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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
general
Rapid Diagnostic Algorithms as a Screening Tool for Tuberculosis: An Assessor Blinded Cross-Sectional Study
PLoS ONE, Volume 7, No. 11, Article e49658, Year 2012
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Description
Background: A major obstacle to effectively treat and control tuberculosis is the absence of an accurate, rapid, and low-cost diagnostic tool. A new approach for the screening of patients for tuberculosis is the use of rapid diagnostic classification algorithms. Methods: We tested a previously published diagnostic algorithm based on four biomarkers as a screening tool for tuberculosis in a Central European patient population using an assessor-blinded cross-sectional study design. In addition, we developed an improved diagnostic classification algorithm based on a study population at a tertiary hospital in Vienna, Austria, by supervised computational statistics. Results: The diagnostic accuracy of the previously published diagnostic algorithm for our patient population consisting of 206 patients was 54% (CI: 47%-61%). An improved model was constructed using inflammation parameters and clinical information. A diagnostic accuracy of 86% (CI: 80%-90%) was demonstrated by 10-fold cross validation. An alternative model relying solely on clinical parameters exhibited a diagnostic accuracy of 85% (CI: 79%-89%). Conclusion: Here we show that a rapid diagnostic algorithm based on clinical parameters is only slightly improved by inclusion of inflammation markers in our cohort. Our results also emphasize the need for validation of new diagnostic algorithms in different settings and patient populations. © 2012 Ratzinger et al.
Authors & Co-Authors
Ratzinger, Franz
Austria, Vienna
Medizinische Universität Wien
Fernandez-Reyes, Delmiro
United Kingdom, London
Mrc National Institute for Medical Research
Lagler, Heimo
Austria, Vienna
Medizinische Universität Wien
Graninger, Wolfgang
Austria, Vienna
Medizinische Universität Wien
Winkler, Stefan
Austria, Vienna
Medizinische Universität Wien
Krishna, Prof Sanjeev
United Kingdom, London
St George’s, University of London
Ramharter, Michael
Austria, Vienna
Medizinische Universität Wien
Germany, Tubingen
Eberhard Karls Universität Tübingen
Statistics
Citations: 8
Authors: 7
Affiliations: 7
Identifiers
Doi:
10.1371/journal.pone.0049658
ISSN:
19326203
Research Areas
Health System And Policy
Study Design
Cross Sectional Study
Cohort Study
Study Approach
Quantitative