Department of Laboratory Medicine, Clinique St-Luc Bouge, Namur, Belgium.
Department of Laboratory Medicine, University-Hospital of Padova, Padova, Italy.
Clin Chem Lab Med. 2022 Mar 18;60(6):808-820. doi: 10.1515/cclm-2021-1288. Print 2022 May 25.
Immunoassays are currently the methods of choice for the measurement of a large panel of complex and heterogenous molecules owing to full automation, short turnaround time, high specificity and sensitivity. Despite remarkable performances, immunoassays are prone to several types of interferences that may lead to harmful consequences for the patient (e.g., prescription of an inadequate treatment, delayed diagnosis, unnecessary invasive investigations). A systematic search is only performed for some interferences because of its impracticality in clinical laboratories as it would notably impact budget, turnaround time, and human resources. Therefore, a case-by-case approach is generally preferred when facing an aberrant result. Hereby, we review the current knowledge on immunoassay interferences and present an algorithm for interference workup in clinical laboratories, from suspecting their presence to using the appropriate tests to identify them. We propose an approach to rationalize the attitude of laboratory specialists when faced with a potential interference and emphasize the importance of their collaboration with clinicians and manufacturers to ensure future improvements.
免疫分析因其自动化程度高、周转时间短、特异性和灵敏度高,目前是测量大量复杂和异质分子的首选方法。尽管免疫分析性能卓越,但也容易受到多种类型的干扰,这可能会给患者带来有害的后果(例如,开出不适当的治疗方案、延误诊断、不必要的侵入性检查)。由于其在临床实验室中的不切实际性(因为这会显著影响预算、周转时间和人力资源),因此仅对某些干扰进行系统搜索。因此,当遇到异常结果时,通常会选择逐个案例的方法。在此,我们综述了免疫分析干扰的最新知识,并提出了临床实验室中干扰检测的算法,从怀疑干扰的存在到使用适当的检测方法来识别干扰。我们提出了一种方法来使实验室专家在面对潜在干扰时的态度合理化,并强调了他们与临床医生和制造商合作的重要性,以确保未来的改进。