PT  - JOURNAL ARTICLE
AU  - Yemisi Takwoingi
AU  - Adriana P Botello
AU  - Jennifer M Burr
AU  - Augusto Azuara-Blanco
AU  - David F Garway-Heath
AU  - Hans G Lemij
AU  - Roshini Sanders
AU  - Anthony J King
AU  - Jonathan J Deeks
AU  - for the Surveillance for Ocular Hypertension Study Group
TI  - External validation of the OHTS-EGPS model for predicting the 5-year risk of open-angle glaucoma in ocular hypertensives
AID  - 10.1136/bjophthalmol-2013-303622
DP  - 2014 Mar 01
TA  - British Journal of Ophthalmology
PG  - 309--314
VI  - 98
IP  - 3
4099  - http://bjo.bmj.com/content/98/3/309.short
4100  - http://bjo.bmj.com/content/98/3/309.full
SO  - Br J Ophthalmol2014 Mar 01; 98
AB  - Aims To independently evaluate and compare the performance of the Ocular Hypertension Treatment Study-European Glaucoma Prevention Study (OHTS-EGPS) prediction equation for estimating the 5-year risk of open-angle glaucoma (OAG) in four cohorts of adults with ocular hypertension. Methods Data from two randomised controlled trials and two observational studies were analysed individually to assess transferability of the prediction equation between different geographical locations and settings. To make best use of the data and to avoid bias, missing predictor values were imputed using multivariate imputation by chained equations. Using the OHTS-EGPS risk prediction equation, predicted risk was calculated for each patient in each cohort. We used the c-index, calibration plot and calibration slope to evaluate predictive ability of the equation. Results Analyses were based on 393, 298, 188 and 159 patients for the Rotterdam, Moorfields, Dunfermline, and Nottingham cohorts, respectively. The discriminative ability was good, with c-indices between 0.69 and 0.83. In calibration analyses, the risk of OAG was generally overestimated, although for the Rotterdam cohort the calibration slope was close to 1 (1.09, 95% CI 0.72 to 1.46), the ideal value when there is perfect agreement between predicted and observed risks. Conclusions The OHTS-EGPS risk prediction equation has predictive utility, but further validation in a population-based setting is needed.