Laboratori de Referència Camp de Tarragona i Terres de l'Ebre, Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, Reus, Spain.
Laboratori de Referència de Catalunya SA, El Prat de Llobregat, Barcelona, Spain.
Ann Clin Biochem. 2021 Nov;58(6):614-621. doi: 10.1177/00045632211038038. Epub 2021 Aug 11.
Anti-SARS-CoV-2 antibodies have been used in the study of the immune response in infected patients. However, differences in sensitivity and specificity have been reported, depending on the method of analysis. The aim of the present study was to evaluate the diagnostic accuracy of an algorithm in which a high-throughput automated assay for total antibodies was used for screening and two semi-automated IgG-specific methods were used to confirm the results, and also to correlate the analytical results with the clinical data and the time elapsed since infection.
We studied 306 patients, some hospitalized and some outpatients, belonging to a population with a high prevalence of COVID-19. One-hundred and ten patients were classified as SARS-CoV-2 negative and 196 as positive by polymerase chain reaction.
The algorithm and automated assay alone had a specificity and a positive predictive value of 100%, although the sensitivity and negative predictive value of the algorithm was higher. Both methods showed a good sensitivity from day 11 of the onset of symptoms in asymptomatic and symptomatic patients. The absorbance of the total antibodies was significantly higher in severely symptomatic than in asymptomatic or mildly symptomatic patients, which suggests the antibody level was higher. We found 15 patients who did not present seroconversion at 12 days from the onset of symptoms or the first polymerase chain reaction test.
This study highlights the proper functioning of algorithms in the diagnosis of the immune response to COVID-19, which can help to define testing strategies against this disease.
抗 SARS-CoV-2 抗体已被用于研究感染患者的免疫反应。然而,据报道,分析方法的不同会导致敏感性和特异性的差异。本研究旨在评估一种算法的诊断准确性,该算法使用高通量自动化总抗体检测进行筛查,并用两种半自动化 IgG 特异性方法进行确认,并将分析结果与临床数据和感染后时间相关联。
我们研究了 306 名患者,其中一些是住院患者,一些是门诊患者,他们来自 COVID-19 高流行地区。110 名患者经聚合酶链反应(PCR)检测被归类为 SARS-CoV-2 阴性,196 名患者被归类为阳性。
算法和自动化检测单独使用的特异性和阳性预测值均为 100%,尽管算法的敏感性和阴性预测值更高。这两种方法在无症状和有症状患者出现症状的第 11 天开始均显示出良好的敏感性。在症状严重的患者中,总抗体的吸光度明显高于无症状或症状轻微的患者,这表明抗体水平更高。我们发现有 15 名患者在出现症状或第一次 PCR 检测后 12 天没有出现血清转化。
本研究强调了算法在诊断 COVID-19 免疫反应中的正确作用,这有助于确定针对这种疾病的检测策略。