Dataset Information

Quantification of Visual Field Variability in Glaucoma: Implications for Visual Field Prediction and Modeling.

ABSTRACT: Purpose:To quantify visual field (VF) variability as a function of threshold sensitivity and location, and to compare weighted pointwise linear regression (PLR) with unweighted PLR and pointwise exponential regression (PER) for data fit and prediction ability. Methods:Two datasets were used for this retrospective study. The first was used to characterize and estimate VF variability, and included a total of 4,747 eyes of 3,095 glaucoma patients with six or more VFs and 3 years or more of follow-up. After performing PER for each series, standard deviation of residuals was quantified for each decibel of sensitivity as a measure of variability. A separate dataset was used to test and compare unweighted PLR, weighted PLR, and PER for data fit and prediction, and included 261 eyes of 176 primary open-angle glaucoma patients with 10 or more VFs and 6 years or more of follow-up. Results:The degree of variability changed as a function of threshold sensitivity with a zenith and a nadir at 33 and 11 dB, respectively. Variability decreased with eccentricity and was higher in the central 10° (P < 0.001). Differences among the methods for data fit were negligible. PER was the best model to predict future sensitivity values in the mid term and long term. Conclusions:VF variability increases with the severity of glaucoma damage and decreases with eccentricity. Weighted linear regression neither improves model fit nor prediction. PER exhibited the best prediction ability, which is likely related to the nonlinear nature of long-term glaucomatous perimetric decay. Translational Relevance:This study suggests that taking into account heteroscedasticity has no advantage in VF modeling.

SUBMITTER: Rabiolo A

PROVIDER: S-EPMC6798312 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Quantification of Visual Field Variability in Glaucoma: Implications for Visual Field Prediction and Modeling.

Rabiolo Alessandro A Morales Esteban E Afifi Abdelmonem A AA Yu Fei F Nouri-Mahdavi Kouros K Caprioli Joseph J

Translational vision science & technology 20190901 5

<h4>Purpose</h4>To quantify visual field (VF) variability as a function of threshold sensitivity and location, and to compare weighted pointwise linear regression (PLR) with unweighted PLR and pointwise exponential regression (PER) for data fit and prediction ability.<h4>Methods</h4>Two datasets were used for this retrospective study. The first was used to characterize and estimate VF variability, and included a total of 4,747 eyes of 3,095 glaucoma patients with six or more VFs and 3 years or m ...[more]

PMID: 31637105

Similar Datasets

Project description:Importance:Visual field variability may impair detection of glaucoma progression over time. Despite the possible overlap between neurocognitive disorders and glaucoma in older individuals, no study has investigated the association between cognitive changes and visual field variability. Objective:To evaluate the association between global neurocognitive impairment and visual field variability in patients diagnosed as having glaucoma or glaucoma suspects. Design, Setting, and Participants:This prospective observational cohort study was conducted at the Visual Performance Laboratory, University of California, San Diego. The study involved 211 eyes of 115 patients followed up for a mean (SD) period of 2.5 (0.8) years, ranging from 1.2 to 4.7 years. Data were obtained during the period extending from March 2011 to April 2015, with data analysis conducted from November 2015 to May 2016. Main Outcomes and Measures:Association between cognitive decline and visual field variability. Patients were monitored with standard automated perimetry (SAP) and had longitudinal assessment of cognitive ability using the Montreal Cognitive Assessment (MoCA). Visual field variability was estimated by the SD of the residuals of ordinary least squares linear regressions of SAP mean deviation (MD) values over time. Linear regression models were used to investigate the association between cognitive decline and visual field variability, adjusting for potentially confounding factors. Results:Among the 115 patients, the mean (SD) age at baseline was 67.4 (10.1) years, 63 were men (54.8%), and 86 were white (74.8%). There was a statistically significant association between change in MoCA scores and visual field variability over time. In a univariable model, a 5-point decline in MoCA score was associated with an increase of 0.18 dB in the SD of residuals of SAP MD (R2 = 4.3%; 95% CI, 0.06-0.30; P = .003). In a multivariable model adjusting for baseline MoCA score, mean SAP MD, age, sex, race/ethnicity, educational level, income, and number of SAP tests, each 5-point decline in MoCA score was associated with an increase of 0.23 dB in the SD of residuals of SAP MD (95% CI, 0.11-0.35; P < .001). Conclusions and Relevance:Cognitive decline was associated with increased visual field variability during follow-up. These findings suggest that screening and monitoring of cognitive dysfunction may be important in the assessment of visual field progression in the context of glaucoma.

Project description:PURPOSE:To develop a visual field (VF) feature model to predict the reversal of glaucoma hemifield test (GHT) results to within normal limits (WNL) after 2 consecutive outside normal limits (ONL) results. DESIGN:Retrospective cohort study. PARTICIPANTS:Visual fields of 44?503 eyes from 26?130 participants. METHODS:Eyes with 3 or more consecutive reliable VFs measured with the Humphrey Field Analyzer (Swedish interactive threshold algorithm standard 24-2) were included. Eyes with ONL GHT results for the 2 baseline VFs were selected. We extracted 3 categories of VF features from the baseline tests: (1) VF global indices (mean deviation [MD] and pattern standard deviation), (2) mismatch between baseline VFs, and (3) VF loss patterns (archetypes). Logistic regression was applied to predict the GHT results reversal. Cross-validation was applied to evaluate the model on testing data by the area under the receiver operating characteristic curve (AUC). We ascertained clinical glaucoma status on a patient subset (n = 97) to determine the usefulness of our model. MAIN OUTCOME MEASURES:Predictive models for GHT results reversal using VF features. RESULTS:For the 16?604 eyes with 2 initial ONL results, the prevalence of a subsequent WNL result increased from 0.1% for MD < -12 dB to 13.8% for MD ?-3 dB. Compared with models with VF global indices, the AUC of predictive models increased from 0.669 (MD ?-3 dB) and 0.697 (-6 dB ? MD < -3 dB) to 0.770 and 0.820, respectively, by adding VF mismatch features and computationally derived VF archetypes (P < 0.001 for both). The GHT results reversal was associated with a large mismatch between baseline VFs. Moreover, the GHT results reversal was associated more with VF archetypes of nonglaucomatous loss, severe widespread loss, and lens rim artifacts. For a subset of 97 eyes, using our model to predict absence of glaucoma based on clinical evidence after 2 ONL results yielded significantly better prediction accuracy (87.7%; P < 0.001) than predicting GHT results reversal (68.8%) with a prescribed specificity 67.7%. CONCLUSIONS:Using VF features may predict the GHT results reversal to WNL after 2 consecutive ONL results.

Project description:PurposeTo model the global test-retest variability of visual fields (VFs) in glaucoma.DesignRetrospective cohort study.ParticipantsTest-retest VFs from 4044 eyes of 4044 participants.MethodsWe selected 2 reliable VFs per eye measured with the Humphrey Field Analyzer (Swedish interactive threshold algorithm 24-2) within 30 days of each other. Each VF had fixation losses (FLs) of 33% or less, false-negative results (FNRs) of 20% or less, and false-positive results (FPRs) of 20% or less. Stepwise linear regression was applied to select the model best predicting the global test-retest variability from 3 categories of features of the first VF: (1) base parameters (age, mean deviation, pattern standard deviation, glaucoma hemifield test results, FPR, FNR, and FL); (2) total deviation (TD) at each location; and (3) computationally derived archetype VF loss patterns. The global test-retest variability was defined as root mean square deviation (RMSD) of TD values at all 52 VF locations.Main outcome measuresArchetype models to predict the global test-retest variability.ResultsThe mean ± standard deviation of the root mean square deviation was 4.39 ± 2.55 dB. Between the 2 VF tests, TD values were correlated more strongly in central than in peripheral VF locations (intraclass coefficient, 0.66-0.89; P < 0.001). Compared with the model using base parameters alone (adjusted R2 = 0.45), adding TD values improved prediction accuracy of the global variability (adjusted R2 = 0.53; P < 0.001; Bayesian information criterion [BIC] decrease of 527; change of >6 represents strong improvement). Lower TD sensitivity in the outermost peripheral VF locations was predictive of higher global variability. Adding archetypes to the base model improved model performance with an adjusted R2 of 0.53 (P < 0.001) and lowering of BIC by 583. Greater variability was associated with concentric peripheral defect, temporal hemianopia, inferotemporal defect, near total loss, superior peripheral defect, and central scotoma (listed in order of decreasing statistical significance), and less normal VF results and superior paracentral defect.ConclusionsInclusion of archetype VF loss patterns and TD values based on first VF improved the prediction of the global test-retest variability than using traditional global VF indices alone.

Dataset Information

Quantification of Visual Field Variability in Glaucoma: Implications for Visual Field Prediction and Modeling.

Publications

Quantification of Visual Field Variability in Glaucoma: Implications for Visual Field Prediction and Modeling.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets