Cousins Matthew M, Konikoff Jacob, Sabin Devin, Khaki Leila, Longosz Andrew F, Laeyendecker Oliver, Celum Connie, Buchbinder Susan P, Seage George R, Kirk Gregory D, Moore Richard D, Mehta Shruti H, Margolick Joseph B, Brown Joelle, Mayer Kenneth H, Kobin Beryl A, Wheeler Darrell, Justman Jessica E, Hodder Sally L, Quinn Thomas C, Brookmeyer Ron, Eshleman Susan H
Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
Department of Biostatistics, School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America.
PLoS One. 2014 Jun 26;9(6):e101043. doi: 10.1371/journal.pone.0101043. eCollection 2014.
Multi-assay algorithms (MAAs) can be used to estimate HIV incidence in cross-sectional surveys. We compared the performance of two MAAs that use HIV diversity as one of four biomarkers for analysis of HIV incidence.
Both MAAs included two serologic assays (LAg-Avidity assay and BioRad-Avidity assay), HIV viral load, and an HIV diversity assay. HIV diversity was quantified using either a high resolution melting (HRM) diversity assay that does not require HIV sequencing (HRM score for a 239 base pair env region) or sequence ambiguity (the percentage of ambiguous bases in a 1,302 base pair pol region). Samples were classified as MAA positive (likely from individuals with recent HIV infection) if they met the criteria for all of the assays in the MAA. The following performance characteristics were assessed: (1) the proportion of samples classified as MAA positive as a function of duration of infection, (2) the mean window period, (3) the shadow (the time period before sample collection that is being assessed by the MAA), and (4) the accuracy of cross-sectional incidence estimates for three cohort studies.
The proportion of samples classified as MAA positive as a function of duration of infection was nearly identical for the two MAAs. The mean window period was 141 days for the HRM-based MAA and 131 days for the sequence ambiguity-based MAA. The shadows for both MAAs were <1 year. Both MAAs provided cross-sectional HIV incidence estimates that were very similar to longitudinal incidence estimates based on HIV seroconversion.
MAAs that include the LAg-Avidity assay, the BioRad-Avidity assay, HIV viral load, and HIV diversity can provide accurate HIV incidence estimates. Sequence ambiguity measures obtained using a commercially-available HIV genotyping system can be used as an alternative to HRM scores in MAAs for cross-sectional HIV incidence estimation.
多检测算法（MAA）可用于在横断面调查中估计HIV发病率。我们比较了两种将HIV多样性作为分析HIV发病率的四种生物标志物之一的MAA的性能。
两种MAA均包括两种血清学检测（LAg亲和力检测和BioRad亲和力检测）、HIV病毒载量检测以及HIV多样性检测。使用无需HIV测序的高分辨率熔解（HRM）多样性检测（239个碱基对env区域的HRM评分）或序列歧义性（1302个碱基对pol区域中歧义碱基的百分比）对HIV多样性进行量化。如果样本符合MAA中所有检测的标准，则分类为MAA阳性（可能来自近期感染HIV的个体）。评估了以下性能特征：（1）作为感染持续时间函数的分类为MAA阳性的样本比例，（2）平均窗口期，（3）阴影期（MAA正在评估的样本采集前的时间段），以及（4）三项队列研究的横断面发病率估计的准确性。
两种MAA中，作为感染持续时间函数的分类为MAA阳性的样本比例几乎相同。基于HRM的MAA的平均窗口期为141天，基于序列歧义性的MAA的平均窗口期为131天。两种MAA的阴影期均<1年。两种MAA提供的横断面HIV发病率估计与基于HIV血清转化的纵向发病率估计非常相似。
包括LAg亲和力检测、BioRad亲和力检测、HIV病毒载量检测和HIV多样性检测的MAA可提供准确的HIV发病率估计。使用市售HIV基因分型系统获得的序列歧义性测量结果可作为MAA中HRM评分的替代方法，用于横断面HIV发病率估计。
