Sexually transmitted diseases 2018 04 10() doi 10.1097/OLQ.0000000000000854
The ideal approach to triaging STD clinic patients between testing-only express visits and standard visits with clinician evaluation is uncertain.
In this cross-sectional study, we used classification and regression tree analysis to develop and validate the optimal algorithm for predicting which patients need a standard visit with clinician assessment (i.e., to maximize correct triage). Using electronic medical record data, we defined patients as needing a standard visit if they reported STD symptoms, received any empiric treatment, or were diagnosed with an infection or syndrome at the same visit. We considered 11 potential predictors for requiring medical evaluation collected via computer-assisted self-interview (CASI) when constructing the optimized algorithm. We compared test characteristics of the optimized algorithm, the Public Health-Seattle and King County STD Clinic’s current 13-component algorithm, and a simple two-component algorithm including only presence of symptoms and contact to STD.
During 10/2010-06/2015, 18,653 unique patients completed a CASI. In the validation samples, the optimized, current, and simple algorithms appropriately triaged 90%, 85%, and 89% of patients, respectively. The optimized algorithm had lower sensitivity for identifying patients needing standard visits (men: 94%; women: 93%) than the current algorithm (men: 95%; women: 98%), as did the simple algorithm (men: 91%; women: 93%). The optimized, current, and simple algorithms triaged 31%, 23%, and 33% of patients to express visits, respectively.
The overall performance of the statistically optimized algorithm did not differ meaningfully from a simple two-component algorithm. In contrast, the current algorithm had the highest sensitivity but lowest overall performance.