Comparison of methods to predict visual field progression in glaucoma.

OBJECTIVE

To compare performance of pointwise linear regression, Glaucoma Change Probability Analysis (GCPA), and the Advanced Glaucoma Intervention Study (AGIS) method in predicting visual field progression in glaucoma.

DESIGN

Longitudinal visual field data from AGIS. Proportion of progressing eyes and time to progression were the main outcome measures. One hundred fifty-six patients with 8 or more years of follow-up were included. Prediction of outcomes at 8 years was used to evaluate the performance of each method (pointwise linear regression, GCPA, and AGIS).

RESULTS

Visual field progression at 8 years was detected in 35%, 31%, and 22% of patients by pointwise linear regression, GCPA, and the AGIS method, respectively. Baseline mean deviation was not different for nonprogressing vs progressing eyes for all methods (P > .05). Pointwise linear regression and GCPA had the highest pairwise concordance (kappa = 0.58 [SD, 0.07]). The false prediction rates at 4 and 8 years varied between 1% and 3%. Glaucoma Change Probability Analysis predicted final outcomes better than pointwise linear regression at 4 years (P = .001).

CONCLUSIONS

All algorithms had low false prediction rates. Glaucoma Change Probability Analysis predicted outcomes better than pointwise linear regression early during follow-up. Algorithms did not perform differently as a function of baseline damage. Pointwise linear regression and GCPA did not agree well regarding spatial distribution of worsening test locations.