Dataset Information

Assessment of Time Lag Between Blood Flow, Retinal Nerve Fiber Layer Thickness and Visual Field Sensitivity Changes in Glaucoma.

ABSTRACT:

Purpose

This study investigates the temporal relationship between blood flow changes and alterations in retinal nerve fiber layer thickness (RNFLT) and mean deviation (MD) in individuals with glaucoma.

Methods

Blood flow, measured by mean blur rate in optic nerve head vessels (MBRv) and tissues (MBRt) using laser speckle flowgraphy (LSFG)-NAVI, was analyzed using structural equation models (SEMs). SEMs assessed whether the previous rate of one parameter predicted the current rate of the other parameter, adjusted for its own rate in the previous time interval. Data from 345 eyes of 174 participants were gathered from visits every six months.

Results

Rates of change of both MBRv and MBRt were significantly predicted by their own rate in the previous time interval and by the rate of change of MD in the previous time interval (P < 0.001 and P = 0.043, respectively), but not by the rate of MD in the concurrent interval (P = 0.947 and P = 0.549), implying that changes in MD precede changes in blood flow. Rates of change of RNFLT were predicted by their own previous rate and the rate of change of MBRv and MBRt in either the previous interval (P = 0.002 and P = 0.008) or the concurrent interval (P = 0.001 and P = 0.018), suggesting that MBR may change before RNFLT.

Conclusions

The evidence supports a temporal sequence where MD changes precede blood flow changes, which, in turn, may precede alterations in RNFLT.

SUBMITTER: Higgins BE

PROVIDER: S-EPMC10996942 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

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Publications

Assessment of Time Lag Between Blood Flow, Retinal Nerve Fiber Layer Thickness and Visual Field Sensitivity Changes in Glaucoma.

Higgins Bethany E BE Cull Grant G Gardiner Stuart K SK

Investigative ophthalmology & visual science 20240401 4

<h4>Purpose</h4>This study investigates the temporal relationship between blood flow changes and alterations in retinal nerve fiber layer thickness (RNFLT) and mean deviation (MD) in individuals with glaucoma.<h4>Methods</h4>Blood flow, measured by mean blur rate in optic nerve head vessels (MBRv) and tissues (MBRt) using laser speckle flowgraphy (LSFG)-NAVI, was analyzed using structural equation models (SEMs). SEMs assessed whether the previous rate of one parameter predicted the current rate ...[more]

PMID: 38564193

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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 compare prospectively detection of progressive retinal nerve fiber layer thickness (RNFL) atrophy identified using time-domain optical coherence tomography with visual field progression using standard automated perimetry in glaucoma suspect and preperimetric glaucoma patients or perimetric glaucoma patients.DesignProspective, longitudinal clinical trial.MethodsEligible eyes with 2 years or more of follow-up underwent time-domain optical coherence tomography and standard automated perimetry every 6 months. The occurrence of visual field progression was defined as the first follow-up visit reaching a significant (P < .05) negative visual field index slope over time. RNFL progression or improvement was defined as a significant negative or positive slope over time, respectively. Specificity was defined as the number of eyes with neither progression nor improvement, divided by the number of eyes without progression. Cox proportional hazard ratios were calculated using univariate and multivariate models with RNFL loss as a time-dependent covariate.ResultsThree hundred ten glaucoma suspect and preperimetric glaucoma eyes and 177 perimetric glaucoma eyes were included. Eighty-nine eyes showed visual field progression and 101 eyes showed RNFL progression. The average time to detect visual field progression in those 89 eyes was 35 ± 13 months, and the average time to detect RNFL progression in those 101 eyes was 36 ± 13 months. In multivariate Cox models, average and superior RNFL losses were associated with subsequent visual field index loss in the entire cohort (every 10-μm loss; hazard ratio, 1.38; P = .03; hazard ratio, 1.20; P = .01; respectively). Among the entire cohort of 487 eyes, 42 had significant visual field index improvement and 55 had significant RNFL improvement (specificity, 91.4% and 88.7%, respectively).ConclusionsStructural progression is associated with functional progression in glaucoma suspect and glaucomatous eyes. Average and superior RNFL thickness may predict subsequent standard automated perimetry loss.

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.

Dataset Information

Assessment of Time Lag Between Blood Flow, Retinal Nerve Fiber Layer Thickness and Visual Field Sensitivity Changes in Glaucoma.

Purpose

Methods

Results

Conclusions

Publications

Assessment of Time Lag Between Blood Flow, Retinal Nerve Fiber Layer Thickness and Visual Field Sensitivity Changes in Glaucoma.

Similar Datasets

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