Moyo Sikhulile, LeCuyer Tessa, Wang Rui, Gaseitsiwe Simani, Weng Jia, Musonda Rosemary, Bussmann Hermann, Mine Madisa, Engelbrecht Susan, Makhema Joseph, Marlink Richard, Baum Marianna K, Novitsky Vladimir, Essex M
1 Botswana-Harvard AIDS Institute Partnership , Gaborone, Botswana .
AIDS Res Hum Retroviruses. 2014 Jan;30(1):29-36. doi: 10.1089/aid.2013.0055. Epub 2013 Sep 6.
Laboratory cross-sectional assays are useful for the estimation of HIV incidence, but are known to misclassify individuals with long-standing infection as recently infected. The false recent rate (FRR) varies widely across geographic areas; therefore, accurate estimates of HIV incidence require a locally defined FRR. We determined FRR for Botswana, where HIV-1 subtype C infection is predominant, using the BED capture enzyme immunoassay (BED), a Bio-Rad Avidity Index (BAI) assay (a modification of the Bio-Rad HIV1/2+O EIA), and two multiassay algorithms (MAA) that included clinical data. To estimate FRR, stored blood samples from 512 antiretroviral (ARV)-naive HIV-1 subtype C-infected individuals from a prospective cohort in Botswana were tested at 18-24 months postenrollment. The following FRR mean (95% CI) values were obtained: BED 6.05% (4.15-8.48), BAI 5.57% (3.70-8.0), BED-BAI 2.25% (1.13-4.0), and a combination of BED-BAI with CD4 (>200) and viral load (>400) threshold 1.43% (0.58-2.93). The interassay agreement between BED and BAI was 92.8% (95% CI, 90.1-94.5) for recent/long-term classification. Misclassification was associated with viral suppression for BED [adjusted OR (aOR) 10.31; p=0.008], BAI [aOR 9.72; p=0.019], and MAA1 [aOR 16.6; p=0.006]. Employing MAA can reduce FRR to <2%. A local FRR can improve cross-sectional HIV incidence estimates.
实验室横断面检测对于估计艾滋病毒发病率很有用，但已知会将长期感染的个体误分类为近期感染。假近期感染率（FRR）在不同地理区域差异很大；因此，准确估计艾滋病毒发病率需要一个本地定义的FRR。我们使用BED捕获酶免疫测定法（BED）、伯乐亲和力指数（BAI）测定法（伯乐HIV1/2+O酶免疫测定法的改良版）以及两种包含临床数据的多检测算法（MAA），确定了以HIV-1 C亚型感染为主的博茨瓦纳的FRR。为了估计FRR，对来自博茨瓦纳一个前瞻性队列的512名未接受抗逆转录病毒治疗（ARV）的HIV-1 C亚型感染个体在入组后18至24个月时的储存血样进行检测。获得了以下FRR均值（95%置信区间）：BED为6.05%（4.15 - 8.48），BAI为5.57%（3.70 - 8.0），BED - BAI为2.25%（1.13 - 4.0），以及BED - BAI与CD4（>200）和病毒载量（>400）阈值组合为1.43%（0.58 - 2.93）。在近期/长期分类方面，BED和BAI之间的检测间一致性为92.8%（95%置信区间，90.1 - 94.5）。误分类与BED [调整后比值比（aOR）10.31；p = 0.008]、BAI [aOR 9.72；p = 0.019]和MAA1 [aOR 16.6；p = 0.006]的病毒抑制相关。采用MAA可将FRR降低至<2%。本地FRR可改善横断面艾滋病毒发病率估计。
