Background

Fourth-generation immunoassays (such as the ADVIA Centaur® HIV Ag/Ab Combo (CHIV) assay) have improved the early diagnosis of human immunodeficiency virus (HIV), and their sensitivity and specificity usually exceed 99%. In regions with a low prevalence of HIV infection, however, the regular occurrence of false positives interferes with a medical laboratory’s workflow. The additional reagent and staff costs associated with false positives can nevertheless be avoided or reduced by gaining a better knowledge of the CHIV assay’s performance.

Objectives/study design

To improve our HIV diagnosis strategy, we retrospectively analyzed all the Centaur® CHIV assays and confirmatory tests performed at Amiens University Medical Center between 2012 and 2018. We used open-source machine learning software to process this large database, develop a predictive model, and identify a new cut-off for Centaur® CHIV index interpretation.

Results

A total of 56,682 HIV serological assay results were analyzed. The results of the CHIV assay were initially reactive or indeterminate for 449 samples. After p24 antigen and/or immunoblotting, there were 171 (38%) false positives and 278 (62%) confirmed true positives. The application of a cut-off of 2.12 led to reclassification of 130 of the 171 false positives as true negatives. Combining our predictive model with medical record analysis reduced the number of false positive CHIV assay results from 171 to 12.

Conclusions

The efficiency of the Centaur® CHIV assay can be increased by adjusting its cut-off for positivity. This adjustment may reduce the number of unnecessary confirmatory tests and accelerate the delivery of HIV test results.

中文翻译：

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分析性能评估和ADVIACentaur®HIV Ag / Ab组合检测的增强。

背景

第四代免疫测定法（例如ADVIACentaur®HIV Ag / Ab Combo（CHIV）测定法）改善了人类免疫缺陷病毒（HIV）的早期诊断，其敏感性和特异性通常超过99％。但是，在艾滋病毒感染率较低的地区，假阳性的经常发生会干扰医学实验室的工作流程。但是，通过更好地了解CHIV测定的性能，可以避免或减少与假阳性相关的额外试剂和人员成本。

目标/研究设计

为了改善我们的HIV诊断策略，我们回顾性分析了2012年至2018年间在亚眠大学医学中心进行的所有Centaur®CHIV检测方法和确证性测试。我们使用开源机器学习软件处理了这个大型数据库，开发了预测模型，并确定Centaur®CHIV指数解释的新界限。

结果

总共分析了56,682份HIV血清学检测结果。对于449个样品，CHIV分析的结果最初是反应性的或不确定的。在进行p24抗原和/或免疫印迹后，有171个（38％）假阳性和278个（62％）确认为真阳性。使用截止值2.12导致将171个假阳性中的130个重新分类为真阴性。将我们的预测模型与病历分析相结合，可使CHIV假阳性检测结果的数量从171减少到12。

结论

可以通过调整阳性阈值来提高Centaur®CHIV检测的效率。这项调整可以减少不必要的确认测试的数量，并加快HIV测试结果的交付速度。