We evaluated the precision and accuracy of a procedure for detecting recent human immunodeficiency virus (HIV) infections, specifically, the avidity index (AI) calculated using a method based on an automated AxSYM HIV 1/2gO assay (Abbott). To evaluate precision, we performed multiple replicates on eight HIV-positive serum samples. To evaluate the accuracy in identifying recent infections (i.e., within 6 months of seroconversion), we used 216 serum samples from 47 persons whose dates of seroconversion were known. To evaluate the sensitivity and specificity of the procedure for different AI cutoff values, we performed receiver operating characteristic (ROC) analysis. To determine the effects of antiretroviral treatment, advanced stage of the disease (i.e., low CD4-cell count), and low HIV viral load on the AI, we analyzed 15 serum samples from 15 persons whose dates of seroconversion were unknown. The precision study showed that the procedure was robust (i.e., the total variance of the AI was lower than 10%). Regarding accuracy, the mean AI was significantly lower for samples collected within 6 months of seroconversion, compared to those collected afterwards (0.68 ؎ 0.16 versus 0.99 ؎ 0.10; P < 0.0001), with no overlap of the 95% confidence intervals. The ROC analysis revealed that an AI lower than 0.6 had a sensitivity of 33.3% and a specificity of 98.4%, compared to 87.9 and 86.3%, respectively, for an AI lower than 0.9. Antiretroviral treatment, low CD4-cell count, and low viral load had no apparent effect on the AI. In conclusion, this procedure is reproducible and accurate in identifying recent infections; it is automated, inexpensive, and easy to perform, and it provides a quantitative result with different levels of sensitivity and specificity depending on the selected cutoff.For persons infected with the human immunodeficiency virus (HIV), knowing at what point in time infection occurred would be useful for a variety of purposes, including surveillance, the planning of vaccine trials, making decisions with regard to treatment, tailoring and evaluating preventive measures, and partner notification. Although for other infections the time of infection can be approximated based on the dynamics of the antibody response, these procedures have not been standardized for HIV infection. Moreover, although the classic sequence of the antibody response, in which a primary immunoglobulin M (IgM) response is followed by an increase in IgG and the IgG response increases with repeated exposures (20), is generally applicable to HIV infection, current screening assays are not able to discriminate among the immunoglobulin classes of anti-HIV antibodies.In the attempt to discover a means of distinguishing recent HIV infections from established infections in single sampling, researchers have studied various antibody assays and testing strategies (4, 12). However, some of these assays have a number of drawbacks, specifically, high costs, difficulties in performing the assay, low reproducibility, qualitative rather than quantita...

We evaluated a procedure for identifying recent HIV infections, using sequential serum samples from 47 HIV-positive persons for whom the seroconversion date could be accurately estimated. Each serum sample was divided into two aliquots: one diluted with phosphate-buffered saline and the other diluted with 1 M guanidine. We assayed the aliquots with the automated AxSYM HIV1/2gO test (Abbott Diagnostics Division), without modifying the manufacturer's protocol. We then calculated the avidity index (AI): the ratio of the sample/cutoff value for the guanidine aliquot to that of the phosphate-buffered saline aliquot. We analyzed 216 serum samples: 34 samples were collected within 6 months of seroconversion (recent seroconversions), and 182 were collected after 6 months. The mean AIs, by time from seroconversion, were 0.68 +/- 0.16 (within 6 months) and 0.98 +/- 0.10 (after 6 months) (P < 0.0001). AI of <0.90 correctly identified 88.2% of recent infections but misclassified as recent infections 13.2% of serum samples collected afterward. The probability of an infection being classified as recent and having AI of > or = 0.90 would be 0.7% in a population with 5% recent infections. AI can identify with a certain degree of accuracy recent HIV infections, and being a quantitative index, it provides different levels of sensitivity and specificity, depending on the selected cutoff value. The standard assay procedure is not modified. This test is simple and inexpensive and could be used for surveillance, decision-making in treatment, and prevention.