Abstract

Background: HIV prevention with a significant reduction of new infections is important for epidemic control strategies. Antibody-based tests for recent infection (TRI) that can help distinguish recent from long-term HIV infection have been used since the mid-1990s to estimate population-level HIV incidence. Because the tests are done centrally, test results are not available to providers immediately. Point of care rapid testing for recent HIV infection (RTRI) was used for distinguishing recent from long-term status and estimated HIV incidence for preventing HIV infection. The usefulness of RTRI for recent HIV infection detection can be accelerated prevention of HIV infection transmission. Objectives: To demonstrate RTRI assay utility to distinguish recent from long-term HIV infection among high-risk individuals. Materials and methods: Between September and October 2022, HIV seropositive plasma samples of a high-risk group (N=90) and HIV-negative group (N=90) were collected from the outpatient department of Khon Kaen Hospital. All Specimens were tested by AsanteTM Rapid Recency Assay (ARRA) and classified as negative, recent and long-term infection based on the presence or absence of specific lines (control line [CL], Positive verification line [PVL] and Long-term line ([TL]) by visual reading. Results: Among 90 HIV seropositive plasma samples, 14.4% (13/90) were recent HIV infection and 84.4% (76/90) were long-term HIV infection. There were no significant differences between recent and long-term infections among men who have sex with men (MSM), transgender women (TG), heterosexuals and sex workers. Most of recently infected HIV patients were the MSM/TG group (53.9%, 7/13) and patients aged between 18 and 25 years old (76.9%, 10/13). CD4 count among recent cases was 396.7±278.4 cells/mm3 . ARRA performance has resulted in a sensitivity of 98.89% (95% CI: 93.96-99.97) and specificity of 100.00% (95% CI: 95.98-100.00). Conclusion: ARRA performance showed excellent agreement with high sensitivity and specificity to comparing with a standard algorithm for HIV diagnosis. Its ability to classify new infections is crucial in HIV intervention and prevention strategies.

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