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基于评估线免疫测定结果模式算法的窗口期,对报告队列中 HIV 新发感染率进行简单估计。

Simple estimation of incident HIV infection rates in notification cohorts based on window periods of algorithms for evaluation of line-immunoassay result patterns.

机构信息

University of Zurich, Institute of Medical Virology, Swiss National Center for Retroviruses, Zurich, Switzerland.

出版信息

PLoS One. 2013 Aug 26;8(8):e71662. doi: 10.1371/journal.pone.0071662. eCollection 2013.

Abstract

BACKGROUND

Tests for recent infections (TRIs) are important for HIV surveillance. We have shown that a patient's antibody pattern in a confirmatory line immunoassay (Inno-Lia) also yields information on time since infection. We have published algorithms which, with a certain sensitivity and specificity, distinguish between incident (< = 12 months) and older infection. In order to use these algorithms like other TRIs, i.e., based on their windows, we now determined their window periods.

METHODS

We classified Inno-Lia results of 527 treatment-naïve patients with HIV-1 infection < = 12 months according to incidence by 25 algorithms. The time after which all infections were ruled older, i.e. the algorithm's window, was determined by linear regression of the proportion ruled incident in dependence of time since infection. Window-based incident infection rates (IIR) were determined utilizing the relationship 'Prevalence = Incidence x Duration' in four annual cohorts of HIV-1 notifications. Results were compared to performance-based IIR also derived from Inno-Lia results, but utilizing the relationship 'incident = true incident + false incident' and also to the IIR derived from the BED incidence assay.

RESULTS

Window periods varied between 45.8 and 130.1 days and correlated well with the algorithms' diagnostic sensitivity (R(2) = 0.962; P<0.0001). Among the 25 algorithms, the mean window-based IIR among the 748 notifications of 2005/06 was 0.457 compared to 0.453 obtained for performance-based IIR with a model not correcting for selection bias. Evaluation of BED results using a window of 153 days yielded an IIR of 0.669. Window-based IIR and performance-based IIR increased by 22.4% and respectively 30.6% in 2008, while 2009 and 2010 showed a return to baseline for both methods.

CONCLUSIONS

IIR estimations by window- and performance-based evaluations of Inno-Lia algorithm results were similar and can be used together to assess IIR changes between annual HIV notification cohorts.

摘要

背景

最近感染的检测(TRIs)对于 HIV 监测非常重要。我们已经表明,患者在确认性线状免疫测定(Inno-Lia)中的抗体模式也提供了有关感染时间的信息。我们已经发布了一些算法,这些算法在一定的灵敏度和特异性下,可以区分新发感染(<=12 个月)和旧感染。为了像其他 TRI 一样使用这些算法,即基于它们的窗口期,我们现在确定了它们的窗口期。

方法

我们根据 25 种算法,将 527 名治疗初治的 HIV-1 感染患者的 Inno-Lia 结果按发病时间进行分类。通过感染后时间与感染时间的线性回归,确定所有感染均被归类为旧感染的时间,即算法的窗口期。基于窗口的新发感染率(IIR)是利用 HIV-1 通报四个年度队列中的“患病率=发病率×持续时间”关系确定的。结果与从 Inno-Lia 结果中推导出来的基于性能的 IIR 进行了比较,该结果利用“新发感染=真实新发感染+假新发感染”的关系,也与从 BED 发病测定法中推导出来的 IIR 进行了比较。

结果

窗口期在 45.8 至 130.1 天之间变化,与算法的诊断灵敏度相关性良好(R²=0.962;P<0.0001)。在 25 种算法中,2005/06 年的 748 例通报中,基于窗口的平均 IIR 为 0.457,而不纠正选择偏差的模型中基于性能的 IIR 为 0.453。使用 153 天的窗口评估 BED 结果,得出的 IIR 为 0.669。2008 年,基于窗口和基于性能的 IIR 分别增加了 22.4%和 30.6%,而 2009 年和 2010 年两种方法均恢复到基线。

结论

基于窗口和基于性能的 Inno-Lia 算法结果评估的 IIR 估计值相似,可以一起用于评估年度 HIV 通报队列之间的 IIR 变化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5042/3753319/43e35e03ba71/pone.0071662.g001.jpg

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