Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA.
Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA.
J Biomed Inform. 2021 May;117:103756. doi: 10.1016/j.jbi.2021.103756. Epub 2021 Mar 22.
Clinicians order laboratory tests in an effort to reduce diagnostic or therapeutic uncertainty. Information theory provides the opportunity to quantify the degree to which a test result is expected to reduce diagnostic uncertainty. We sought to apply information theory toward the evaluation and optimization of a diagnostic test threshold and to determine if the results would differ from those of conventional methodologies. We used a heparin/PF4 immunoassay (PF4 ELISA) as a case study.
The laboratory database was queried for PF4 ELISA and serotonin release assay (SRA) results during the study period, with the latter serving as the gold standard for the disease heparin-induced thrombocytopenia (HIT). The optimized diagnostic threshold of the PF4 ELISA test was compared using conventional versus information theoretic approaches under idealized (pretest probability = 50%) and realistic (pretest probability = 2.4%) testing conditions.
Under ideal testing conditions, both analyses yielded a similar optimized optical density (OD) threshold of OD > 0.79. Under realistic testing conditions, information theory suggested a higher threshold, OD > 1.5 versus OD > 0.6. Increasing the diagnostic threshold improved the global information value, the value of a positive test and the noise content with only a minute change in the negative test value.
Our information theoretic approach suggested that the current FDA approved cutoff (OD > 0.4) is overly permissive leading to loss of test value and injection of noise into an already complex diagnostic dilemma. Because our approach is purely statistical and takes as input data that are readily accessible in the clinical laboratory it offers a scalable and data-driven strategy for optimizing test value that may be widely applicable in the domain of laboratory medicine.
Information theory provides more meaningful measures of test value than the widely used accuracy-based metrics.
临床医生开具实验室检查，旨在降低诊断或治疗的不确定性。信息论提供了量化测试结果降低诊断不确定性程度的机会。我们试图将信息论应用于评估和优化诊断测试阈值，并确定其结果是否与传统方法学有所不同。我们使用肝素/PF4 免疫测定（PF4 ELISA）作为案例研究。
在研究期间，查询了实验室数据库中的 PF4 ELISA 和血清素释放测定（SRA）结果，后者作为疾病肝素诱导的血小板减少症（HIT）的金标准。使用理想（预测试概率= 50%）和现实（预测试概率= 2.4%）检测条件下的传统和信息理论方法比较 PF4 ELISA 测试的优化诊断阈值。
在理想的检测条件下，两种分析都得出了相似的优化光密度（OD）阈值，即 OD>0.79。在现实的检测条件下，信息论建议采用更高的阈值，即 OD>1.5 而非 OD>0.6。提高诊断阈值可改善全局信息值、阳性测试值和噪声含量，而仅对阴性测试值产生微小变化。
我们的信息理论方法表明，当前 FDA 批准的截止值（OD>0.4）过于宽松，导致测试价值损失，并将噪声注入到已经复杂的诊断难题中。由于我们的方法纯粹是基于统计学的，并且将易于在临床实验室中获取的数据作为输入，因此它提供了一种可扩展且基于数据的优化测试价值的策略，可能在实验室医学领域具有广泛的适用性。
信息论提供了比广泛使用的基于准确性的指标更有意义的测试价值衡量标准。
