Roland C. Merchant, MD, MPH, ScD Clinical Prediction Rule for Chlamydia and/or Gonorrhea Urethritis Among Adult Male Emergency Department Patients Roland C. Merchant, MD, MPH, ScD Dina M. DePalo, BA Tao Liu, PhD Josiah D. Rich, MD, MPH Michael D. Stein, MD

Background Males with signs and symptoms of urethritis frequently present to emergency departments (EDs) for medical care No commercially-available rapid tests for chlamydia/gonorrhea Empiric treatment vs. waiting for test results CDC guidance on when to initiate empiric treatment for chlamydia and/or gonorrhea can lead to antibiotic overusage Methods of limiting antibiotic usage to when it is indicated are needed to avoid antibiotic overusage, reduce antibiotic resistance, reduce costs, and avoid adverse reactions

Objectives Create a clinical prediction rule for predicting laboratory-confirmed chlamydia and/or gonorrhea infections among adult male ED patients Help ED clinicians determine when to begin antibiotic treatment empirically or defer antibiotics pending test results

Methods (1) Study design Study setting: Rhode Island Hospital ED Retrospective study of adult male ED patients Medical record and laboratory database review of patients diagnosed in the ED with a possible urethral infection prior to laboratory results of testing Study setting: Rhode Island Hospital ED Providence, Rhode Island Level I trauma center and academic medical center affiliated with Brown University >95,000 adult patient visits annually

Methods (2) Study participants Adult (≥18 years-old) males whose ED clinician diagnosed them with possible chlamydia and/or gonorrhea urethritis Exclusion criteria Known chlamydia/gonorrhea Repeat visits for the same infection Did not have laboratory testing performed for chlamydia and/or gonorrhea Urine ligase nucleic acid amplification tests (NAATS) for both chlamydia and gonorrhea

Methods (3) Data collection January 1998-December 2004 Hospital and ED provider billing record computerized databases were independently searched for subjects Cases of interest to the study were identified using International Classification of Disease, 9th revision, Clinical Modification (ICD-9) codes Captured visits from both databases Medical records and laboratory databases reviewed for subjects identified for the study

Methods (4) Prediction rule construction Selected demographic and clinical characteristics associated with the presence of a laboratory-confirmed infection Best functional form of variables selected Pearson’s χ2 testing Likelihood ratios (LRs) Logistic regression for odds ratios (ORs) Receiver-operator characteristic (ROC) curve plots

Methods (5) Prediction rule construction (cont.) Created a training dataset (75% of patients) and testing dataset (25%) by random assignment Using the training dataset Used multivariable logistic regression to establish the final components of the prediction rule Grouped and ranked factors by their ability to identify the presence/absence of an infection Established five hierarchical risk levels for the presence/absence of an infection Internally validated the prediction rule using the testing dataset

Results (1) ICD-9 code search 1,218 ED visits identified and 1,166 (95.7%) could be reviewed 985 had a possible chlamydia/gonorrhea infection as diagnosed by the ED clinician 822 had chlamydia/gonorrhea testing 29.2% had chlamydia, gonorrhea, or both 13.8% had chlamydia alone 12.1% had gonorrhea alone 3.3% had both

Results (2)

Demographic Profiles

History of Present Illness Profiles

History of Known STD Contact Profiles

Demographic Variables

Symptoms and STD Contact History Variables

Prediction Rule for the Presence of a Chlamydia, Gonorrhea, or Both Infections

Prediction Rule for the Absence of a Chlamydia, Gonorrhea, or Both Infections

Predicted Probabilities of Infection by Baseline Prevalence Using the Prediction Rule Plot A Plot B Presence of an infection Absence of an infection *DC=discharge, C/G contact=sexual contact with someone known to have chlamydia and/or gonorrhea *Diagonal lines indicate when pre-test and post-test probability of infection are equal. *Horizontal lines indicate when the post-test probability of infection is equal to chance

Conclusions and Implications A combination of demographic and clinical factors can modestly assist ED clinicians in predicting which patients are more likely to have chlamydia/gonorrhea urethritis Needs prospective, external validation May help promote more effective empiric treatment

Questions