Independent Data and Pattern Scientist, Hoenderloo, 7351BD, The Netherlands.
Department for Digital Arts, University for Applied Arts Vienna, Vienna, 1030, Austria.
F1000Res. 2021 May 10;10:369. doi: 10.12688/f1000research.51061.3. eCollection 2021.
The performance of diagnostic tests crucially depends on the disease prevalence, test sensitivity, and test specificity. However, these quantities are often not well known when tests are performed outside defined routine lab procedures which make the rating of the test results somewhat problematic. A current example is the mass testing taking place within the context of the world-wide SARS-CoV-2 crisis. Here, for the first time in history, laboratory test results have a dramatic impact on political decisions. Therefore, transparent, comprehensible, and reliable data is mandatory. It is in the nature of wet lab tests that their quality and outcome are influenced by multiple factors reducing their performance by handling procedures, underlying test protocols, and analytical reagents. These limitations in sensitivity and specificity have to be taken into account when calculating the real test results. As a resolution method, we have developed a Bayesian calculator, the Bayes Lines Tool (BLT), for analyzing disease prevalence, test sensitivity, test specificity, and, therefore, true positive, false positive, true negative, and false negative numbers from official test outcome reports. The calculator performs a simple SQL (Structured Query Language) query and can easily be implemented on any system supporting SQL. We provide an example of influenza test results from California, USA, as well as two examples of SARS-CoV-2 test results from official government reports from The Netherlands and Germany-Bavaria, to illustrate the possible parameter space of prevalence, sensitivity, and specificity consistent with the observed data. Finally, we discuss this tool's multiple applications, including its putative importance for informing policy decisions.
诊断测试的性能主要取决于疾病的流行率、测试的灵敏度和特异性。然而，当测试在定义明确的常规实验室程序之外进行时，这些数量通常不为人知，这使得测试结果的评估有些问题。当前的一个例子是在全球 SARS-CoV-2 危机背景下进行的大规模测试。在这里，实验室测试结果首次对政治决策产生了巨大影响。因此，透明、可理解和可靠的数据是强制性的。湿实验室测试的性质是，其质量和结果受到多种因素的影响，这些因素会通过处理程序、基础测试协议和分析试剂降低其性能。在计算实际测试结果时，必须考虑到这些灵敏度和特异性的限制。作为一种解决方法，我们开发了一个贝叶斯计算器，即贝叶斯线工具 (BLT)，用于分析疾病流行率、测试灵敏度、测试特异性，从而从官方测试结果报告中计算出真正的阳性、假阳性、真正的阴性和假阴性数字。该计算器执行简单的 SQL（结构化查询语言）查询，并且可以轻松地在任何支持 SQL 的系统上实现。我们提供了来自美国加利福尼亚州的流感测试结果的示例，以及来自荷兰和德国巴伐利亚州的官方政府报告的 SARS-CoV-2 测试结果的两个示例，以说明与观察到的数据一致的流行率、灵敏度和特异性的可能参数空间。最后，我们讨论了该工具的多种应用，包括其对政策决策提供信息的潜在重要性。
