Division of Infectious Diseases and Tropical Medicine, Department of Medicine I, Medical University Vienna, Vienna, Austria.
PLoS One. 2012;7(11):e49658. doi: 10.1371/journal.pone.0049658. Epub 2012 Nov 21.
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.
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.
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%).
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.
有效治疗和控制结核病的主要障碍是缺乏准确、快速且低成本的诊断工具。一种新的结核病患者筛查方法是使用快速诊断分类算法。
我们使用评估者盲法的横断面研究设计，在中欧患者人群中测试了一种先前发表的基于四个生物标志物的诊断算法作为结核病筛查工具。此外，我们还通过监督计算统计学，基于奥地利维也纳的一家三级医院的研究人群，开发了一种改进的诊断分类算法。
由 206 名患者组成的患者人群中，先前发表的诊断算法的诊断准确性为 54%（CI：47%-61%）。通过 10 倍交叉验证，构建了一个使用炎症参数和临床信息的改进模型。该模型的诊断准确性为 86%（CI：80%-90%）。仅依赖临床参数的替代模型的诊断准确性为 85%（CI：79%-89%）。
在这里，我们表明，在我们的队列中，基于临床参数的快速诊断算法通过纳入炎症标志物仅略有改善。我们的研究结果还强调了在不同环境和患者人群中验证新诊断算法的必要性。
