Dataset Information

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

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 ? 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 (? 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.

SUBMITTER: Saeedi OJ

PROVIDER: S-EPMC7260059 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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

Saeedi Osamah J OJ Elze Tobias T D'Acunto Loris L Swamy Ramya R Hegde Vikram V Gupta Surabhi S Venjara Amin A Tsai Joby J Myers Jonathan S JS Wellik Sarah R SR De Moraes Carlos Gustavo CG Pasquale Louis R LR Shen Lucy Q LQ Boland Michael V MV

Ophthalmology 20190204 6

<h4>Purpose</h4>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.<h4>Design</h4>Retrospective longitudinal cohort study.<h4>Participants</h4>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 relia ...[more]

PMID: 30731101

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Project description:ImportanceRates 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.ObjectiveTo compare 3 pointwise methods of estimating the rate of VF progression in glaucoma.Design, setting, and participantsThis 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.ExposuresEstimates 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 measuresProportion of VF series detected as progressing, estimates of false-positive proportions, time to detect progression, and agreement among measures.ResultsAmong 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 relevanceThese 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.

Project description:PurposeChoroideremia is an X-linked choroidopathy caused by pathogenic variants in the CHM gene. It is characterized by the early appearance of multiple scotomas in the peripheral visual field that spread and coalesce, usually sparing central vision until late in the disease. These features make quantitative monitoring of visual decline particularly challenging. Here, we describe a novel computational approach to convert Goldmann visual field (GVF) data into quantitative volumetric measurements. With this approach, we analyzed visual field loss in a longitudinal, retrospective cohort of patients with choroideremia.DesignSingle-center, retrospective, cohort study.ParticipantsWe analyzed data from 238 clinic visits of 56 molecularly-confirmed male patients with choroideremia from 41 families (range, 1-27 visits per patient). Patients had a median follow up of 4 years (range, 0-56 years) with an age range of 5 to 76 years at the time of their visits.MethodsClinical data from molecularly-confirmed patients with choroideremia, including GVF data, were included for analysis. Goldmann visual field records were traced using a tablet-based application, and the 3-dimensional hill of vision was interpolated for each trace. This procedure allowed quantification of visual field loss from data collected over decades with differing protocols, including different or incomplete isopters. Visual acuity (VA) data were collected and converted to logarithm of the minimum angle of resolution values. A delayed exponential mixed-effects model was used to evaluate the loss of visual field volume over time.Main outcome measuresVisual acuity and GVF volume.ResultsThe estimated mean age at disease onset was 12.6 years (standard deviation, 9.1 years; 95% quantile interval, 6.5-36.4 years). The mean field volume loss was 6.8% per year (standard deviation, 4.5%; 95% quantile interval, 1.9%-18.8%) based on exponential modeling. Field volume was more strongly correlated between eyes (r2 = 0.935) than best-corrected VA (r2 = 0.285).ConclusionsVolumetric analysis of GVF data enabled quantification of peripheral visual function in patients with choroideremia and evaluation of disease progression. The methods presented here may facilitate the analysis of historical GVF data from patients with inherited retinal disease and other diseases associated with visual field loss. This work informs the creation of appropriate outcome measures in choroideremia therapeutic trials, particularly in trial designs.Financial disclosuresProprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

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

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

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