Antibody responses vary widely between individuals1, complicating the correct classification of low-titer measurements using conventional assay cut-offs. We found all participants in a clinically diverse cohort of SARS-CoV-2 PCR+ individuals (n=105), and n=33 PCR+ hospital staff, to have detectable IgG specific for pre-fusion-stabilized spike (S) glycoprotein trimers, while 98% of persons had IgG specific for the receptor-binding domain (RBD). However, anti-viral IgG levels differed by several orders of magnitude between individuals and were associated with disease severity, with critically ill patients displaying the highest anti-viral antibody titers and strongest in vitro neutralizing responses. Parallel analysis of random healthy blood donors and pregnant women (n=1,000) of unknown serostatus, further demonstrated highly variable IgG titers amongst seroconverters, although these were generally lower than in hospitalized patients and included several measurements that scored between the classical 3 and 6SD assay cut-offs. Since the correct classification of seropositivity is critical for individual- and population-level metrics, we compared different probabilistic algorithms for their ability to assign likelihood of past infection. To do this, we used tandem anti-S and -RBD IgG responses from our PCR+ individuals (n=138) and a large cohort of historical negative controls (n=595) as training data, and generated an equal-weighted learner from the output of support vector machines and linear discriminant analysis. Applied to test samples, this approach provided a more quantitative way to interpret anti-viral titers over a large continuum, scrutinizing measurements overlapping the negative control background more closely and offering a probability-based diagnosis with potential clinical utility. Especially as most SARS-CoV-2 infections result in asymptomatic or mild disease, these platform-independent approaches improve individual and epidemiological estimates of seropositivity, critical for effective management of the pandemic and monitoring the response to vaccination.

中文翻译：

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分类高度可变的抗SARS-CoV-2抗体反应的概率方法

个体之间的抗体反应差异很大1，这使使用常规检测方法进行低滴度检测的正确分类变得复杂。我们发现SARS-CoV-2 PCR +患者（n = 105）和n = 33 PCR +医院工作人员的临床上各不相同的队列中的所有参与者均具有可检测到的针对融合前稳定的加标（S）糖蛋白三聚体的IgG，而98％的人具有针对受体结合域（RBD）的IgG。但是，个体之间的抗病毒IgG水平相差几个数量级，并且与疾病的严重程度有关，危重病人表现出最高的抗病毒抗体滴度和最强的体外中和反应。对随机健康献血者和未知血清状况的孕妇（n = 1,000）进行平行分析，进一步证明了血清转化器中的IgG滴度非常可变，尽管这些滴度通常低于住院患者，并且包括在经典3SD和6SD分析临界值之间得分的多项测量。由于正确的血清阳性阳性分类对于个人和人群水平的指标至关重要，因此我们比较了不同的概率算法分配过去感染可能性的能力。为此，我们使用来自PCR +个体（n = 138）和大量历史阴性对照（n = 595）的串联抗S和-RBD IgG反应作为训练数据，并从支持向量机的输出和线性判别分析。这种方法适用于测试样品，提供了一种更定量的方法来解释大范围连续体中的抗病毒效价，仔细检查与阴性对照背景重叠的测量值，并提供具有潜在临床实用性的基于概率的诊断。特别是当大多数SARS-CoV-2感染导致无症状或轻度疾病时，这些与平台无关的方法改善了血清阳性的个体和流行病学估计，这对于有效管理大流行和监测疫苗接种反应至关重要。