Agreement and Predictors of Discordance of 6 Visual Field Progression Algorithms

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 Boland

Research output: Contribution to journalArticle

Abstract

Purpose: To determine the agreement of 6 established visual field (VF) progression algorithms in a large dataset of VFs from multiple institutions and to determine predictors of discordance among these algorithms. Design: Retrospective longitudinal cohort study. Participants: Visual fields from 5 major eye care institutions in the United States were analyzed, including a subset of eyes with at least 5 Swedish interactive threshold algorithm standard 24-2 VFs that met our reliability criteria. Of a total of 831 240 VFs, a subset of 90 713 VFs from 13 156 eyes of 8499 patients met the inclusion criteria. Methods: Six commonly used VF progression algorithms (mean deviation [MD] slope, VF index slope, Advanced Glaucoma Intervention Study, Collaborative Initial Glaucoma Treatment Study, pointwise linear regression, and permutation of pointwise linear regression) were applied to this cohort, and each eye was determined to be stable or progressing using each measure. Agreement between individual algorithms was tested using Cohen's κ coefficient. Bivariate and multivariate analyses were used to determine predictors of discordance (3 algorithms progressing and 3 algorithms stable). Main Outcome Measures: Agreement and discordance between algorithms. Results: Individual algorithms showed poor to moderate agreement with each other when compared directly (κ range, 0.12–0.52). Based on at least 4 algorithms, 11.7% of eyes progressed. Major predictors of discordance or lack of agreement among algorithms were more depressed initial MD (P < 0.01) and older age at first available VF (P < 0.01). A greater number of VFs (P < 0.01), more years of follow-up (P < 0.01), and eye care institution (P = 0.03) also were associated with discordance. Conclusions: This extremely large comparative series demonstrated that existing algorithms have limited agreement and that agreement varies with clinical parameters, including institution. These issues underscore the challenges to the clinical use and application of progression algorithms and of applying big-data results to individual practices.

Original languageEnglish (US)
JournalOphthalmology
DOIs
StatePublished - Jan 1 2019

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Visual Fields
Glaucoma
Linear Models
Longitudinal Studies
Cohort Studies
Multivariate Analysis
Outcome Assessment (Health Care)

ASJC Scopus subject areas

  • Ophthalmology

Cite this

Agreement and Predictors of Discordance of 6 Visual Field Progression Algorithms. / Saeedi, Osamah J.; Elze, Tobias; D'Acunto, Loris; Swamy, Ramya; Hegde, Vikram; Gupta, Surabhi; Venjara, Amin; Tsai, Joby; Myers, Jonathan S.; Wellik, Sarah R.; De Moraes, Carlos Gustavo; Pasquale, Louis R.; Shen, Lucy Q.; Boland, Michael.

In: Ophthalmology, 01.01.2019.

Research output: Contribution to journalArticle

Saeedi, OJ, Elze, T, D'Acunto, L, Swamy, R, Hegde, V, Gupta, S, Venjara, A, Tsai, J, Myers, JS, Wellik, SR, De Moraes, CG, Pasquale, LR, Shen, LQ & Boland, M 2019, 'Agreement and Predictors of Discordance of 6 Visual Field Progression Algorithms', Ophthalmology. https://doi.org/10.1016/j.ophtha.2019.01.029
Saeedi, Osamah J. ; Elze, Tobias ; D'Acunto, Loris ; Swamy, Ramya ; Hegde, Vikram ; Gupta, Surabhi ; Venjara, Amin ; Tsai, Joby ; Myers, Jonathan S. ; Wellik, Sarah R. ; De Moraes, Carlos Gustavo ; Pasquale, Louis R. ; Shen, Lucy Q. ; Boland, Michael. / Agreement and Predictors of Discordance of 6 Visual Field Progression Algorithms. In: Ophthalmology. 2019.
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AU - Saeedi, Osamah J.

AU - Elze, Tobias

AU - D'Acunto, Loris

AU - Swamy, Ramya

AU - Hegde, Vikram

AU - Gupta, Surabhi

AU - Venjara, Amin

AU - Tsai, Joby

AU - Myers, Jonathan S.

AU - Wellik, Sarah R.

AU - De Moraes, Carlos Gustavo

AU - Pasquale, Louis R.

AU - Shen, Lucy Q.

AU - Boland, Michael

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N2 - Purpose: To determine the agreement of 6 established visual field (VF) progression algorithms in a large dataset of VFs from multiple institutions and to determine predictors of discordance among these algorithms. Design: Retrospective longitudinal cohort study. Participants: Visual fields from 5 major eye care institutions in the United States were analyzed, including a subset of eyes with at least 5 Swedish interactive threshold algorithm standard 24-2 VFs that met our reliability criteria. Of a total of 831 240 VFs, a subset of 90 713 VFs from 13 156 eyes of 8499 patients met the inclusion criteria. Methods: Six commonly used VF progression algorithms (mean deviation [MD] slope, VF index slope, Advanced Glaucoma Intervention Study, Collaborative Initial Glaucoma Treatment Study, pointwise linear regression, and permutation of pointwise linear regression) were applied to this cohort, and each eye was determined to be stable or progressing using each measure. Agreement between individual algorithms was tested using Cohen's κ coefficient. Bivariate and multivariate analyses were used to determine predictors of discordance (3 algorithms progressing and 3 algorithms stable). Main Outcome Measures: Agreement and discordance between algorithms. Results: Individual algorithms showed poor to moderate agreement with each other when compared directly (κ range, 0.12–0.52). Based on at least 4 algorithms, 11.7% of eyes progressed. Major predictors of discordance or lack of agreement among algorithms were more depressed initial MD (P < 0.01) and older age at first available VF (P < 0.01). A greater number of VFs (P < 0.01), more years of follow-up (P < 0.01), and eye care institution (P = 0.03) also were associated with discordance. Conclusions: This extremely large comparative series demonstrated that existing algorithms have limited agreement and that agreement varies with clinical parameters, including institution. These issues underscore the challenges to the clinical use and application of progression algorithms and of applying big-data results to individual practices.

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