Predictive model for the diagnosis of tuberculous pleural effusion
Autor(a) principal: | |
---|---|
Data de Publicação: | 2007 |
Outros Autores: | , |
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. |
id |
BSID-1_390c33baf6c479f09a54ea6fcac3e460 |
---|---|
oai_identifier_str |
oai:scielo:S1413-86702007000100019 |
network_acronym_str |
BSID-1 |
network_name_str |
Brazilian Journal of Infectious Diseases |
repository_id_str |
|
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 |
eu_rights_str_mv |
openAccess |
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 |
_version_ |
1754209239770857472 |