A receiver operating characteristic curve identified the best cutoff at 0.7, resulting in a sensitivity and specificity of more then 95%. The predictive model improved the specificity of ADA alone, keeping its sensitivity. This model seems helpful when a microbiological or histological diagnosis of pleural tuberculosis could not be established. External validation of these results is necessary before recommendation for routine application.Brazilian Society of Infectious Diseases2007-02-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1413-86702007000100019Brazilian Journal of Infectious Diseases v.11 n.1 2007reponame:Brazilian Journal of Infectious Diseasesinstname:Brazilian Society of Infectious Diseases (BSID)instacron:BSID10.1590/S1413-86702007000100019info:eu-repo/semantics/openAccessNeves,Denise DupratDias,Ricardo MarquesCunha,Antônio José Ledo A. daeng2007-06-29T00:00:00Zoai:scielo:S1413-86702007000100019Revistahttps://www.bjid.org.br/https://old.scielo.br/oai/scielo-oai.phpbjid@bjid.org.br||lgoldani@ufrgs.br1678-43911413-8670opendoar:2007-06-29T00:00Brazilian Journal of Infectious Diseases - Brazilian Society of Infectious Diseases (BSID)false
dc.title.none.fl_str_mv Predictive model for the diagnosis of tuberculous pleural effusion
title Predictive model for the diagnosis of tuberculous pleural effusion
spellingShingle Predictive model for the diagnosis of tuberculous pleural effusion
Neves,Denise Duprat
Sensitivity and specificity
tuberculosis
pleural effusions
predictive model
diagnosis
title_short Predictive model for the diagnosis of tuberculous pleural effusion
title_full Predictive model for the diagnosis of tuberculous pleural effusion
title_fullStr Predictive model for the diagnosis of tuberculous pleural effusion
title_full_unstemmed Predictive model for the diagnosis of tuberculous pleural effusion
title_sort Predictive model for the diagnosis of tuberculous pleural effusion
author Neves,Denise Duprat
author_facet Neves,Denise Duprat
Dias,Ricardo Marques
Cunha,Antônio José Ledo A. da
author_role author
author2 Dias,Ricardo Marques
Cunha,Antônio José Ledo A. da
author2_role author
author
dc.contributor.author.fl_str_mv Neves,Denise Duprat
Dias,Ricardo Marques
Cunha,Antônio José Ledo A. da
dc.subject.por.fl_str_mv Sensitivity and specificity
tuberculosis
pleural effusions
predictive model
diagnosis
topic Sensitivity and specificity
tuberculosis
pleural effusions
predictive model
diagnosis
description This study developed a predictive model to identify pleural tuberculosis. A consecutive cases study of patients investigating the cause of pleural effusion, in an area of high prevalence of tuberculosis (Rio de Janeiro, Brazil). Clinical and laboratory variables were compared among patients with tuberculosis (TB) and without tuberculosis (NTB), individually and using logistic regression. The performance was described as diagnostic accuracy, compared to a gold standard in a masked way. We have studied 104 TB patients, 41 with malignant, 29 transudates, 28 parapneumonic, 13 with miscellaneous diseases. After identification of individual discrimination power aided by clinical, radiological and laboratory variables, the following ones were included in a multivariate analysis: ADA, total leukocytes, percentile of lymphocytes, protein, lactate dehydrogenase, duration of disease, age and gender. A logistic regression model to predict pleural tuberculosis including the five first variables showed the best performance. A receiver operating characteristic curve identified the best cutoff at 0.7, resulting in a sensitivity and specificity of more then 95%. The predictive model improved the specificity of ADA alone, keeping its sensitivity. This model seems helpful when a microbiological or histological diagnosis of pleural tuberculosis could not be established. External validation of these results is necessary before recommendation for routine application.
publishDate 2007
dc.date.none.fl_str_mv 2007-02-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1413-86702007000100019
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1413-86702007000100019
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S1413-86702007000100019
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Brazilian Society of Infectious Diseases
publisher.none.fl_str_mv Brazilian Society of Infectious Diseases
dc.source.none.fl_str_mv Brazilian Journal of Infectious Diseases v.11 n.1 2007
reponame:Brazilian Journal of Infectious Diseases
instname:Brazilian Society of Infectious Diseases (BSID)
instacron:BSID
instname_str Brazilian Society of Infectious Diseases (BSID)
instacron_str BSID
institution BSID
reponame_str Brazilian Journal of Infectious Diseases
collection Brazilian Journal of Infectious Diseases
repository.name.fl_str_mv Brazilian Journal of Infectious Diseases - Brazilian Society of Infectious Diseases (BSID)
repository.mail.fl_str_mv bjid@bjid.org.br||lgoldani@ufrgs.br
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