University of Zurich, Institute of Medical Virology, Swiss National Center for Retroviruses, Zurich, Switzerland.
BMC Infect Dis. 2011 Sep 26;11:254. doi: 10.1186/1471-2334-11-254.
Serologic testing algorithms for recent HIV seroconversion (STARHS) provide important information for HIV surveillance. We have shown that a patient's antibody reaction in a confirmatory line immunoassay (INNO-LIA HIV I/II Score, Innogenetics) provides information on the duration of infection. Here, we sought to further investigate the diagnostic specificity of various Inno-Lia algorithms and to identify factors affecting it.
Plasma samples of 714 selected patients of the Swiss HIV Cohort Study infected for longer than 12 months and representing all viral clades and stages of chronic HIV-1 infection were tested blindly by Inno-Lia and classified as either incident (up to 12 m) or older infection by 24 different algorithms. Of the total, 524 patients received HAART, 308 had HIV-1 RNA below 50 copies/mL, and 620 were infected by a HIV-1 non-B clade. Using logistic regression analysis we evaluated factors that might affect the specificity of these algorithms.
HIV-1 RNA < 50 copies/mL was associated with significantly lower reactivity to all five HIV-1 antigens of the Inno-Lia and impaired specificity of most algorithms. Among 412 patients either untreated or with HIV-1 RNA ≥ 50 copies/mL despite HAART, the median specificity of the algorithms was 96.5% (range 92.0-100%). The only factor that significantly promoted false-incident results in this group was age, with false-incident results increasing by a few percent per additional year. HIV-1 clade, HIV-1 RNA, CD4 percentage, sex, disease stage, and testing modalities exhibited no significance. Results were similar among 190 untreated patients.
The specificity of most Inno-Lia algorithms was high and not affected by HIV-1 variability, advanced disease and other factors promoting false-recent results in other STARHS. Specificity should be good in any group of untreated HIV-1 patients.
近期 HIV 血清转化的血清学检测算法（STARHS）为 HIV 监测提供了重要信息。我们已经表明，患者在确认性线免疫分析（INNO-LIA HIV I/II Score，Innogenetics）中的抗体反应提供了关于感染持续时间的信息。在这里，我们试图进一步研究各种 Inno-Lia 算法的诊断特异性，并确定影响其特异性的因素。
对来自瑞士 HIV 队列研究的 714 名选择的患者进行了检测，这些患者的感染时间超过 12 个月，代表了慢性 HIV-1 感染的所有病毒谱系和阶段。这些患者的血浆样本通过 Inno-Lia 进行了盲测，并根据 24 种不同的算法将其分类为近期感染（不超过 12 个月）或既往感染。其中，524 名患者接受了 HAART，308 名患者的 HIV-1 RNA 低于 50 拷贝/mL，620 名患者感染了 HIV-1 非 B 谱系。使用逻辑回归分析，我们评估了可能影响这些算法特异性的因素。
HIV-1 RNA < 50 拷贝/mL 与 Inno-Lia 的所有五个 HIV-1 抗原的反应性显著降低有关，并损害了大多数算法的特异性。在 412 名未接受治疗或尽管接受了 HAART 但 HIV-1 RNA ≥ 50 拷贝/mL 的患者中，算法的中位数特异性为 96.5%（范围为 92.0-100%）。唯一显著增加该组假近期结果的因素是年龄，每增加一年，假近期结果就会增加几个百分点。HIV-1 谱系、HIV-1 RNA、CD4 百分比、性别、疾病阶段和检测方式均无显著意义。在 190 名未接受治疗的患者中，结果相似。
大多数 Inno-Lia 算法的特异性较高，不受 HIV-1 变异性、晚期疾病和其他因素的影响，这些因素会导致其他 STARHS 中的假近期结果。在任何一组未经治疗的 HIV-1 患者中，特异性都应该很好。
