Ismail Adel Aa
Wakefield, UK
Clin Med (Lond). 2017 Jul;17(4):329-332. doi: 10.7861/clinmedicine.17-4-329.
A laboratory test has three phases, pre-analytical, analytical and post-analytical. The purpose of this review is to highlight an issue concerning the analytical phase of one of the most widely deployed groups of diagnostic tests using a common technology - namely immunoassay.Immunoassay entails an inherently high error rate and, therefore, has the potential for inaccurate and misleading results susceptible to misinterpretation and/or diagnostic misapplication by clinicians. An approach based on Bayesian inference (without mathematics or equations) - illustrated by examples - is presented; this may help clinicians in discerning potentially erroneous results even when they appear plausible and not unreasonable.Essentially, false positive results are most likely to occur when the disease prevalence/incidence is low. False negative results become more prominent when the prevalence/incidence of disease increases. When concern is raised, available follow-up laboratory tests should be initiated to establish with confidence the diagnostic reliability or unreliability of such results.
实验室检测有三个阶段,即分析前阶段、分析阶段和分析后阶段。本综述的目的是突出一个有关诊断检测中应用最广泛的一组检测方法(即免疫测定法)的分析阶段的问题。免疫测定法本身的错误率就很高,因此有可能产生不准确和有误导性的结果,容易被临床医生误解和/或诊断误用。本文介绍了一种基于贝叶斯推理的方法(无需数学或公式),并举例说明;这可能有助于临床医生辨别潜在的错误结果,即使这些结果看似合理且并非不合理。从本质上讲,当疾病患病率/发病率较低时,最容易出现假阳性结果。当疾病患病率/发病率增加时,假阴性结果会变得更加突出。当对此产生疑虑时,应启动可用的后续实验室检测,以确定此类结果在诊断上的可靠性或不可靠性。