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.
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.
Authors: Felipe A Medeiros; Robert N Weinreb; Grant Moore; Jeffrey M Liebmann; Christopher A Girkin; Linda M Zangwill Journal: Ophthalmology Date: 2012-01-21 Impact factor: 12.079
Authors: Colleen M Kummet; K D Zamba; Carrie K Doyle; Chris A Johnson; Michael Wall Journal: Invest Ophthalmol Vis Sci Date: 2013-09-19 Impact factor: 4.799
Authors: Felipe A Medeiros; Linda M Zangwill; Kaweh Mansouri; Renato Lisboa; Ali Tafreshi; Robert N Weinreb Journal: Invest Ophthalmol Vis Sci Date: 2012-04-24 Impact factor: 4.799
Authors: Namita Bhardwaj; Philip I Niles; David S Greenfield; Maggie Hymowitz; Mitra Sehi; William J Feuer; Donald L Budenz Journal: J Glaucoma Date: 2013 Oct-Nov Impact factor: 2.503