ArticlePloS one2012
Rapid diagnostic algorithms as a screening tool for tuberculosis: an assessor blinded cross-sectional study.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Trial
- A proof-of-concept study of iCope: A nurse-led psychoeducational telephone intervention for women attending a rapid diagnostic centre for breast abnormality.Canadian oncology nursing journal = Revue canadienne de nursing oncologique · 2023Article
- Article
- Idiosyncratic Drug-Induced Liver Injury (DILI) and Herb-Induced Liver Injury (HILI): Diagnostic Algorithm Based on the Quantitative Roussel Uclaf Causality Assessment Method (RUCAM).Diagnostics (Basel, Switzerland) · 2021Review
- Diagnostic 'omics' for active tuberculosis.BMC medicine · 2016Review
- A comparison of tools used for tuberculosis diagnosis in resource-limited settings: a case study at Mubende referral hospital, Uganda.PloS one · 2014Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
backgroundA 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.
methodsWe 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.
resultsThe 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%).
conclusionHere 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.
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