Global Virus Network, Middle East Region, Shiraz, Iran.
Research Center for Health Sciences, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
Biochem Med (Zagreb). 2022 Jun 15;32(2):020705. doi: 10.11613/BM.2022.020705.
Coronavirus disease 2019 (COVID-19) is known to induce robust antibody response in most of the affected individuals. The objective of the study was to determine if we can harvest the test sensitivity and specificity of a commercial serologic immunoassay merely based on the frequency distribution of the SARS-CoV-2 immunoglobulin (Ig) G concentrations measured in a population-based seroprevalence study.
The current study was conducted on a subset of a previously published dataset from the canton of Geneva. Data were taken from two non-consecutive weeks (774 samples from May 4-9, and 658 from June 1-6, 2020). Assuming that the frequency distribution of the measured SARS-CoV-2 IgG is binormal (an educated guess), using a non-linear regression, we decomposed the distribution into its two Gaussian components. Based on the obtained regression coefficients, we calculated the prevalence of SARS-CoV-2 infection, the sensitivity and specificity, and the most appropriate cut-off value for the test. The obtained results were compared with those obtained from a validity study and a seroprevalence population-based study.
The model could predict more than 90% of the variance observed in the SARS-CoV-2 IgG distribution. The results derived from our model were in good agreement with the results obtained from the seroprevalence and validity studies. Altogether 138 of 1432 people had SARS-CoV-2 IgG ≥ 0.90, the cut-off value which maximized the Youden's index. This translates into a true prevalence of 7.0% (95% confidence interval 5.4% to 8.6%), which is in keeping with the estimated prevalence of 7.7% derived from our model. Our model can provide the true prevalence.
Having an educated guess about the distribution of test results, the test performance indices can be derived with acceptable accuracy merely based on the test results frequency distribution without the need for conducting a validity study and comparing the test results against a gold-standard test.
已知 2019 年冠状病毒病（COVID-19）会在大多数受感染个体中引发强烈的抗体反应。本研究的目的是确定我们是否可以仅基于基于人群的血清阳性率研究中测量的 SARS-CoV-2 免疫球蛋白（IgG）浓度的频率分布来提高商业血清学免疫分析的检测灵敏度和特异性。
本研究基于先前在日内瓦州发表的数据集的一个子集进行。数据取自两个非连续的星期（2020 年 5 月 4 日至 9 日采集了 774 个样本，6 月 1 日至 6 日采集了 658 个样本）。假设所测量的 SARS-CoV-2 IgG 的频率分布为双正态（推测），我们使用非线性回归将分布分解为两个高斯分量。根据获得的回归系数，我们计算了 SARS-CoV-2 感染的患病率，检测的灵敏度和特异性以及最合适的检测截止值。将获得的结果与有效性研究和基于人群的血清阳性率研究的结果进行比较。
该模型可以预测 SARS-CoV-2 IgG 分布中观察到的超过 90％的方差。我们的模型得出的结果与血清阳性率和有效性研究的结果吻合良好。共有 1432 人中的 138 人的 SARS-CoV-2 IgG≥0.90，该值为最大化尤登指数的截止值。这转化为真实患病率为 7.0％（95％置信区间为 5.4％至 8.6％），与我们模型得出的估计患病率 7.7％相符。我们的模型可以提供真实的患病率。
根据对测试结果分布的推测，无需进行有效性研究并将测试结果与金标准测试进行比较，仅基于测试结果的频率分布，就可以得出具有可接受准确性的测试性能指标。
