Rates of Glaucomatous Structural and Functional Change From a Large Clinical Population: The Duke Glaucoma Registry Study.

PURPOSE: To investigate rates of structural and functional change in a large clinical population of glaucoma and glaucoma suspect patients. DESIGN: Retrospective cohort. METHODS: Twenty-nine thousand five hundred forty-eight spectral-domain optical coherence tomography (OCT) and 19,812 standard automated perimetry (SAP) tests from 6138 eyes of 3669 patients with ≥6 months of follow-up, 2 good quality spectral-domain OCT peripapillary retinal nerve fiber layer scans, and 2 reliable SAP tests were included. Data were extracted from the Duke Glaucoma Registry, a large database of electronic health records of patients from the Duke Eye Center and satellite clinics. Rates of change for the 2 metrics were obtained using linear mixed models, categorized according to pre-established cutoffs, and analyzed according to the severity of the disease. RESULTS: Average rates of change were -0.73 ± 0.80 µm per year for global retinal nerve fiber layer thickness and -0.09 ± 0.36 dB per year for SAP mean deviation. More than one quarter (26.6%) of eyes were classified as having at least a moderate rate of change by spectral-domain OCT vs 9.1% by SAP (P < .001). In eyes with severe disease, 31.6% were classified as progressing at moderate or faster rates by SAP vs 26.5% by spectral-domain OCT (P = .055). Most eyes classified as fast by spectral-domain OCT were classified as slow by SAP and vice versa. CONCLUSION: Although most patients under routine care had slow rates of progression, a substantial proportion had rates that could potentially result in major losses if sustained over time. Both structural and functional tests should be used to monitor glaucoma, and spectral-domain OCT still has a relevant role in detecting fast progressors in advanced disease.

The Relationship Between Asymmetries of Corneal Properties and Rates of Visual Field Progression in Glaucoma Patients.

PRéCIS:: In this study, asymmetries in corneal hysteresis (CH) between eyes of glaucoma patients were significantly associated with asymmetries in rates of visual field loss, suggesting a role of hysteresis as a risk factor for disease progression. PURPOSE: The purpose of this study was to investigate the relationship between asymmetries in rates of glaucoma progression and asymmetries of corneal properties between eyes of subjects with primary open-angle glaucoma. PARTICIPANTS AND METHODS: This prospective study followed 126 binocular subjects with glaucoma for an average of 4.3±0.8 years. CH was measured at baseline using the Ocular Response Analyzer. Standard automated perimetry (SAP) and intraocular pressure were measured at baseline and every 6 months. Rates of visual field progression were calculated using ordinary least square regression of SAP mean deviation (MD) values over time for each eye. Eyes were defined as "better" and "worse" based on the slopes of SAP MD. Pearson correlation test, and univariable and multivariable regression models were used to investigate the relationship between inter-eye asymmetry in CH and central corneal thickness and inter-eye differences in rates of visual field progression. RESULTS: Only asymmetry of CH was significantly associated with the asymmetry in SAP MD rates of change between eyes (r=0.22; P=0.01). In a multivariable model adjusting for age, race, central corneal thickness, mean intraocular pressure and baseline disease severity, CH asymmetry remained significantly associated with asymmetric progression (P=0.032). CONCLUSION: CH asymmetry between eyes was associated with asymmetry on rates of visual field change, providing further support for the role of CH as a risk factor for glaucoma progression.

Comparing the Rule of 5 to Trend-based Analysis for Detecting Glaucoma Progression on OCT.

PURPOSE: The rule of 5 is a simple rule for detecting retinal nerve fiber layer (RNFL) change on spectral-domain OCT (SD-OCT), in which a loss of 5 µm of global RNFL on a follow-up test is considered evidence of significant change when compared with the baseline. The rule is based on short-term test-retest variability of SD-OCT and is often used in clinical practice. The purpose of this study was to compare the rule of 5 with trend-based analysis of global RNFL thickness over time for detecting glaucomatous progression. DESIGN: Prospective cohort. PARTICIPANTS: A total of 300 eyes of 210 glaucoma subjects followed for an average of 5.4±1.5 years with a median of 11 (interquartile range, 7-14) visits. METHODS: Trend-based analysis was performed by ordinary least-squares (OLS) linear regression of global RNFL thickness over time. For estimation of specificity, false-positives were obtained by assessing for progression on series of randomly permutated follow-up visits for each eye, which removes any systematic trend over time. The specificity of trend-based analysis was matched to that of the rule of 5 to allow meaningful comparison of the "hit rate," or the proportion of glaucoma eyes categorized as progressing at each time point, using the original sequence of visits. MAIN OUTCOME MEASURES: Comparison between hit rates of trend-analysis versus rule of 5 at matched specificity. RESULTS: After 5 years, the simple rule of 5 identified 37.5% of eyes as progressing at a specificity of 81.1%. At the same specificity, the hit rate for trend-based analysis was significantly greater than that of the rule of 5 (62.9% vs. 37.5%; P < 0.001). If the rule of 5 was required to be repeatable on a consecutive test, specificity improved to 93.4%, but hit rate decreased to 21.0%. At this higher specificity, trend-based analysis still had a significantly greater hit rate than the rule of 5 (47.4% vs. 21.0%, respectively; P < 0.001). CONCLUSIONS: Trend-based analysis was superior to the simple rule of 5 for identifying progression in glaucoma eyes and should be preferred as a method for longitudinal assessment of global SD-OCT RNFL change over time.

Quantification of Retinal Nerve Fibre Layer Thickness on Optical Coherence Tomography with a Deep Learning Segmentation-Free Approach.

This study describes a segmentation-free deep learning (DL) algorithm for measuring retinal nerve fibre layer (RNFL) thickness on spectral-domain optical coherence tomography (SDOCT). The study included 25,285 B-scans from 1,338 eyes of 706 subjects. Training was done to predict RNFL thickness from raw unsegmented scans using conventional RNFL thickness measurements from good quality images as targets, forcing the DL algorithm to learn its own representation of RNFL. The algorithm was tested in three different sets: (1) images without segmentation errors or artefacts, (2) low-quality images with segmentation errors, and (3) images with other artefacts. In test set 1, segmentation-free RNFL predictions were highly correlated with conventional RNFL thickness (r = 0.983, P < 0.001). In test set 2, segmentation-free predictions had higher correlation with the best available estimate (tests with good quality taken in the same date) compared to those from the conventional algorithm (r = 0.972 vs. r = 0.829, respectively; P < 0.001). Segmentation-free predictions were also better in test set 3 (r = 0.940 vs. r = 0.640, P < 0.001). In conclusion, a novel segmentation-free algorithm to extract RNFL thickness performed similarly to the conventional method in good quality images and better in images with errors or other artefacts.

Comparison of Short- And Long-Term Variability in Standard Perimetry and Spectral Domain Optical Coherence Tomography in Glaucoma.

PURPOSE: To assess short- and long-term variability on standard automated perimetry (SAP) and spectral domain optical coherence tomography (SD-OCT) in glaucoma. DESIGN: Prospective cohort. METHODS: Ordinary least squares linear regression of SAP mean deviation (MD) and SD-OCT global retinal nerve fiber layer (RNFL) thickness were fitted over time for sequential tests conducted within 5 weeks (short-term testing) and annually (long-term testing). Residuals were obtained by subtracting the predicted and observed values, and each patient's standard deviation (SD) of the residuals was used as a measure of variability. Wilcoxon signed-rank test was performed to test the hypothesis of equality between short- and long-term variability. RESULTS: A total of 43 eyes of 43 glaucoma subjects were included. Subjects had a mean 4.5 ± 0.8 SAP and OCT tests for short-term variability assessment. For long-term variability, the same number of tests were performed and results annually collected over an average of 4.0 ± 0.8 years. The average SD of the residuals was significantly higher in the long-term than in the short-term period for both tests: 1.05 ± 0.70 dB vs. 0.61 ± 0.34 dB, respectively (P < 0.001) for SAP MD and 1.95 ± 1.86 µm vs. 0.81 ± 0.56 µm, respectively (P < 0.001) for SD-OCT RNFL thickness. CONCLUSIONS: Long-term variability was higher than short-term variability on SD-OCT and SAP. Because current event-based algorithms for detection of glaucoma progression on SAP and SD-OCT have relied on short-term variability data to establish their normative databases, these algorithms may be underestimating the variability in the long-term and thus may overestimate progression over time.

Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs.

PURPOSE: To compare the diagnostic performance of human gradings vs predictions provided by a machine-to-machine (M2M) deep learning (DL) algorithm trained to quantify retinal nerve fiber layer (RNFL) damage on fundus photographs. DESIGN: Evaluation of a machine learning algorithm. METHODS: An M2M DL algorithm trained with RNFL thickness parameters from spectral-domain optical coherence tomography was applied to a subset of 490 fundus photos of 490 eyes of 370 subjects graded by 2 glaucoma specialists for the probability of glaucomatous optical neuropathy (GON), and estimates of cup-to-disc (C/D) ratios. Spearman correlations with standard automated perimetry (SAP) global indices were compared between the human gradings vs the M2M DL-predicted RNFL thickness values. The area under the receiver operating characteristic curves (AUC) and partial AUC for the region of clinically meaningful specificity (85%-100%) were used to compare the ability of each output to discriminate eyes with repeatable glaucomatous SAP defects vs eyes with normal fields. RESULTS: The M2M DL-predicted RNFL thickness had a significantly stronger absolute correlation with SAP mean deviation (rho=0.54) than the probability of GON given by human graders (rho=0.48; P < .001). The partial AUC for the M2M DL algorithm was significantly higher than that for the probability of GON by human graders (partial AUC = 0.529 vs 0.411, respectively; P = .016). CONCLUSION: An M2M DL algorithm performed as well as, if not better than, human graders at detecting eyes with repeatable glaucomatous visual field loss. This DL algorithm could potentially replace human graders in population screening efforts for glaucoma.

Detecting Retinal Nerve Fibre Layer Segmentation Errors on Spectral Domain-Optical Coherence Tomography with a Deep Learning Algorithm.

In this study we developed a deep learning (DL) algorithm that detects errors in retinal never fibre layer (RNFL) segmentation on spectral-domain optical coherence tomography (SDOCT) B-scans using human grades as the reference standard. A dataset of 25,250 SDOCT B-scans reviewed for segmentation errors by human graders was randomly divided into validation plus training (50%) and test (50%) sets. The performance of the DL algorithm was evaluated in the test sample by outputting a probability of having a segmentation error for each B-scan. The ability of the algorithm to detect segmentation errors was evaluated with the area under the receiver operating characteristic (ROC) curve. Mean DL probabilities of segmentation error in the test sample were 0.90 ± 0.17 vs. 0.12 ± 0.22 (P < 0.001) for scans with and without segmentation errors, respectively. The DL algorithm had an area under the ROC curve of 0.979 (95% CI: 0.974 to 0.984) and an overall accuracy of 92.4%. For the B-scans with severe segmentation errors in the test sample, the DL algorithm was 98.9% sensitive. This algorithm can help clinicians and researchers review images for artifacts in SDOCT tests in a timely manner and avoid inaccurate diagnostic interpretations.

Face memory deficits in subjects with eye diseases: a comparative analysis between glaucoma and age-related macular degeneration patients from a developing country.

PURPOSE: Older people present significant declines in face recognition with age. Spatial vision (high-contrast acuity) and age are the best predictors of face recognition. Visual disabilities are more common in the older population due to aging eye diseases. The purpose of the study was to compare the face recognition memory deficit between primary open angle glaucoma (POAG) and age-related macular degeneration (ARMD) patients living in a developing country. METHODS: This was a cross-sectional, observational study. The sample comprised 64 patients with POAG, 48 with ARMD, and 52 controls. All groups were matched for age, gender, comorbidity, and ethnic distribution. Evidence of cognitive impairment was ruled out and subjects with even mild cognitive impairment were not included in the study. After a complete eye examination including measurement of the best-corrected visual acuity, fundus evaluation, and automated visual field, all subjects underwent the Cambridge face memory test (CFMT). CFMT score in percentage (%) was the main outcome measure and data were compared with ANOVA. RESULTS: The mean age was 66.6 ± 9.2, 69.8 ± 9.3, and 63.4 ± 7.3 years, for POAG, ARMD, and controls, respectively (P = 0.152). Gender, ethnicity, and comorbidity were evenly distributed among the groups. The CFMT score was 53.3 ± 15.2%, 49.8 ± 14.2%, and 62.1 ± 15.9% for POAG, ARMD, and controls, respectively (P < 0.001). CONCLUSION: ARMD and POAG patients have higher face recognition memory deficit as compared to normal controls. This might be due to a visual disability.

A Comparative Analysis of the Fear of Falling Between Glaucoma and Age-Related Macular Degeneration Patients From a Developing Country.

PURPOSE: Falls are very prevalent in the older population. Visually impaired elderly patients are prone to falls as the result of visual loss and ageing. The purpose of the study was to compare the fear of falling (FoF) between primary open angle glaucoma (POAG) and age-related macular degeneration (ARMD) patients who live in a developing country. METHODS: This was a cross-sectional observational study. After a complete eye examination including measurement of best-corrected visual acuity, ophthalmoscopy, and automated visual field, all subjects completed the Fall Efficacy Scale International Brazil (FES-I-Brazil) questionnaire. RESULTS: The sample comprised 64 patients with POAG, 48 with ARMD, and 52 controls. All groups were matched for age, sex, comorbidity, and ethnic distribution. The FES-I score was 24.6 ± 8.7, 25.3 ± 6.3, and 24.2 ± 7.7 for glaucoma, ARMD, and controls, respectively (P = 0.894). A post hoc analysis comparing all subjects with advanced visual field defect (mean deviation [MD] < -12 dB) revealed a higher FES-I score in ARMD patients as compared to POAG ones (46.2 ± 16.8 and 24.0 ± 7.7 for ARMD and POAG, respectively, P < 0.001). CONCLUSION: In this cohort of elderly subjects with eye diseases, the FoF was similar among groups; however, ARMD patients with more compromised visual field had higher FoF as compared to POAG patients and controls. TRANSLATIONAL RELEVANCE: A high rate of fear of falling exists in ARMD patients with compromised visual field. This finding may be useful in developing multidimensional strategies to decrease fear of falling and improve quality of life in older persons living in a developing country.