Detecting recent HIV infections is important to evaluate incidence and monitor epidemic trends. We aimed to evaluate the diagnostic performance and accuracy of the avidity index (AI) for discriminating for recent HIV infections. We collected serum samples from HIV-1 positive individuals: A) with known date of infection (midpoint in time between last HIV-negative and first HIV-positive test); B) infected for >1 year. Samples were divided into two aliquots: one diluted with phosphate buffered saline (PBS) and the other with 1 M guanidine. Both aliquots were assayed by the Architect HIV Ag/Ab Combo 4th generation assay (Abbott). We compared AI found in recent (RI=<6 months from seroconversion) and established (EI) infections. The diagnostic accuracy was evaluated by receiver operating characteristic (ROC) curve analysis. The proportion of samples misclassified as recent (FRR) was calculated. In total, 647 samples were collected: 455 in group A (51.6% RI and 48.4% EI) and 192 in group B. Among these, sixteen samples were from elite controllers, 294 from treated patients, 328 from patients infected with non-B subtypes. Samples before antiretroviral initiation showed a mean AI significantly lower among RI compared to EI (0.66+0.28 vs. 1.00±0.12; p<0.000). The FRR was 0% using a cut-off of ≤0.70. An extremely low FRR was observed among elite controllers, samples with low VL or CD4. HIV subtype had no impact on AI misclassifications. All individuals in group A reached the AI threshold of 0.80 within 24 months after seroconversion. The AI is an accurate serological marker for discriminating recent from established HIV infections and meets WHO requirements for HIV incidence assays. © 2017 Walter de Gruyter GmbH, Berlin/Boston.
HIV avidity index performance using a modified fourth-generation immunoassay to detect recent HIV infections
RODELLA, ANNA;Terlenghi, Luigina;Caruso, Arnaldo;
2017-01-01
Abstract
Detecting recent HIV infections is important to evaluate incidence and monitor epidemic trends. We aimed to evaluate the diagnostic performance and accuracy of the avidity index (AI) for discriminating for recent HIV infections. We collected serum samples from HIV-1 positive individuals: A) with known date of infection (midpoint in time between last HIV-negative and first HIV-positive test); B) infected for >1 year. Samples were divided into two aliquots: one diluted with phosphate buffered saline (PBS) and the other with 1 M guanidine. Both aliquots were assayed by the Architect HIV Ag/Ab Combo 4th generation assay (Abbott). We compared AI found in recent (RI=<6 months from seroconversion) and established (EI) infections. The diagnostic accuracy was evaluated by receiver operating characteristic (ROC) curve analysis. The proportion of samples misclassified as recent (FRR) was calculated. In total, 647 samples were collected: 455 in group A (51.6% RI and 48.4% EI) and 192 in group B. Among these, sixteen samples were from elite controllers, 294 from treated patients, 328 from patients infected with non-B subtypes. Samples before antiretroviral initiation showed a mean AI significantly lower among RI compared to EI (0.66+0.28 vs. 1.00±0.12; p<0.000). The FRR was 0% using a cut-off of ≤0.70. An extremely low FRR was observed among elite controllers, samples with low VL or CD4. HIV subtype had no impact on AI misclassifications. All individuals in group A reached the AI threshold of 0.80 within 24 months after seroconversion. The AI is an accurate serological marker for discriminating recent from established HIV infections and meets WHO requirements for HIV incidence assays. © 2017 Walter de Gruyter GmbH, Berlin/Boston.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
