The tradition algorithm approach underestimates the prevalence of serodiagnosis of syphilis in HIV-infected individuals.

Currently, there are three algorithms for screening of syphilis: traditional algorithm, reverse algorithm and European Centre for Disease Prevention and Control (ECDC) algorithm. To date, there is not a generally recognized diagnostic algorithm. When syphilis meets HIV, the situation is even more complex. To evaluate their screening performance and impact on the seroprevalence of syphilis in HIV-infected individuals, we conducted a cross-sectional study included 865 serum samples from HIV-infected patients in a tertiary hospital. Every sample (one per patient) was tested with toluidine red unheated serum test (TRUST), T. pallidum particle agglutination assay (TPPA), and Treponema pallidum enzyme immunoassay (TP-EIA) according to the manufacturer's instructions. The results of syphilis serological testing were interpreted following different algorithms respectively. We directly compared the traditional syphilis screening algorithm with the reverse syphilis screening algorithm in this unique population. The reverse algorithm achieved remarkable higher seroprevalence of syphilis than the traditional algorithm (24.9% vs. 14.2%, p < 0.0001). Compared to the reverse algorithm, the traditional algorithm also had a missed serodiagnosis rate of 42.8%. The total percentages of agreement and corresponding kappa values of tradition and ECDC algorithm compared with those of reverse algorithm were as follows: 89.4%,0.668; 99.8%, 0.994. There was a very good strength of agreement between the reverse and the ECDC algorithm. Our results supported the reverse (or ECDC) algorithm in screening of syphilis in HIV-infected populations. In addition, our study demonstrated that screening of HIV-populations using different algorithms may result in a statistically different seroprevalence of syphilis.

Abstract licence: CC BY

10 more references, whose wording the paper’s licence keeps private.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.