The algorithms used for the diagnosis of syphilis: an integrative review

• Autor(a) principal: Peçanha Júnior, Claudio
• Data de Publicação: 2022
• Outros Autores: Brasil, Girlandia Alexandre
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Research, Society and Development
• Texto Completo: https://rsdjournal.org/index.php/rsd/article/view/31447
• topic: Sífilis; Treponema pallidum; Algoritmos de Diagnósticos; Pruebas serológicas.; Sífilis; Treponema pallidum; Algoritmos de Diagnóstico; Testes sorológicos.; Syphilis; Treponema pallidum; Diagnostic Algorithms; Serological tests.

Syphilis is an infectious disease caused by the bacterium Treponema pallidum (T. pallidum), which can progress to a chronic disease, with irreversible sequelae for affected individuals. Its transmission is by unprotected sexual contact, blood transfusion or vertical transmission. The disease is classified as primary, secondary, latent or tertiary. Despite the request for prevention, in 2020 the World Health Organization (WHO) counted 7 million new cases worldwide, so accurate diagnosis becomes essential. Currently, there is no laboratory test that is considered a reliable gold standard, so different diagnostic algorithms have been applied, three of which are the most common for serological diagnosis, being the traditional, reverse algorithm and the European Center for Disease Prevention and Control (ECDC). Therefore, the present study aimed to perform an integrative review on the algorithms used for the diagnosis of syphilis, for this purpose the following descriptors "Syphilis" and "Treponema pallidum" and "Diagnostic algorithms" and "Serological tests" were used in the different databases for the selection of articles. In total, 21 articles were included in the study. After analyzing the included articles, it was evidenced that the reverse algorithm has a better ability to detect cases of early and late syphilis. Quantitative non-treponemais trials on active infections and treatment follow-up are recommended. Despite this data, the choice of which algorithm to use should be based on local epidemiology, workload, automation needs, and available budget.