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Glaucomatous retinal nerve fiber layer thickness loss is associated with slower reaction times under a divided attention task.

ABSTRACT: To examine the relationship between glaucomatous structural damage and ability to divide attention during simulated driving.Cross-sectional observational study.Hamilton Glaucoma Center, University of California San Diego.Total of 158 subjects from the Diagnostic Innovations in Glaucoma Study, including 82 with glaucoma and 76 similarly aged controls.Ability to divide attention was investigated by measuring reaction times to peripheral stimuli (at low, medium, or high contrast) while concomitantly performing a central driving task (car following or curve negotiation). All subjects had standard automated perimetry (SAP) and optical coherence tomography was used to measure retinal nerve fiber layer (RNFL) thickness. Cognitive ability was assessed using the Montreal Cognitive Assessment and subjects completed a driving history questionnaire.Reaction times to the driving simulator divided attention task.The mean reaction times to the low-contrast stimulus were 1.05 s and 0.64 s in glaucoma and controls, respectively, during curve negotiation (P < .001), and 1.19 s and 0.77 s (P = .025), respectively, during car following. There was a nonlinear relationship between reaction times and RNFL thickness in the better eye. RNFL thickness remained significantly associated with reaction times even after adjusting for age, SAP mean deviation in the better eye, cognitive ability, and central driving task performance.Although worse SAP sensitivity was associated with worse ability to divide attention, RNFL thickness measurements provided additional information. Information from structural tests may improve our ability to determine which patients are likely to have problems performing daily activities, such as driving.

SUBMITTER: Tatham AJ

PROVIDER: S-EPMC4515218 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Glaucomatous retinal nerve fiber layer thickness loss is associated with slower reaction times under a divided attention task.

Tatham Andrew J AJ Boer Erwin R ER Rosen Peter N PN Della Penna Mauro M Meira-Freitas Daniel D Weinreb Robert N RN Zangwill Linda M LM Medeiros Felipe A FA

American journal of ophthalmology 20140725 5

<h4>Purpose</h4>To examine the relationship between glaucomatous structural damage and ability to divide attention during simulated driving.<h4>Design</h4>Cross-sectional observational study.<h4>Methods</h4><h4>Setting</h4>Hamilton Glaucoma Center, University of California San Diego.<h4>Patient population</h4>Total of 158 subjects from the Diagnostic Innovations in Glaucoma Study, including 82 with glaucoma and 76 similarly aged controls.<h4>Observation procedure</h4>Ability to divide attention ...[more]

PMID: 25068641

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Project description:PurposeCircumpapillary retinal nerve fiber layer thickness (RNFLT) measurement aids in the clinical diagnosis of glaucoma. Spectral domain optical coherence tomography (SD-OCT) machines measure RNFLT and provide normative color-coded plots. In this retrospective study, we investigate whether normative percentiles of RNFLT (pRNFLT) from Spectralis SD-OCT improve prediction of glaucomatous visual field loss over raw RNFLT.MethodsA longitudinal database containing OCT scans and visual fields from Massachusetts Eye & Ear glaucoma clinic patients was generated. Reliable OCT-visual field pairs were selected. Spectralis OCT normative distributions were extracted from machine printouts. Supervised machine learning models compared predictive performance between pRNFLT and raw RNFLT inputs. Regional structure-function associations were assessed with univariate regression to predict mean deviation (MD). Multivariable classification predicted MD, pattern standard deviation, MD change per year, and glaucoma hemifield test.ResultsThere were 3016 OCT-visual field pairs that met the reliability criteria. Spectralis norms were found to be independent of age, sex, and ocular magnification. Regional analysis showed significant decrease in R2 from pRNFLT models compared to raw RNFLT models in inferotemporal sectors, across multiple regressors. In multivariable classification, there were no significant improvements in area under the curve of receiver operating characteristic curve (ROC-AUC) score with pRNFLT models compared to raw RNFLT models.ConclusionsOur results challenge the assumption that normative percentiles from OCT machines improve prediction of glaucomatous visual field loss. Raw RNFLT alone shows strong prediction, with no models presenting improvement by the manufacturer norms. This may result from insufficient patient stratification in tested norms.Translational relevanceUnderstanding correlation of normative databases to visual function may improve clinical interpretation of OCT data.

Project description:PurposeTo develop and evaluate a deep learning system for differentiating between eyes with and without glaucomatous visual field damage (GVFD) and predicting the severity of GFVD from spectral domain OCT (SD OCT) optic nerve head images.DesignEvaluation of a diagnostic technology.ParticipantsA total of 9765 visual field (VF) SD OCT pairs collected from 1194 participants with and without GVFD (1909 eyes).MethodsDeep learning models were trained to use SD OCT retinal nerve fiber layer (RNFL) thickness maps, RNFL en face images, and confocal scanning laser ophthalmoscopy (CSLO) images to identify eyes with GVFD and predict quantitative VF mean deviation (MD), pattern standard deviation (PSD), and mean VF sectoral pattern deviation (PD) from SD OCT data.Main outcome measuresDeep learning models were compared with mean RNFL thickness for identifying GVFD using area under the curve (AUC), sensitivity, and specificity. For predicting MD, PSD, and mean sectoral PD, models were evaluated using R2 and mean absolute error (MAE).ResultsIn the independent test dataset, the deep learning models based on RNFL en face images achieved an AUC of 0.88 for identifying eyes with GVFD and 0.82 for detecting mild GVFD significantly (P < 0.001) better than using mean RNFL thickness measurements (AUC = 0.82 and 0.73, respectively). Deep learning models outperformed standard RNFL thickness measurements in predicting all quantitative VF metrics. In predicting MD, deep learning models based on RNFL en face images achieved an R2 of 0.70 and MAE of 2.5 decibels (dB) compared with 0.45 and 3.7 dB for RNFL thickness measurements. In predicting mean VF sectoral PD, deep learning models achieved high accuracy in the inferior nasal (R2 = 0.60) and superior nasal (R2 = 0.67) sectors, moderate accuracy in inferior (R2 = 0.26) and superior (R2 = 0.35) sectors, and lower accuracy in the central (R2 = 0.15) and temporal (R2 = 0.12) sectors.ConclusionsDeep learning models had high accuracy in identifying eyes with GFVD and predicting the severity of functional loss from SD OCT images. Accurately predicting the severity of GFVD from SD OCT imaging can help clinicians more effectively individualize the frequency of VF testing to the individual patient.

Project description:PurposeDescribe a new method to analyze retinal nerve fiber layer (RNFL) thickness maps.DesignCross-sectional study.SubjectsRNFL thickness maps of healthy and glaucomatous eyes.MethodsOptical coherence tomography (OCT) RNFL raster scans from 98 healthy and 50 glaucomatous eyes were analyzed. The RNFL thickness maps were separated into superficial (SNFL) and deep (DNFL) slabs through a partial thickness plane set at the modal thickness (mode). Association between mode and OCT signal strength (SS), age, axial length, and visual field mean deviation (VFMD) was tested (Pearson coefficient, r). Thicknesses of inferior and superior SNFL regions (i-,s-SNFL), and inferior, superior, nasal, and temporal DNFL regions (i-,s-,n-,t-DNFL) were calculated. The regions thicknesses were compared between healthy and glaucomatous eyes (t-test) and between glaucomatous eyes with early, moderate, and severe disease (ANOVA and linear regressions of thickness on VFMD). Diagnostic accuracy and correlation with VFMD of RNFL regions thicknesses were calculated as the area under the receiver operating characteristic curve (AUC) and Pearson r, respectively. P<0.05 was considered significant.Main outcomeThickness of regions in SNFL and DNFL slabs.ResultsThe mode was not associated with SS, age, axial length, or VFMD, it circumscribed the thicker RNFL around the optic disc of healthy and glaucomatous eyes, and it was used to separate the SNFL and DNFL slabs of RNFL thickness maps. The thickness of the SNFL slab was less in glaucomatous eyes than in healthy eyes (P<0.001). S-SNFL and i-SNFL thicknesses (respectively) were 86.0±8.2μm and 87.3±9.6μm in healthy eyes vs. 66.1±9.1μm and 63.4±8.2μm in glaucomatous eyes (P<0.001 for both). The thickness of the DNFL slab was similar between groups (P=0.19). T-DNFL thickness was 37.0±5.3μm in healthy eyes vs. 33.9±5.0μm in glaucomatous eyes (P<0.001); thicknesses of all other DNFL regions were similar. The SNFL regions only thinned with progressively worse glaucoma severity, had excellent AUCs (AUC≥0.95, P<0.001), and correlated strongly with VFMD (r≥0.60, P<0.001).ConclusionsGlaucomatous RNFL thinning is predominantly detected within a slab with thickness greater than the mode. SNFL thickness has great AUC and correlation with VFMD in glaucomatous eyes. The usefulness for diagnosis and monitoring of glaucoma needs further study.

Project description:PURPOSE:To investigate the role of sex on retinal nerve fiber layer (RNFL) thickness at 768 circumpapillary locations based on OCT findings. DESIGN:Population-based cross-sectional study. PARTICIPANTS:We investigated 5646 eyes of 5646 healthy participants from the Leipzig Research Centre for Civilization Diseases (LIFE)-Adult Study of a predominantly white population. METHODS:All participants underwent standardized systemic assessments and ocular imaging. Circumpapillary RNFL (cRNFL) thickness was measured at 768 points equidistant from the optic nerve head using spectral-domain OCT (Spectralis; Heidelberg Engineering, Heidelberg, Germany). To control ocular magnification effects, the true scanning radius was estimated by scanning focus. Student t test was used to evaluate sex differences in cRNFL thickness globally and at each of the 768 locations. Multivariable linear regression and analysis of variance were used to evaluate individual contributions of various factors to cRNFL thickness variance. MAIN OUTCOME MEASURES:Difference in cRNFL thickness between males and females. RESULTS:Our population consisted of 54.8% females. The global cRNFL thickness was 1 ?m thicker in females (P < 0.001). However, detailed analysis at each of the 768 locations revealed substantial location specificity of the sex effects, with RNFL thickness difference ranging from -9.98 to +8.00 ?m. Females showed significantly thicker RNFLs in the temporal, superotemporal, nasal, inferonasal, and inferotemporal regions (43.6% of 768 locations), whereas males showed significantly thicker RNFLs in the superior region (13.2%). The results were similar after adjusting for age, body height, and scanning radius. The superotemporal and inferotemporal RNFL peaks shifted temporally in females by 2.4° and 1.9°, respectively. On regions with significant sex effects, sex explained more RNFL thickness variance than age, whereas the major peak locations and interpeak angle explained most of the RNFL thickness variance unexplained by sex. CONCLUSIONS:Substantial sex effects on cRNFL thickness were found at 56.8% of all 768 circumpapillary locations, with specific patterns for different sectors. Over large regions, sex was at least as important in explaining the cRNFL thickness variance as was age, which is well established to have a substantial impact on cRNFL thickness. Including sex in the cRNFL thickness norm could therefore improve glaucoma diagnosis and monitoring.

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Glaucomatous retinal nerve fiber layer thickness loss is associated with slower reaction times under a divided attention task.

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Glaucomatous retinal nerve fiber layer thickness loss is associated with slower reaction times under a divided attention task.

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