Adv Clin Chem. 2014;66:241-94. doi: 10.1016/b978-0-12-801401-1.00007-4.
The primary role of the clinical laboratory is to report accurate results for diagnosis of disease and management of illnesses. This goal has, to a large extent been achieved for routine biochemical tests, but not for immunoassays which remained susceptible to interference from endogenous immunoglobulin antibodies, causing false, and clinically misleading results. Clinicians regard all abnormal results including false ones as "pathological" necessitating further investigations, or concluding iniquitous diagnosis. Even more seriously, "false-negative" results may wrongly exclude pathology, thus denying patients' necessary treatment. Analytical error rate in immunoassays is relatively high, ranging from 0.4% to 4.0%. Because analytical interference from endogenous antibodies is confined to individuals' sera, it can be inconspicuous, pernicious, sporadic, and insidious because it cannot be detected by internal or external quality assessment procedures. An approach based on Bayesian reasoning can enhance the robustness of clinical validation in highlighting potentially erroneous immunoassay results. When this rational clinical/statistical approach is followed by analytical affirmative follow-up tests, it can help identifying inaccurate and clinically misleading immunoassay data even when they appear plausible and "not-unreasonable." This chapter is largely based on peer reviewed articles associated with and related to this approach. The first section underlines (without mathematical equations) the dominance and misuse of conventional statistics and the underuse of Bayesian paradigm and shows that laboratorians are intuitively (albeit unwittingly) practicing Bayesians. Secondly, because interference from endogenous antibodies is method's dependent (with numerous formats and different reagents), it is almost impossible to accurately assess its incidence in all differently formulated immunoassays and for each analytes/biomarkers. However, reiterating the basic concepts underpinning interference from endogenous antibodies can highlight why interference will remain analytically pernicious, sporadic, and an inveterate problem. The following section discuses various stratagems to reduce this source of inaccuracy in current immunoassay results including the role of Bayesian reasoning. Finally, the role of three commonly used follow-up affirmative tests and their interpretation in confirming analytical interference is discussed.
临床实验室的主要作用是报告准确的结果，以诊断疾病和管理疾病。在很大程度上，这一目标已经实现了常规生化检测，但免疫测定仍容易受到内源性免疫球蛋白抗体的干扰，导致错误的、临床上误导性的结果。临床医生将所有异常结果，包括假阳性结果，都视为“病理性”，需要进一步检查，或得出错误的诊断。更严重的是，“假阴性”结果可能错误地排除了病理，从而剥夺了患者必要的治疗。免疫测定的分析误差率相对较高，范围在 0.4%至 4.0%之间。由于内源性抗体的分析干扰仅限于个体的血清，因此它可能是不显眼的、有害的、零星的和隐伏的，因为它不能通过内部或外部质量评估程序检测到。基于贝叶斯推理的方法可以提高临床验证的稳健性，突出潜在错误的免疫测定结果。当遵循这种合理的临床/统计方法并进行分析性阳性后续测试时，即使当免疫测定数据看似合理且“并非不合理”时，它也可以帮助识别不准确和临床上误导性的免疫测定数据。本章主要基于与该方法相关和相关的同行评审文章。第一节在没有数学方程的情况下强调了传统统计学的主导地位和误用，以及贝叶斯范式的使用不足，并表明实验室人员正在直观地（尽管是无意识地）实践贝叶斯方法。其次，由于内源性抗体的干扰是方法依赖性的（具有多种格式和不同的试剂），因此几乎不可能准确评估其在所有不同配方的免疫测定中和每个分析物/生物标志物中的发生率。然而，反复强调内源性抗体干扰的基本概念可以突出说明干扰为何将继续在分析上具有危害性、零星发生且是一个根深蒂固的问题。接下来的一节讨论了各种策略，以减少当前免疫测定结果中的这种不准确性，包括贝叶斯推理的作用。最后，讨论了三种常用的后续阳性测试及其在确认分析干扰方面的解释。
