Laboratory of Immunoregulation, NIAID, NIH, Baltimore, MD, USA.
Division of Infectious Diseases, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
J Int AIDS Soc. 2018 Feb;21(2). doi: 10.1002/jia2.25082.
Cross-sectional methods can be used to estimate HIV incidence for surveillance and prevention studies. We evaluated assays and multi-assay algorithms (MAAs) for incidence estimation in subtype C settings.
We analysed samples from individuals with subtype C infection with known duration of infection (2442 samples from 278 adults; 0.1 to 9.9 years after seroconversion). MAAs included 1-4 of the following assays: Limiting Antigen Avidity assay (LAg-Avidity), BioRad-Avidity assay, CD4 cell count and viral load (VL). We evaluated 23,400 MAAs with different assays and assay cutoffs. We identified the MAA with the largest mean window period, where the upper 95% confidence interval (CI) of the shadow was <1 year. This MAA was compared to the LAg-Avidity and BioRad-Avidity assays alone, a widely used LAg algorithm (LAg-Avidity <1.5 OD-n + VL >1000 copies/mL), and two MAAs previously optimized for subtype B settings. We compared these cross-sectional incidence estimates to observed incidence in an independent longitudinal cohort.
The optimal MAA was LAg-Avidity <2.8 OD-n + BioRad-Avidity <95% + VL >400 copies/mL. This MAA had a mean window period of 248 days (95% CI: 218, 284), a shadow of 306 days (95% CI: 255, 359), and provided the most accurate and precise incidence estimate for the independent cohort. The widely used LAg algorithm had a shorter mean window period (142 days, 95% CI: 118, 167), a longer shadow (410 days, 95% CI; 318, 491), and a less accurate and precise incidence estimate for the independent cohort.
An optimal MAA was identified for cross-sectional HIV incidence in subtype C settings. The performance of this MAA is superior to a testing algorithm currently used for global HIV surveillance.
横断面研究方法可用于监测和预防研究中估计 HIV 发病率。我们评估了适用于 C 型亚型的估计发病率的检测方法和多检测算法（MAAs）。
我们分析了已知感染持续时间（278 名成年人中有 2442 名样本；从血清转换后 0.1 至 9.9 年）的 C 型亚型感染个体的样本。MAAs 包括以下 1-4 种检测方法：有限抗原亲和力检测（LAg-Avidity）、BioRad-Avidity 检测、CD4 细胞计数和病毒载量（VL）。我们评估了 23400 个具有不同检测方法和检测截止值的 MAAs。我们确定了 MAA 的最大平均窗口期，即上限 95%置信区间（CI）<1 年。该 MAA 与 LAg-Avidity 和 BioRad-Avidity 检测单独比较，与广泛使用的 LAg 算法（LAg-Avidity <1.5 OD-n + VL >1000 拷贝/ml）和两种针对 B 型亚型优化的 MAA 进行比较。我们将这些横断面发病率估计与独立纵向队列中的观察到的发病率进行了比较。
最佳 MAA 是 LAg-Avidity <2.8 OD-n + BioRad-Avidity <95% + VL >400 拷贝/ml。该 MAA 的平均窗口期为 248 天（95%CI：218，284），阴影期为 306 天（95%CI：255，359），为独立队列提供了最准确和最精确的发病率估计。广泛使用的 LAg 算法的平均窗口期较短（142 天，95%CI：118，167），阴影期较长（410 天，95%CI：318，491），对独立队列的发病率估计准确性和精确性较低。
在 C 型亚型环境中确定了最佳的 MAA 用于横断面 HIV 发病率估计。该 MAA 的性能优于当前用于全球 HIV 监测的检测算法。
