Prediction model for gonorrhea, chlamydia, and trichomoniasis in the emergency department.

Abstract

History and physical examination findings can be unreliable for prediction of genitourinary tract infections and differentiation of urinary tract infections from sexually transmitted infections (STIs). The study objective was to develop a prediction tool to more accurately identify patients with STIs. A retrospective review of 64,490 emergency department (ED) encounters between April 18, 2014, and March 7, 2017, where patients age 18 years or older had urinalysis and urine culture or testing for gonorrhea, chlamydia, or trichomonas, was used to develop a prediction model for men and women with Neisseria gonorrhoeae or Chlamydia trachomatis, or both, and for women with Trichomonas vaginalis. The data set was randomly divided into two-thirds discovery and one-third validation. Groups were assigned through a random number generator. Backward step regression modeling was used to identify the best model for each outcome. With use of age, race, marital status, and findings from vaginal wet preparation (white blood cells [WBCs], clue cells, and yeast) and urinalysis (squamous epithelial cells, protein, leukocyte esterase, and WBCs), the models had areas under the receiver operating characteristic curve of 0.80 for men with N gonorrhoeae or C trachomatis, or both; 0.75 for women with N gonorrhoeae or C trachomatis, or both; and 0.73 for women with T vaginalis. The model estimated likelihood of ED patients having STIs was reasonably accurate with a limited number of demographic and laboratory variables. In the absence of point-of-care STI testing, use of a prediction tool for STIs may improve antimicrobial stewardship.