A critical appraisal of risk models for predicting sexually transmitted infections.
BACKGROUND
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.


METHODS
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.


RESULTS
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.


CONCLUSIONS
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.
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.
Titilola
T
Falasinnu
Paul
P
Gustafson
Travis Salway
TS
Hottes
Mark
M
Gilbert
Gina
G
Ogilvie
Jean
J
Shoveller
D003362:Cost-Benefit Analysis; D003657:Decision Making; D005260:Female; D006266:Health Education / Q000191:economics / Q000379:methods; D006293:Health Promotion / Q000191:economics; D006801:Humans; D020407:Internet; D008297:Male; D008962:Models, Theoretical; D018570:Risk Assessment / Q000379:methods*; D012749:Sexually Transmitted Diseases / Q000191:economics / Q000517:prevention & control*