Performance Characteristics of a New Less Sensitive HIV-1 Enzyme Immunoassay for Use in Estimating HIV Seroincidence

Abstract

Less sensitive (LS) HIV-1 enzyme immunoassays (EIAs) have significantly improved the quantity and quality of HIV surveillance data. The first LS-HIV-1 EIA, the Abbott 3A11-LS, provided reliable incidence data, but the assay required specialized equipment, and the lack of available reagents made testing difficult. This study evaluated the use of an alternate assay, a modified version of the Vironostika HIV-1 EIA (Vironostika-LS), to be used for LS testing. The Vironostika-LS has similar performance characteristics to the Abbott 3A11-LS with additional advantages. This 96-well formatted assay is commonly found in public health laboratories for routine HIV-1 testing and can be used with both serum and dried blood spot specimens. The estimated mean time from seroconversion (defined using a standardized optical density cutoff of 1.0) with the Vironostika-LS was 170 days (95% CI, 145-200 days). When the Vironostika-LS was applied to a matched serum set previously tested with the Abbott 3A11-LS, the Vironostika-LS accurately identified 97% of specimens with recent or long-standing HIV infection. The paper also reports Vironostika-LS quality control guidelines and the results from 3 rounds of proficiency testing.

Estimating HIV-1 incidence using the serologic testing algorithm for recent HIV infections at HIV counseling and testing centers in the city of São Paulo, Brazil

“…Double-negative samples were considered HIV-1 negative. Samples for which the ELISA results were inconclusive, as well as those testing positive for HIV-1 only once, were further analyzed by Western blot according to the guidelines established by the Brazilian Health Ministry [3].…”

“…The HIV-1 epidemic in Brazil is characterized by the number of reported AIDS cases and the number of HIV-infected pregnant women (and their infected children), as well as HIV-1 sentinel surveillance of certain population groups. In several studies, recent HIV-1 infection has been described using a less sensitive ELISA [1][2][3][4]. This detection strategy, known as the serologic testing algorithm for recent HIV seroconversion (STARHS) [3], has been used to estimate HIV-1 incidence and to investigate epidemiological aspects in newly-infected individuals.…”

Dynamics of HIV Type 1 Recombination Following Superinfection

“…The patient was determined to have been recently infected with HIV-1 ( < 6 months) by serological incidence testing. 13 The viral load and CD4 counts over the period of study are shown in Fig. 1.…”

Accuracy of different thresholds for the anti-HIV avidity index

“…Le indagini di laboratorio finora disponibili per la diagnosi e il monitoraggio dell'infezione da HIV (ricerca di anticorpi con test di "screening" e di conferma, antigene p24, test combinati per anticorpi e antigene (2), HIV-RNA, conta dei linfociti CD4) non si sono rivelate accurate nell'individuare chi, fra i nuovi infetti, fosse anche un sieroconvertito recente. Alcuni studi internazionali hanno analizzato la possibilità di identificare le persone che hanno sieroconvertito recentemente per l'HIV attraverso un test da effettuare su un semplice campione di siero; in particolare, negli Stati Uniti è stato studiato ed applicato il "detuned test" o "STARHS" (Serologic Testing Algorithm for Recent HIV Seroconversion), che tuttavia presenta molti limiti (7,8,11). Una "datazione" dell'infezione, utile sia per scopi epidemiologici (sorveglianza di infezione, incidenza) che clinici è teoricamente possibile, come per altre patologie infettive, con metodiche atte a valutare l'avidità anticorpale, in base We evaluated the accuracy of the avidity index (AI) of anti-HIV antibodies on 357 serum samples obtained from 127 subjects for whom an estimated date of seroconversion was calculated on the basis of the interval between the last negative and first positive anti-HIV test result.The ROC curve analysis performed at different thresholds of the AI showed that a cutoff of 0.80 (93.0% sensitivity and 98.5% specificity) yields the best overall accuracy (95.8%) and should be employed for surveillance purposes, whereas the application of the anti-HIV AI on individual cases is not recommended.…”