Dataset Information

Estimating the true accuracy of diagnostic tests for dengue infection using bayesian latent class models.

ABSTRACT:

Background

Accuracy of rapid diagnostic tests for dengue infection has been repeatedly estimated by comparing those tests with reference assays. We hypothesized that those estimates might be inaccurate if the accuracy of the reference assays is not perfect. Here, we investigated this using statistical modeling.

Methods/principal findings

Data from a cohort study of 549 patients suspected of dengue infection presenting at Colombo North Teaching Hospital, Ragama, Sri Lanka, that described the application of our reference assay (a combination of Dengue IgM antibody capture ELISA and IgG antibody capture ELISA) and of three rapid diagnostic tests (Panbio NS1 antigen, IgM antibody and IgG antibody rapid immunochromatographic cassette tests) were re-evaluated using bayesian latent class models (LCMs). The estimated sensitivity and specificity of the reference assay were 62.0% and 99.6%, respectively. Prevalence of dengue infection (24.3%), and sensitivities and specificities of the Panbio NS1 (45.9% and 97.9%), IgM (54.5% and 95.5%) and IgG (62.1% and 84.5%) estimated by bayesian LCMs were significantly different from those estimated by assuming that the reference assay was perfect. Sensitivity, specificity, PPV and NPV for a combination of NS1, IgM and IgG cassette tests on admission samples were 87.0%, 82.8%, 62.0% and 95.2%, respectively.

Conclusions

Our reference assay is an imperfect gold standard. In our setting, the combination of NS1, IgM and IgG rapid diagnostic tests could be used on admission to rule out dengue infection with a high level of accuracy (NPV 95.2%). Further evaluation of rapid diagnostic tests for dengue infection should include the use of appropriate statistical models.

SUBMITTER: Pan-ngum W

PROVIDER: S-EPMC3548900 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

Estimating the true accuracy of diagnostic tests for dengue infection using bayesian latent class models.

Pan-ngum Wirichada W Blacksell Stuart D SD Lubell Yoel Y Pukrittayakamee Sasithon S Bailey Mark S MS de Silva H Janaka HJ Lalloo David G DG Day Nicholas P J NP White Lisa J LJ Limmathurotsakul Direk D

PloS one 20130118 1

<h4>Background</h4>Accuracy of rapid diagnostic tests for dengue infection has been repeatedly estimated by comparing those tests with reference assays. We hypothesized that those estimates might be inaccurate if the accuracy of the reference assays is not perfect. Here, we investigated this using statistical modeling.<h4>Methods/principal findings</h4>Data from a cohort study of 549 patients suspected of dengue infection presenting at Colombo North Teaching Hospital, Ragama, Sri Lanka, that des ...[more]

PMID: 23349667

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Dataset Information

Estimating the true accuracy of diagnostic tests for dengue infection using bayesian latent class models.

Background

Methods/principal findings

Conclusions

Publications

Estimating the true accuracy of diagnostic tests for dengue infection using bayesian latent class models.

Similar Datasets

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