The diagnostic yield of the Meares & Stamey test can be significantly improved by symptom-based patient selection and the experience of the test performer

Background: Even if Meares-Stamey 4-glass (M&S) test is regarded a decisive tool for diagnosing prostatitis its use is only rarely performed in everyday clinical practice. Here, we analyze if the diagnostic yield of the M&S test could be improved by a pre-test categorization of patients due to undergo a M&S test. Methods: All clinical and microbiological data of patients who underwent M&S test in two urological centers from January 2004 to December 2021 were analyzed in this retrospective cohort study. One center has a dedicated staff member for the study of prostatitis (Cohort I), while the other center is a general urological unit (Cohort II). All patients were divided into 3 groups on the basis of the assembled data: patients with symptoms related to prostatitis only (Group I), patients with symptoms related to both prostatitis and BPH (Group II), patients with symptoms related to BPH only (Group III). The rates of positive microbiological results in each group were compared. Results: In the whole period, 9347 patients were analyzed and categorized as follows: Group I, 1884; Group II, 5151; Group III, 2312. Three-thousand and eight-hundred twenty-three patients showed positive culture results (40.9%). The most common isolated species was Escherichia coli (49.7%), followed by Enteroccus spp. (31.8%). The rates of positive M&S tests in the different symptom groups were: Group I, 1532 (81.4%); Group II, 1494 (29.0%); Group III, 797 (34.4%). The overall rate of positive M&S tests in each urology center showed that the center with a staff member who is dedicated to prostatitis studies (Cohort I) had a significantly higher rate of positive M&S tests than the general urological department (Cohort II) (64.3% vs 31.4%; p < 0.001). Conclusions: Symptom-based patient selection and dedicated staff members will increase the diagnostic yield of the M&S test and reduce the number of unnecessary tests.