Effectiveness of the algorithm for detecting cases of recent HIV-1 infection in the Russian Federation

Objective. Accurate identification of recent HIV-1 infection cases will ensure a more effective and precise assessment of the dynamics of virus transmission, the time between infection and diagnosis, and the quality of screening and prevention programs. This study was undertaken to adjust the recent HIV-1 infection testing algorithm using a cohort of patients, in whom the time since infection was known. Materials and methods. We used blood plasma samples obtained from 264 HIV-infected patients with a known date of infection. All samples were analyzed using two serological assays aimed to differentiate between cases of recent and established HIV infection. Using the results of sequencing of the pol region, we calculated the proportion of variable positions in order to determine the duration of infection. To identify the cases of recent HIV infection, we evaluated different variants of a diagnostic algorithm that included a combination of serological tests, molecular genetic analysis of the viral genome, and other clinical and laboratory parameters. Results. The effectiveness of the DS-ELISA-HIV-AB-TERM (DS) assay for the detection of recent infection was higher than that of the Architect HIV Ag/Ab Combo assay (Abbott). The sensitivity and specificity of the DS assay were 94.4% and 96.7%, respectively. The sensitivity and specificity of the Abbott assay were 86.4% and 77,4%, respectively. The HIV-1 genome variability threshold of 0.33% allowed the differentiation between samples depending on the time since infection with a cut-off of 12 months: 82.1% of recent samples and 62.7% of established samples were correctly identified using this method. We analyzed the effectiveness of schemes of the algorithm for the detection of recent infection lasting no longer than 9 months. Conclusion. Our findings allow us to recommend the algorithm based on the Russian DS assay for the detection of recent HIV-1 cases in routine clinical practice. This algorithm will enable the detection of new HIV cases, thereby improving the disease control. Key words: HIV-1, HIV infection, genetic variability, time since infection, duration of infection, recent infection, early infection, seroconversion