Purpose

To evaluate the association between the rates of progressive visual field loss and the occurrence of depressive symptoms in patients with glaucoma followed over time.

Design

Prospective observational cohort study.

Participants

The study included 204 eyes of 102 patients with glaucomatous visual field defects on standard automated perimetry (SAP).

Methods

All patients had Geriatric Depression Scale (GDS) questionnaires and visual field tests obtained over a mean follow-up time of 2.2±0.6 years. Change in depressive symptoms was assessed by calculating the difference between GDS scores at the last follow-up visit from those at baseline. Rates of visual field loss were assessed by SAP. An integrated binocular visual field was estimated from the monocular SAP tests, and rates of change in mean sensitivity (MS) over time were obtained from linear mixed models. Regression models were used to investigate the association between progressive visual field loss and changes in depressive symptoms, adjusting for potentially confounding clinical and socioeconomic variables.

Main outcome measures

The association between rates of change in binocular SAP MS and change in GDS questionnaire scores.

Results

There was a significant correlation between change in the GDS scores during follow-up and change in binocular SAP sensitivity. Each 1 decibel (dB)/year change in binocular SAP MS was associated with a change of 2.0 units in the GDS scores during the follow-up period (P = 0.025). In a multivariable model adjusting for baseline disease severity, change in visual acuity, age, gender, race, Montreal Cognitive Assessment score, education, income, and comorbidity index, each 1 dB/year change in binocular SAP MS was associated with a change of 3.0 units in the GDS score (P = 0.019).

Conclusions

Faster visual field progression was associated with the occurrence of depressive symptoms in patients with glaucoma.

Purpose

We examined the relationship between relative afferent pupillary defects (RAPDs) and macular structural damage measured by macular thickness and macular ganglion cell-inner plexiform layer (mGCIPL) thickness in patients with glaucoma.

Methods

A cross-sectional study was done of 106 glaucoma patients and 85 healthy individuals from the Diagnostic Innovations in Glaucoma Study. All subjects underwent standard automated perimetry (SAP) and optic nerve and macular imaging using Cirrus Spectral Domain Optical Coherence Tomography (SDOCT). Glaucoma was defined as repeatable abnormal SAP or progressive glaucomatous changes on stereo photographs. Pupil responses were assessed using an automated pupillometer, which records the magnitude of RAPD (RAPD score), with additional RAPD scores recorded for each of a series of colored stimuli (blue, red, green, and yellow). The relationship between RAPD score and intereye differences (right minus left eye) in circumpapillary retinal nerve fiber layer (cpRNFL) thickness, mGCIPL, macular thickness, and SAP mean deviation (MD), was examined using linear regression.

Results

There was fair correlation between RAPD score and asymmetric macular structural damage measured by intereye difference in mGCIPL thickness (R(2) = 0.285, P < 0.001). The relationship between RAPD score and intereye difference in macular thickness was weaker (R(2) = 0.167, P < 0.001). Intereye difference in cpRNFL thickness (R(2) = 0.350, P < 0.001) and SAP MD (R(2) = 0.594, P < 0.001) had stronger association with RAPD scores compared to intereye difference in mGCIPL and macular thickness.

Conclusions

Objective assessment of pupillary responses using a pupillometer was associated with asymmetric macular structural damage in patients with glaucoma.

Purpose

The purpose of this study was to evaluate the effect of disease severity and number of tests acquired during follow-up on the relative odds of identifying progression by structural or functional tests in glaucoma.

Methods

This was an observational cohort study involving 462 eyes of 305 patients with glaucoma and 62 eyes of 49 healthy subjects. Glaucoma patients and healthy subjects were followed for an average of 3.6 ± 0.9 and 3.8 ± 0.9 years, with a median (interquantile range) of 8 (6-9) and 7 (6-8) visits, respectively. At each visit, subjects underwent visual field assessment with standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) evaluation by spectral-domain optical coherence tomography (SD-OCT). Slopes of change in SAP mean sensitivity and OCT RNFL thickness over time were estimated by linear regression using progressively cumulative visits over time. Cutoff values for age-related expected rates of change for each test were obtained from the healthy group. Progression by SD-OCT and/or SAP was determined if the slope of change was statistically significant and also lower (faster) than the fifth percentile cutoff calculated from the healthy group. A generalized estimating equation logistic regression model was used to evaluate the relative odds of progressing by OCT versus SAP in glaucoma eyes.

Results

Eyes with less severe disease at baseline had a higher chance of being detected as progressing by SD-OCT but not by SAP, whereas an increase in disease severity at baseline increased the chance that the eye would be detected as progressing by SAP but not SD-OCT. Each 1 dB higher MD was associated with a 5% increase in the odds of detecting progression by SD-OCT versus SAP (odds ratio = 1.05 per 1 dB; 95% confidence interval: 1.01-1.09; P = 0.005).

Conclusions

The ability to detect glaucoma progression by SAP versus SD-OCT is significantly influenced by the stage of disease. Our results may provide useful information for guiding clinicians on the relative utility of these tests for detecting change throughout the disease continuum.

BACKGROUND: Evaluating longitudinal changes in gliomas is a time-intensive process with significant interrater variability. Automated segmentation could reduce interrater variability and increase workflow efficiency for assessment of treatment response. We sought to evaluate whether neural networks would be comparable to expert assessment of pre- and posttreatment diffuse gliomas tissue subregions including resection cavities. METHODS: A retrospective cohort of 647 MRIs of patients with diffuse gliomas (average 55.1 years; 29%/36%/34% female/male/unknown; 396 pretreatment and 251 posttreatment, median 237 days post-surgery) from 7 publicly available repositories in The Cancer Imaging Archive were split into training (536) and test/generalization (111) samples. T1, T1-post-contrast, T2, and FLAIR images were used as inputs into a 3D nnU-Net to predict 3 tumor subregions and resection cavities. We evaluated the performance of networks trained on pretreatment training cases (Pre-Rx network), posttreatment training cases (Post-Rx network), and both pre- and posttreatment cases (Combined networks). RESULTS: Segmentation performance was as good as or better than interrater reliability with median dice scores for main tumor subregions ranging from 0.82 to 0.94 and strong correlations between manually segmented and predicted total lesion volumes (0.94 < R 2 values < 0.98). The Combined network performed similarly to the Pre-Rx network on pretreatment cases and the Post-Rx network on posttreatment cases with fewer false positive resection cavities (7% vs 59%). CONCLUSIONS: Neural networks that accurately segment pre- and posttreatment diffuse gliomas have the potential to improve response assessment in clinical trials and reduce provider burden and errors in measurement.