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Defining the True Sensitivity of Culture for the Diagnosis of Melioidosis Using Bayesian Latent Class Models

BACKGROUND: Culture remains the diagnostic gold standard for many bacterial infections, and the method against which other tests are often evaluated. Specificity of culture is 100% if the pathogenic organism is not found in healthy subjects, but the sensitivity of culture is more difficult to determ...

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Autores principales: Limmathurotsakul, Direk, Jamsen, Kris, Arayawichanont, Arkhom, Simpson, Julie A., White, Lisa J., Lee, Sue J., Wuthiekanun, Vanaporn, Chantratita, Narisara, Cheng, Allen, Day, Nicholas P. J., Verzilli, Claudio, Peacock, Sharon J.
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2932979/
https://www.ncbi.nlm.nih.gov/pubmed/20830194
http://dx.doi.org/10.1371/journal.pone.0012485
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author Limmathurotsakul, Direk
Jamsen, Kris
Arayawichanont, Arkhom
Simpson, Julie A.
White, Lisa J.
Lee, Sue J.
Wuthiekanun, Vanaporn
Chantratita, Narisara
Cheng, Allen
Day, Nicholas P. J.
Verzilli, Claudio
Peacock, Sharon J.
author_facet Limmathurotsakul, Direk
Jamsen, Kris
Arayawichanont, Arkhom
Simpson, Julie A.
White, Lisa J.
Lee, Sue J.
Wuthiekanun, Vanaporn
Chantratita, Narisara
Cheng, Allen
Day, Nicholas P. J.
Verzilli, Claudio
Peacock, Sharon J.
author_sort Limmathurotsakul, Direk
collection PubMed
description BACKGROUND: Culture remains the diagnostic gold standard for many bacterial infections, and the method against which other tests are often evaluated. Specificity of culture is 100% if the pathogenic organism is not found in healthy subjects, but the sensitivity of culture is more difficult to determine and may be low. Here, we apply Bayesian latent class models (LCMs) to data from patients with a single Gram-negative bacterial infection and define the true sensitivity of culture together with the impact of misclassification by culture on the reported accuracy of alternative diagnostic tests. METHODS/PRINCIPAL FINDINGS: Data from published studies describing the application of five diagnostic tests (culture and four serological tests) to a patient cohort with suspected melioidosis were re-analysed using several Bayesian LCMs. Sensitivities, specificities, and positive and negative predictive values (PPVs and NPVs) were calculated. Of 320 patients with suspected melioidosis, 119 (37%) had culture confirmed melioidosis. Using the final model (Bayesian LCM with conditional dependence between serological tests), the sensitivity of culture was estimated to be 60.2%. Prediction accuracy of the final model was assessed using a classification tool to grade patients according to the likelihood of melioidosis, which indicated that an estimated disease prevalence of 61.6% was credible. Estimates of sensitivities, specificities, PPVs and NPVs of four serological tests were significantly different from previously published values in which culture was used as the gold standard. CONCLUSIONS/SIGNIFICANCE: Culture has low sensitivity and low NPV for the diagnosis of melioidosis and is an imperfect gold standard against which to evaluate alternative tests. Models should be used to support the evaluation of diagnostic tests with an imperfect gold standard. It is likely that the poor sensitivity/specificity of culture is not specific for melioidosis, but rather a generic problem for many bacterial and fungal infections.
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spelling pubmed-29329792010-09-09 Defining the True Sensitivity of Culture for the Diagnosis of Melioidosis Using Bayesian Latent Class Models Limmathurotsakul, Direk Jamsen, Kris Arayawichanont, Arkhom Simpson, Julie A. White, Lisa J. Lee, Sue J. Wuthiekanun, Vanaporn Chantratita, Narisara Cheng, Allen Day, Nicholas P. J. Verzilli, Claudio Peacock, Sharon J. PLoS One Research Article BACKGROUND: Culture remains the diagnostic gold standard for many bacterial infections, and the method against which other tests are often evaluated. Specificity of culture is 100% if the pathogenic organism is not found in healthy subjects, but the sensitivity of culture is more difficult to determine and may be low. Here, we apply Bayesian latent class models (LCMs) to data from patients with a single Gram-negative bacterial infection and define the true sensitivity of culture together with the impact of misclassification by culture on the reported accuracy of alternative diagnostic tests. METHODS/PRINCIPAL FINDINGS: Data from published studies describing the application of five diagnostic tests (culture and four serological tests) to a patient cohort with suspected melioidosis were re-analysed using several Bayesian LCMs. Sensitivities, specificities, and positive and negative predictive values (PPVs and NPVs) were calculated. Of 320 patients with suspected melioidosis, 119 (37%) had culture confirmed melioidosis. Using the final model (Bayesian LCM with conditional dependence between serological tests), the sensitivity of culture was estimated to be 60.2%. Prediction accuracy of the final model was assessed using a classification tool to grade patients according to the likelihood of melioidosis, which indicated that an estimated disease prevalence of 61.6% was credible. Estimates of sensitivities, specificities, PPVs and NPVs of four serological tests were significantly different from previously published values in which culture was used as the gold standard. CONCLUSIONS/SIGNIFICANCE: Culture has low sensitivity and low NPV for the diagnosis of melioidosis and is an imperfect gold standard against which to evaluate alternative tests. Models should be used to support the evaluation of diagnostic tests with an imperfect gold standard. It is likely that the poor sensitivity/specificity of culture is not specific for melioidosis, but rather a generic problem for many bacterial and fungal infections. Public Library of Science 2010-08-30 /pmc/articles/PMC2932979/ /pubmed/20830194 http://dx.doi.org/10.1371/journal.pone.0012485 Text en Limmathurotsakul et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Limmathurotsakul, Direk
Jamsen, Kris
Arayawichanont, Arkhom
Simpson, Julie A.
White, Lisa J.
Lee, Sue J.
Wuthiekanun, Vanaporn
Chantratita, Narisara
Cheng, Allen
Day, Nicholas P. J.
Verzilli, Claudio
Peacock, Sharon J.
Defining the True Sensitivity of Culture for the Diagnosis of Melioidosis Using Bayesian Latent Class Models
title Defining the True Sensitivity of Culture for the Diagnosis of Melioidosis Using Bayesian Latent Class Models
title_full Defining the True Sensitivity of Culture for the Diagnosis of Melioidosis Using Bayesian Latent Class Models
title_fullStr Defining the True Sensitivity of Culture for the Diagnosis of Melioidosis Using Bayesian Latent Class Models
title_full_unstemmed Defining the True Sensitivity of Culture for the Diagnosis of Melioidosis Using Bayesian Latent Class Models
title_short Defining the True Sensitivity of Culture for the Diagnosis of Melioidosis Using Bayesian Latent Class Models
title_sort defining the true sensitivity of culture for the diagnosis of melioidosis using bayesian latent class models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2932979/
https://www.ncbi.nlm.nih.gov/pubmed/20830194
http://dx.doi.org/10.1371/journal.pone.0012485
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