Prevalence of hepatitis C virus (HCV) genotypes and increase of type 4 in central Italy: an update and report of a new method of HCV genotyping.

Hepatitis C virus (HCV) genotyping is very useful for identifying the patients (type 1 and 4) that need more aggressive management. In recent years, genotype 4 has shown spread in different parts of Europe. The aim of this study was to update on the prevalence of HCV genotypes of 288 patients in Central Italy, to analyze the possible increase of genotype 4 and to evaluate a new simple genotyping method. A line-probe assay (LiPA, Bayer) was used based on the reverse hybridization of HCV genome fragments previously amplified and biotinylated by a polymerase chain reaction (PCR) assay, COBAS System Amplicor HCV monitor version 2.0 (Roche) or previously amplified by COBAS Ampliprep/TaqMan HCV test (Roche). This last method uses non-biotin-labeled primers, therefore we added for each sample 10 microl of amplified HCV products, 10 microl of denaturation solution and 10 microl of biotinylated-nested primers (Bayer) to utilize the genotyping procedure previously used. The results showed that the prevalence of type 1, 2 and 3 (482, 34.6 and 10.5%, respectively) as well as the prevalent subtypes, 1b and 2a/2c (30.7 and 27.2%, respectively) were similar to previous data. Type 1 and 2 were statistically associated with an older group of patients when compared with type 3 and 4 (p < 0.001). Type 3 and 4 showed a significant prevalence of male patients compared to type 1 and in particular to type 2 (p < 0.014). The prevalence of type 4 was 5.6% in 2004, 6.1% in 2005 and 9.9% from January to July 2006. Type 4 showed an increase of male prevalence over a 3-year period (p < 0.001). In conclusion, subtype 1b and 2a/2c showed a very similar prevalence, age and gender distribution in Central Italy. The type 4 patient group was analyzed because an increase of this genotype (1.8 times) was detected.

Identifying recent HIV infections using the avidity index and an automated enzyme immunoassay.

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.

Precision and accuracy of a procedure for detecting recent human immunodeficiency virus infections by calculating the antibody avidity index by an automated immunoassay-based method.

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.