Prediction rules have been proposed as alternatives to screening recommendations and have potential applications in sexual health decision making. To our knowledge, there has been no review undertaken providing a critical appraisal of existing prediction rules in sexual health contexts. This review aims to identify and characterize prediction rules developed and validated for sexually transmitted infection (STI) screening, describe the methodological issues essential to the suitability of derived models for clinical or public health application, and synthesize the literature on the performance of these models.
We searched MEDLINE (2003–2012) to identify studies that reported on models predicting STIs. We explored the methodological quality of the studies based on a 16-item quality assessment checklist. We also evaluated the studies based on data extracted on model discrimination, calibration, sensitivity, and testing efficiency.
We identified 16 publications reporting on STI prediction rules. The most poorly addressed quality items were missing values, calibration measures, and variable definition. Overall, the performance of risk models as measured by discrimination (area under the receiver operating characteristic curve range, 0.64–0.88) and calibration was found to be generally good or satisfactory. Eight studies attained or were close to attaining the performance benchmark of testing less than 60% of the target population to achieve 90% sensitivity. The 2 risk models that were externally validated displayed adequate discrimination in new settings.
Although we identified several well-performing STI risk prediction rules, few have been validated. Future developments in the use of prediction rules should address their clinical consequence, comparative usefulness, external validity, and implementation impact.
A review identified several well-performing sexually transmitted infection risk prediction rules; however, few of these rules have been validated.
From the *The School of Population and Public Health, University of British Columbia, Vancouver, Canada; †The Department of Statistics, University of British Columbia, Vancouver, Canada; and ‡British Columbia Centre for Disease Control, Vancouver, Canada
Conflict of interest disclosure and sources of funding: Titilola Falasinnu is supported by the Canadian Institutes of Health Research Doctoral Research Award. Paul Gustafson is supported by a grant from the Natural Sciences and Engineering Research Council of Canada. All of the remaining authors have disclosed that they have no financial relationships with or interests in any commercial companies pertaining to this manuscript.
Correspondence: Titilola Falasinnu, MHS, School of Population and Public Health, University of British Columbia, 2206 East Mall, Vancouver, BC, Canada V6T 1Z3. E-mail address: firstname.lastname@example.org.
Received for publication February 14, 2013, and accepted February 18, 2014.