Very low sensitivity of wet mount microscopy compared to PCR against culture in the diagnosis of vaginal trichomoniasis in Uganda: a cross sectional study

Sheila Nabweyambo, Othman Kakaire, Stefanie Sowinski, Alfred Okeng, Henry Ojiambo, Joshua Kimeze, Irene Najjingo, Freddie Bwanga

Trichomonas vaginalis (TV) causes the Trichomoniasis Syndrome composed of vaginitis in women, urethritis in men and tube infection in both sexes. This infection is strongly associated with premature rupture of membranes, preterm delivery, low birth weight, promoting HIV sexual transmission and infertility. Prevention of these complications requires accurate early detection and effective treatment of infected individuals. In the resource limited settings, the wet mount microscopy (WMM) is often the only available test for laboratory detection of TV, but its accuracy and that of polymerase chain reaction (PCR) tools in Uganda remain poorly studied. The aim of this cross-sectional study was to compare the diagnostic accuracy of the WMM and PCR against culture as reference standard for the direct diagnosis of TV among symptomatic women. Three high vaginal swabs were collected from each of one hundred fifty women presenting with symptoms suggestive of active vaginal trichomoniasis at the sexually transmitted diseases clinic of Mulago National Referral Hospital Kampala, Uganda. The swabs were tested for TV with WMM, in-house PCR and TV culture. Results were analysed using excel 2007, SPSS v16, and Meta-disc software to determine the diagnostic accuracy of the tests. The sensitivity, specificity and kappa agreement of the WMM was 25% (95% CI 5.5-57.2%), 100% (95% CI 97-100) and 0.38, respectively. Corresponding values for the PCR were 91.7% (95% CI 61.5-99.8), 99.3% (95% CI 96-100) and 0.91, respectively. Among the TV symptomatic women, the sensitivity of the WMM was very low, with two-thirds of the patients missing a diagnosis while the in-house PCR was highly sensitive and specific. Feasibility studies aimed at incorporating PCR tools in algorithms for diagnosis of TV infection in resource-limited settings are recommended.