根据测试结果频率分布确定 SARS-CoV-2 血清免疫测定测试性能指标。

IF 3.8 3区 医学 Q1 MEDICAL LABORATORY TECHNOLOGY
Farrokh Habibzadeh, Parham Habibzadeh, Mahboobeh Yadollahie, Mohammad M Sajadi
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引用次数: 1

摘要

导言:众所周知,2019 年冠状病毒病(COVID-19)会在大多数感染者体内诱发强大的抗体反应。本研究的目的是确定我们是否能仅仅根据基于人群的血清流行研究中测得的 SARS-CoV-2 免疫球蛋白 (Ig) G 浓度的频率分布来收获商业血清免疫测定的检测灵敏度和特异性:本研究是在日内瓦州以前公布的数据集的一个子集上进行的。数据来自两个非连续的星期(2020 年 5 月 4 日至 9 日采集了 774 份样本,6 月 1 日至 6 日采集了 658 份样本)。假设测得的 SARS-CoV-2 IgG 的频率分布是二正态分布(根据经验推测),我们使用非线性回归法将其分解为两个高斯成分。根据得到的回归系数,我们计算出了 SARS-CoV-2 感染率、灵敏度和特异性以及最合适的检测临界值。我们将所得结果与一项有效性研究和一项血清流行率人群研究的结果进行了比较:结果:该模型可预测 90% 以上的 SARS-CoV-2 IgG 分布变异。我们的模型得出的结果与血清流行率研究和有效性研究得出的结果非常吻合。在 1432 人中,共有 138 人的 SARS-CoV-2 IgG ≥ 0.90,这是尤登指数最大化的临界值。这意味着真实发病率为 7.0%(95% 置信区间为 5.4% 至 8.6%),与我们的模型得出的 7.7% 的估计发病率相符。我们的模型可以提供真实的患病率:结论:在对测试结果的分布有了一定的猜测后,只需根据测试结果的频率分布,就能得出准确度可接受的测试性能指标,而无需进行有效性研究,也无需将测试结果与金标准测试结果进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Determining the SARS-CoV-2 serological immunoassay test performance indices based on the test results frequency distribution.

Determining the SARS-CoV-2 serological immunoassay test performance indices based on the test results frequency distribution.

Determining the SARS-CoV-2 serological immunoassay test performance indices based on the test results frequency distribution.

Determining the SARS-CoV-2 serological immunoassay test performance indices based on the test results frequency distribution.

Introduction: 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.

Materials and methods: 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.

Results: 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.

Conclusions: 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.

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来源期刊
Biochemia Medica
Biochemia Medica 医学-医学实验技术
CiteScore
5.50
自引率
3.00%
发文量
70
审稿时长
>12 weeks
期刊介绍: Biochemia Medica is the official peer-reviewed journal of the Croatian Society of Medical Biochemistry and Laboratory Medicine. Journal provides a wide coverage of research in all aspects of clinical chemistry and laboratory medicine. Following categories fit into the scope of the Journal: general clinical chemistry, haematology and haemostasis, molecular diagnostics and endocrinology. Development, validation and verification of analytical techniques and methods applicable to clinical chemistry and laboratory medicine are welcome as well as studies dealing with laboratory organization, automation and quality control. Journal publishes on a regular basis educative preanalytical case reports (Preanalytical mysteries), articles dealing with applied biostatistics (Lessons in biostatistics) and research integrity (Research integrity corner).
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