Literature DB >> 32239185

Pointwise Methods to Measure Long-term Visual Field Progression in Glaucoma.

Diana Salazar1, Esteban Morales1, Alessandro Rabiolo1,2, Vicente Capistrano1, Mark Lin1, Abdelmonem A Afifi3, Fei Yu1,3, Kouros Nouri-Mahdavi1, Joseph Caprioli1.   

Abstract

Importance: Rates of visual field (VF) progression vary among patients with glaucoma. Knowing the rate of progression of individual patients would allow appropriately aggressive therapy for patients with high rates of visual loss and protect those with low rates from unnecessary therapy. Objective: To compare 3 pointwise methods of estimating the rate of VF progression in glaucoma. Design, Setting, and Participants: This retrospective, observational cohort study included 729 eyes of 567 consecutive patients with primary open-angle glaucoma who had at least 6 reliable VFs and at least 3 years of follow-up. One hundred seventy-six patients (257 eyes) were treated at a tertiary glaucoma center; in addition, data were collected from 391 participants (472 eyes) in the Advanced Glaucoma Intervention Study. Data were collected from May 1988 to November 2004 and analyzed from October 2018 to February 2019. Exposures: Estimates of VF progression were measured with guided progression analysis (GPA), pointwise linear regression (PLR), and the glaucoma rate index (GRI). A subgroup analysis was performed in a subset of patients with likely VF progression and likely VF stability. Main Outcomes and Measures: Proportion of VF series detected as progressing, estimates of false-positive proportions, time to detect progression, and agreement among measures.
Results: Among the 567 patients included in the analysis, mean (SD) age was 65.6 (9.7) years, 300 (52.9%) were female, and 295 (52.0%) were white. The median baseline mean deviation was -6.7 (interquartile range [IQR], -11.6 to -3.5) dB; the median follow-up time, 8.9 (IQR, 7.3-10.4) years. The proportion of eyes labeled as progressing was 27.7% according to the GPA, 33.5% according to the PLR, and 52.9% according to the GRI; pairwise differences for GRI vs PLR were 20% (95% CI, 17%-23%); for GRI vs GPA, 25% (95% CI, 22%-29%); and for PLR vs GPA, 6% (95% CI, 3%-9%; P < .001 for all comparisons, McNemar test). The shortest median time to progression was with the GRI (8.8 [IQR, 2.4-10.5 years), compared with the GPA and PLR (both >16 years). The hazard ratio of VF progression for GRI vs PLR (reference) was 11.3 (95% CI, 9.2-13.7); for GRI vs GPA (reference), 18.1 (95% CI, 14.5-22.6); and for PLR vs GPA (reference), 1.5 (95% CI, 1.3-1.9; P < .001 for all comparisons, Cox proportional hazards regression). These results held in the subgroup with likely progression; the proportions of progressing eyes were 73.7% (115 of 156) for GPA, 81.4% (127 of 156) for PLR, and 92.9% (145 of 156) for GRI. Pairwise difference for GRI vs PLR was 11.5% (95% CI, 7.4%-17.6%; P < .001, McNemar test); for GRI vs GPA, 19.2% (95% CI, 12.6%-26.4%; P < .001, McNemar test); and for PLR vs GPA, 7.7% (95% CI, 0.3%-15.7%; P = .08, McNemar test). Conclusions and Relevance: These results suggest GRI can detect long-term VF progression in glaucoma earlier than PLR or GPA. Validation with prospective designs may strengthen the generalizability and value of this method.

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Year:  2020        PMID: 32239185      PMCID: PMC7118669          DOI: 10.1001/jamaophthalmol.2020.0647

Source DB:  PubMed          Journal:  JAMA Ophthalmol        ISSN: 2168-6165            Impact factor:   7.389


  39 in total

1.  Visual field progression: comparison of Humphrey Statpac2 and pointwise linear regression analysis.

Authors:  A I McNaught; D P Crabb; F W Fitzke; R A Hitchings
Journal:  Graefes Arch Clin Exp Ophthalmol       Date:  1996-07       Impact factor: 3.117

2.  Agreement and Predictors of Discordance of 6 Visual Field Progression Algorithms.

Authors:  Osamah J Saeedi; Tobias Elze; Loris D'Acunto; Ramya Swamy; Vikram Hegde; Surabhi Gupta; Amin Venjara; Joby Tsai; Jonathan S Myers; Sarah R Wellik; Carlos Gustavo De Moraes; Louis R Pasquale; Lucy Q Shen; Michael V Boland
Journal:  Ophthalmology       Date:  2019-02-04       Impact factor: 12.079

3.  Converting to SITA-standard from full-threshold visual field testing in the follow-up phase of a clinical trial.

Authors:  David C Musch; Brenda W Gillespie; Bonnie M Motyka; Leslie M Niziol; Richard P Mills; Paul R Lichter
Journal:  Invest Ophthalmol Vis Sci       Date:  2005-08       Impact factor: 4.799

4.  The measurement of observer agreement for categorical data.

Authors:  J R Landis; G G Koch
Journal:  Biometrics       Date:  1977-03       Impact factor: 2.571

5.  Glaucoma Progression Analysis software compared with expert consensus opinion in the detection of visual field progression in glaucoma.

Authors:  Angelo P Tanna; Donald L Budenz; Jagadeesh Bandi; William J Feuer; Robert M Feldman; Leon W Herndon; Douglas J Rhee; Julia Whiteside-de Vos; Joyce Huang; Douglas R Anderson
Journal:  Ophthalmology       Date:  2011-12-02       Impact factor: 12.079

6.  A method to measure and predict rates of regional visual field decay in glaucoma.

Authors:  Joseph Caprioli; Dennis Mock; Elena Bitrian; Abdelmonem A Afifi; Fei Yu; Kouros Nouri-Mahdavi; Anne L Coleman
Journal:  Invest Ophthalmol Vis Sci       Date:  2011-07-01       Impact factor: 4.799

7.  Comparison of methods to detect visual field progression in glaucoma .

Authors:  K Nouri-Mahdavi; L Brigatti; M Weitzman; J Caprioli
Journal:  Ophthalmology       Date:  1997-08       Impact factor: 12.079

8.  Visual field progression in glaucoma: estimating the overall significance of deterioration with permutation analyses of pointwise linear regression (PoPLR).

Authors:  Neil O'Leary; Balwantray C Chauhan; Paul H Artes
Journal:  Invest Ophthalmol Vis Sci       Date:  2012-10-01       Impact factor: 4.799

9.  Comparison of Methods to Detect and Measure Glaucomatous Visual Field Progression.

Authors:  Alessandro Rabiolo; Esteban Morales; Lilian Mohamed; Vicente Capistrano; Ji Hyun Kim; Abdelmonem Afifi; Fei Yu; Anne L Coleman; Kouros Nouri-Mahdavi; Joseph Caprioli
Journal:  Transl Vis Sci Technol       Date:  2019-09-11       Impact factor: 3.283

10.  Enhancement of Visual Field Predictions with Pointwise Exponential Regression (PER) and Pointwise Linear Regression (PLR).

Authors:  Esteban Morales; John Mark S de Leon; Niloufar Abdollahi; Fei Yu; Kouros Nouri-Mahdavi; Joseph Caprioli
Journal:  Transl Vis Sci Technol       Date:  2016-03-14       Impact factor: 3.283

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  1 in total

1.  Pointwise and Region-Wise Course of Visual Field Loss in Patients With Glaucoma.

Authors:  Samaneh Sabouri; Saeedeh Pourahmad; Koenraad A Vermeer; Hans G Lemij; Siamak Yousefi
Journal:  Transl Vis Sci Technol       Date:  2022-07-08       Impact factor: 3.048

  1 in total

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