Dataset Information

Effect of Cognitive Demand on Functional Visual Field Performance in Senior Drivers with Glaucoma.

ABSTRACT: Purpose: To investigate the effect of cognitive demand on functional visual field performance in drivers with glaucoma. Method: This study included 20 drivers with open-angle glaucoma and 13 age- and sex-matched controls. Visual field performance was evaluated under different degrees of cognitive demand: a static visual field condition (C1), dynamic visual field condition (C2), and dynamic visual field condition with active driving (C3) using an interactive, desktop driving simulator. The number of correct responses (accuracy) and response times on the visual field task were compared between groups and between conditions using Kruskal-Wallis tests. General linear models were employed to compare cognitive workload, recorded in real-time through pupillometry, between groups and conditions. Results: Adding cognitive demand (C2 and C3) to the static visual field test (C1) adversely affected accuracy and response times, in both groups (p < 0.05). However, drivers with glaucoma performed worse than did control drivers when the static condition changed to a dynamic condition [C2 vs. C1 accuracy; glaucoma: median difference (Q1-Q3) 3 (2-6.50) vs.

Controls

2 (0.50-2.50); p = 0.05] and to a dynamic condition with active driving [C3 vs. C1 accuracy; glaucoma: 2 (2-6) vs.

Controls

1 (0.50-2); p = 0.02]. Overall, drivers with glaucoma exhibited greater cognitive workload than controls (p = 0.02). Conclusion: Cognitive demand disproportionately affects functional visual field performance in drivers with glaucoma. Our results may inform the development of a performance-based visual field test for drivers with glaucoma.

SUBMITTER: Gangeddula V

PROVIDER: S-EPMC5582159 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Effect of Cognitive Demand on Functional Visual Field Performance in Senior Drivers with Glaucoma.

Gangeddula Viswa V Ranchet Maud M Akinwuntan Abiodun E AE Bollinger Kathryn K Devos Hannes H

Frontiers in aging neuroscience 20170830

<b>Purpose:</b> To investigate the effect of cognitive demand on functional visual field performance in drivers with glaucoma. <b>Method:</b> This study included 20 drivers with open-angle glaucoma and 13 age- and sex-matched controls. Visual field performance was evaluated under different degrees of cognitive demand: a static visual field condition (C1), dynamic visual field condition (C2), and dynamic visual field condition with active driving (C3) using an interactive, desktop driving simulat ...[more]

PMID: 28912712

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Project description:PurposeTo 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.DesignRetrospective cohort study.ParticipantsVisual fields of 44 503 eyes from 26 130 participants.MethodsEyes 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 measuresPredictive models for GHT results reversal using VF features.ResultsFor 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%.ConclusionsUsing VF features may predict the GHT results reversal to WNL after 2 consecutive ONL results.

Project description:PurposeTo quantify the central visual field (VF) loss patterns in glaucoma using artificial intelligence.DesignRetrospective study.ParticipantsVFs of 8712 patients with 13 951 Humphrey 10-2 test results from 13 951 eyes for cross-sectional analyses, and 824 patients with at least 5 reliable 10-2 test results at 6-month intervals or more from 1191 eyes for longitudinal analyses.MethodsTotal deviation values were used to determine the central VF patterns using the most recent 10-2 test results. A 24-2 VF within a 3-month window of the 10-2 tests was used to stage eyes into mild, moderate, or severe functional loss using the Hodapp-Anderson-Parrish scale at baseline. Archetypal analysis was applied to determine the central VF patterns. Cross-validation was performed to determine the optimal number of patterns. Stepwise regression was applied to select the optimal feature combination of global indices, average baseline decomposition coefficients from central VFs archetypes, and other factors to predict central VF mean deviation (MD) slope based on the Bayesian information criterion (BIC).Main outcome measuresThe central VF patterns stratified by severity stage based on 24-2 test results and a model to predict the central VF MD change over time using baseline test results.ResultsFrom cross-sectional analysis, 17 distinct central VF patterns were determined for the 13 951 eyes across the spectrum of disease severity. These central VF patterns could be divided into isolated superior loss, isolated inferior loss, diffuse loss, and other loss patterns. Notably, 4 of the 5 patterns of diffuse VF loss preserved the less vulnerable inferotemporal zone, whereas they lost most of the remaining more vulnerable zone described by the Hood model. Inclusion of coefficients from central VF archetypical patterns strongly improved the prediction of central VF MD slope (BIC decrease, 35; BIC decrease of >6 indicating strong prediction improvement) than using only the global indices of 2 baseline VF results. Eyes with baseline VF results with more superonasal and inferonasal loss were more likely to show worsening MD over time.ConclusionsWe quantified central VF patterns in glaucoma, which were used to improve the prediction of central VF worsening compared with using only global indices.

Dataset Information

Effect of Cognitive Demand on Functional Visual Field Performance in Senior Drivers with Glaucoma.

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Publications

Effect of Cognitive Demand on Functional Visual Field Performance in Senior Drivers with Glaucoma.

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