Developing a System to Predict Laboratory-Confirmed Chlamydial and/or Gonococcal Urethritis in Adult Male Emergency Department Patients

Abstract

Objectives: We aimed to create a system for predicting which male emergency department (ED) patients with suspected chlamydial and/or gonococcal urethritis would have laboratory-confirmed infections based on clinical factors available at the initial ED encounter. Methods: We used statistical models to develop a system to predict either the presence or absence of laboratory-confirmed chlamydial and/or gonorrheal urethritis based on patient demographics and presenting symptoms. Data for the system were extracted from a retrospective chart review of adult male patients who were suspected of having, and were tested for, chlamydial and/or gonococcal urethritis at an adult, urban, northeastern United States, academic ED from January 1998 to December 2004. Results: Among the 822 patients tested, 29.2% had chlamydia, gonorrhea, or both infections; 13.8% were infected with chlamydia alone, 12.1% were infected with gonorrhea alone, and 3.3% were infected with both. From the statistical models, the following factors were predictive of a positive laboratory test for chlamydia and/or gonorrhea: age ≤ 24 years, penile discharge, sexual contact with someone known to have chlamydia and/or gonorrhea, and not having health care insurance. A system using a hierarchical grouping of these factors based on the predicted probabilities of a laboratory-confirmed chlamydial and/or gonococcal urethritis, paired with baseline ED prevalence of these infections, was confirmed through internal validation testing to modestly predict which patients had or did not have a laboratory-confirmed infection. Conclusions: This system of a combination of risk factors available during the clinical encounter in the ED modestly predicts which adult male patients suspected of having chlamydial and/or gonorrheal urethritis are more likely to have or not have a laboratory-confirmed infection. A prospective study is needed to create and validate a clinical prediction rule based on the results of this system.