Visual Field Outcomes in the Primary Tube Versus Trabeculectomy Study.

PURPOSE: To describe visual field outcomes in the Primary Tube Versus Trabeculectomy (PTVT) Study. DESIGN: Cohort analysis. PARTICIPANTS: A total of 155 eyes (155 subjects) randomly assigned to treatment with tube shunt surgery (n = 84) or trabeculectomy with mitomycin C (n = 71). METHODS: The PTVT Study was a multicenter randomized clinical trial comparing the safety and efficacy of trabeculectomy and tube shunt surgery in eyes without previous intraocular surgery. Subjects underwent standard automated perimetry (SAP) at baseline and annually for 5 years. Standard automated perimetry tests were deemed reliable if the false-positive rate was ≤ 15%. Tests were excluded if visual acuity was ≤ 20/400 or loss of ≥ 2 Snellen lines from baseline because of a nonglaucomatous etiology. Linear mixed-effects models were used to compare rates of change in SAP mean deviation (MD) between the 2 groups. Intraocular pressure (IOP) control was assessed by percentage of visits with IOP < 18 mmHg and mean IOP. MAIN OUTCOME MEASURES: Rate of change in SAP MD during follow-up. RESULTS: A total of 730 SAP tests were evaluated (average of 4.7 tests per eye). The average SAP MD at baseline was -12.8 ± 8.3 decibels (dB) in the tube group and -12.0 ± 8.4 dB in the trabeculectomy group (P = 0.57). The mean rate of change in SAP MD was -0.32 ± 0.39 dB/year in the trabeculectomy group and -0.47 ± 0.43 dB/year in the tube group (P = 0.23). Eyes with mean IOP 14 to 17.5 mmHg had significantly faster rates of SAP MD loss compared with eyes with mean IOP < 14 mmHg (-0.59 ± 0.13 vs. -0.27 ± 0.08 dB/year; P = 0.012), and eyes with only 50% to 75% of visits with IOP < 18 mmHg had faster rates than those with 100% of visits with IOP < 18 mmHg (-0.90 ± 0.16 vs. -0.29 ± 0.08 dB/year; P < 0.001). Multivariable analysis identified older age and worse IOP control as risk factors for faster progression in both treatment groups. CONCLUSIONS: No statistically significant difference in mean rates of visual field change was observed between trabeculectomy and tube shunt surgery in the PTVT Study. Worse IOP control was significantly associated with faster rates of SAP MD loss during follow-up. Older patients were also at risk for faster progression.

Short-term Detection of Fast Progressors in Glaucoma: The Fast Progression Assessment through Clustered Evaluation (Fast-PACE) Study.

PURPOSE: To evaluate the performance of an intensive, clustered testing approach in identifying eyes with rapid glaucoma progression over 6 months in the Fast Progression Assessment through Clustered Evaluation (Fast-PACE) Study. DESIGN: Prospective cohort study. PARTICIPANTS: A total of 125 eyes from 65 primary open-angle glaucoma (POAG) subjects. METHODS: Subjects underwent 2 sets of 5 weekly visits (clusters) separated by an average of 6 months and then were followed with single visits every 6 months for an overall mean follow-up of 25 months (mean of 17 tests). Each visit consisted of testing with standard automated perimetry (SAP) 24-2 and 10-2, and spectral-domain OCT (SD-OCT). Progression was assessed using trend analyses of SAP mean deviation (MD) and retinal nerve fiber layer (RNFL) thickness. Generalized estimating equations were applied to adjust for correlations between eyes for confidence interval (CI) estimation and hypothesis testing. MAIN OUTCOME MEASURES: Diagnostic accuracy of the 6-month clustering period to identify progression detected during the overall follow-up. RESULTS: A total of 19 of 125 eyes (15%, CI, 9%-24%) progressed based on SAP 24-2 MD over the 6-month clustering period. A total of 14 eyes (11%, CI, 6%-20%) progressed on SAP 10-2 MD, and 16 eyes (13%, CI, 8%-21%) progressed by RNFL thickness, with 30 of 125 eyes (24%, CI, 16%-34%) progressing by function, structure, or both. Of the 35 eyes progressing during the overall follow-up, 25 had progressed during the 6-month clustering period, for a sensitivity of 71% (CI, 53%-85%). Of the 90 eyes that did not progress during the overall follow-up, 85 also did not progress during the 6-month period, for a specificity of 94% (CI, 88%-98%). Of the 14 eyes considered fast progressors by SAP 24-2, SAP 10-2, or SD-OCT during the overall follow-up, 13 were identified as progressing during the 6-month cluster period, for a sensitivity of 93% (CI, 66%-100%) for identifying fast progression with a specificity of 85% (CI, 77%-90%). CONCLUSIONS: Clustered testing in the Fast-PACE Study detected fast-progressing glaucoma eyes over 6 months. The methodology could be applied in clinical trials investigating interventions to slow glaucoma progression and may be of value for short-term assessment of high-risk subjects. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references in the Footnotes and Disclosures at the end of this article.

Validation of Rates of Mean Deviation Change as Clinically Relevant End Points for Glaucoma Progression.

PURPOSE: To investigate whether rates of standard automated perimetry (SAP) mean deviation (MD) over an initial 2-year follow-up period were predictive of events of visual field progression over an extended follow-up. DESIGN: Longitudinal, prospective, observational study. PARTICIPANTS: Two hundred forty-six eyes of 168 patients with glaucoma followed up every 6 months for up to 5 years. METHODS: Patients were required to have a minimum of 5 reliable SAP tests during the first 2 years of follow-up. Events of progression were evaluated using 2 methods: Guided Progression Analysis (GPA; Carl Zeiss Meditec, Inc) and a United States Food and Drug Administration (FDA)-suggested end point. The date of the first test showing progression after the first 2 years was considered to be the event date. Rates of change in SAP MD were calculated for the first 2 years of follow-up, and joint longitudinal survival models were used to assess the risk of faster initial MD loss for subsequent progression based on each event analysis. MAIN OUTCOME MEASURE: Risk of having an event of progression based on initial rates of SAP MD change. RESULTS: Fifty-six eye (22.8%) showed an event of progression by the GPA and 51 eyes (20.7%) did so by the FDA end point. Each 0.1-dB/year faster rate of SAP MD loss in the first 2 years was associated with a 26% increase in risk of a GPA progression end point developing (R2 = 76%) and 32% risk of an FDA-based end point developing (R2 = 83%). A reduction of 30% in the rate of MD change in the first 2 years was associated with a 20% reduction in the cumulative probability of a progression event developing over 5 years of follow-up. CONCLUSIONS: Rates of SAP MD change for eyes with glaucoma calculated over the initial 2 years of follow-up were strongly predictive of events of progression over subsequent follow-up. Our findings give support for the use of slopes of MD change as suitable end points of progression in clinical trials. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

Rates of Visual Field Change in Eyes With Optic Disc Drusen.

BACKGROUND: Optic disc drusen (ODD) are calcified deposits in the prelaminar portion of the optic nerve head. Although often asymptomatic, these deposits can cause progressive visual field defects and vision loss. The purpose of this study was to evaluate rates of functional loss in eyes with ODD and to investigate risk factors associated with rates of visual field progression. METHODS: This was a retrospective cohort study including 65 eyes of 43 patients with ODD from the Duke Ophthalmic Registry. All eyes had at least 12 months of follow-up and at least 3 reliable standard automated perimetry (SAP) tests. Linear mixed models were used to estimate rates of SAP mean deviation (MD) loss over time. Univariable and multivariable models were used to assess the effect of clinical variables and intraocular pressure (IOP) on rates of change. RESULTS: Subjects were followed for an average of 7.6 ± 5.3 years. The mean rate of SAP MD change was -0.23 ± 0.26 dB/year, ranging from -1.19 to 0.13 dB/year. Fifty-seven eyes (87.7%) had slow progression (slower than -0.5 dB/year), 6 eyes (9.2%) had moderate progression (between -0.5 dB/year and -1 dB/year), and 2 eyes (3.1%) had fast progression (faster than -1 dB/year). In multivariable models, older age and worse SAP MD at baseline were significantly associated with faster rates of change. Mean IOP was not associated with faster rates of MD change in both univariable and multivariable analyses. CONCLUSIONS: Most eyes with ODD had slow rates of visual field loss over time. Age and baseline severity were significantly associated with faster rates of visual field loss.

Blood Pressure and Glaucomatous Progression in a Large Clinical Population.

PURPOSE: To investigate the effect of systemic arterial blood pressure (BP) on rates of progressive structural damage over time in glaucoma. DESIGN: Retrospective cohort study. PARTICIPANTS: A total of 7501 eyes of 3976 subjects with glaucoma or suspected of glaucoma followed over time from the Duke Glaucoma Registry. METHODS: Linear mixed models were used to investigate the effects of BP on the rates of retinal nerve fiber layer (RNFL) loss from spectral-domain OCT (SD-OCT) over time. Models were adjusted for intraocular pressure (IOP), gender, race, diagnosis, central corneal thickness (CCT), follow-up time, and baseline disease severity. MAIN OUTCOME MEASURE: Effect of mean arterial pressure (MAP), systolic arterial pressure (SAP), and diastolic arterial pressure (DAP) on rates of RNFL loss over time. RESULTS: A total of 157 291 BP visits, 45 408 IOP visits, and 30 238 SD-OCT visits were included. Mean rate of RNFL change was -0.70 µm/year (95% confidence interval, -0.72 to -0.67 µm/year). In univariable models, MAP, SAP, and DAP during follow-up were not significantly associated with rates of RNFL loss. However, when adjusted for mean IOP during follow-up, each 10 mmHg reduction in mean MAP (-0.06 µm/year; P = 0.007) and mean DAP (-0.08 µm/year; P < 0.001) but not SAP (-0.01 µm/year; P = 0.355) was associated with significantly faster rates of RNFL thickness change over time. The effect of the arterial pressure metrics remained significant after additional adjustment for baseline age, diagnosis, sex, race, follow-up time, disease severity, and corneal thickness. CONCLUSIONS: When adjusted for IOP, lower MAP and DAP during follow-up were significantly associated with faster rates of RNFL loss, suggesting that levels of systemic BP may be a significant factor in glaucoma progression.

Corneal hysteresis: ready for prime time?

PURPOSE OF THE REVIEW: This review summarizes recent findings on corneal hysteresis, a biomechanical property of the cornea. Corneal hysteresis measurements can be easily acquired clinically and may serve as surrogate markers for biomechanical properties of tissues in the back of the eye, like the lamina cribrosa and peripapillary sclera, which may be related to the susceptibility to glaucomatous damage. RECENT FINDINGS: Several studies have provided evidence of the associations between corneal hysteresis and clinically relevant outcomes in glaucoma. Corneal hysteresis has been shown to be predictive of glaucoma development in eyes suspected of having the disease. For eyes already diagnosed with glaucoma, lower corneal hysteresis has been associated with higher risk of progression and faster rates of visual field loss over time. Such associations appear to be stronger than those for corneal thickness, suggesting that corneal hysteresis may be a more important predictive factor. Recent evidence has also shown that cornealcorrected intraocular pressure measurements may present advantages compared to conventional Goldmann tonometry in predicting clinically relevant outcomes in glaucoma. SUMMARY: Given the evidence supporting corneal hysteresis as an important risk factor for glaucoma development and its progression, practitioners should consider measuring corneal hysteresis in all patients at risk for glaucoma, as well as in those already diagnosed with the disease.

Detection of Progressive Glaucomatous Optic Nerve Damage on Fundus Photographs with Deep Learning.

PURPOSE: To investigate whether predictions of retinal nerve fiber layer (RNFL) thickness obtained from a deep learning model applied to fundus photographs can detect progressive glaucomatous changes over time. DESIGN: Retrospective cohort study. PARTICIPANTS: Eighty-six thousand one hundred twenty-three pairs of color fundus photographs and spectral-domain (SD) OCT images collected during 21 232 visits from 8831 eyes of 5529 patients with glaucoma or glaucoma suspects. METHODS: A deep learning convolutional neural network was trained to assess fundus photographs and to predict SD OCT global RNFL thickness measurements. The model then was tested on an independent sample of eyes that had longitudinal follow-up with both fundus photography and SD OCT. The ability to detect eyes that had statistically significant slopes of SD OCT change was assessed by receiver operating characteristic (ROC) curves. The repeatability of RNFL thickness predictions was investigated by measurements obtained from multiple photographs that had been acquired during the same day. MAIN OUTCOME MEASURES: The relationship between change in predicted RNFL thickness from photographs and change in SD OCT RNFL thickness over time. RESULTS: The test sample consisted of 33 466 pairs of fundus photographs and SD OCT images collected during 7125 visits from 1147 eyes of 717 patients. Eyes in the test sample were followed up for an average of 5.3 ± 3.3 years, with an average of 6.2 ± 3.8 visits. A significant correlation was found between change over time in predicted and observed RNFL thickness (r = 0.76; 95% confidence interval [CI], 0.70-0.80; P < 0.001). Retinal nerve fiber layer predictions showed an ROC curve area of 0.86 (95% CI, 0.83-0.88) to discriminate progressors from nonprogressors. For detecting fast progressors (slope faster than 2 µm/year), the ROC curve area was 0.96 (95% CI, 0.94-0.98), with a sensitivity of 97% for 80% specificity and 85% for 90% specificity. For photographs obtained at the same visit, the intraclass correlation coefficient was 0.946 (95% CI, 0.940-0.952), with a coefficient of variation of 3.2% (95% CI, 3.1%-3.3%). CONCLUSIONS: A deep learning model was able to obtain objective and quantitative estimates of RNFL thickness that correlated well with SD OCT measurements and potentially could be used to monitor for glaucomatous changes over time.

Impact of Intraocular Pressure Control on Rates of Retinal Nerve Fiber Layer Loss in a Large Clinical Population.

PURPOSE: To investigate the impact of intraocular pressure (IOP) control on rates of change of spectral-domain OCT (SD-OCT) retinal nerve fiber layer (RNFL) thickness in a large clinical population. DESIGN: Retrospective cohort study. PARTICIPANTS: A total of 85 835 IOP measurements and 60 223 SD-OCT tests from 14 790 eyes of 7844 patients. METHODS: Data were extracted from the Duke Glaucoma Registry, a large database of electronic medical records of patients with glaucoma and suspected disease followed over time at the Duke Eye Center and satellite clinics. All records from patients with a minimum of 6 months of follow-up and at least 2 good-quality SD-OCT scans and 2 clinical visits with Goldmann applanation tonometry were included. Eyes were categorized according to the frequency of visits with IOP below cutoffs of 21 mmHg, 18 mmHg, and 15 mmHg over time. Rates of change for global RNFL thickness were obtained using linear mixed models and classified as slow if change was slower than -1.0 µm/year; moderate if between -1.0 and -2.0 µm/year; and fast if faster than -2.0 µm/year. Multivariable models were adjusted for age, gender, race, diagnosis, central corneal thickness, follow-up time, and baseline disease severity. MAIN OUTCOME MEASURES: Rates of change in SD-OCT RNFL thickness according to levels of IOP control. RESULTS: Eyes had a mean follow-up of 3.5±1.9 years. Average rate of change in RNFL thickness was -0.68±0.59 µm/year. Each 1 mmHg higher mean IOP was associated with 0.05 µm/year faster RNFL loss (P < 0.001) after adjustment for potentially confounding variables. For eyes that had fast progression, 41% of them had IOP <21 mmHg in all visits during follow-up, whereas 20% of them had all visits with IOP <18 mmHg, but only 9% of them had all visits with IOP <15 mmHg. CONCLUSIONS: Intraocular pressure was significantly associated with rates of progressive RNFL loss in a large clinical population. Eyes with stricter IOP control over follow-up visits had a smaller chance of exhibiting fast deterioration. Our findings may assist clinicians in establishing target pressures in clinical practice.

Visual Field Outcomes in the Tube Versus Trabeculectomy Study.

PURPOSE: To describe visual field (VF) outcomes in the Tube Versus Trabeculectomy (TVT) Study. DESIGN: Cohort analysis of patients in a multicenter randomized clinical trial. PARTICIPANTS: A total of 122 eyes of 122 patients, with 61 eyes in both the tube shunt and trabeculectomy groups. METHODS: The TVT Study is a multicenter randomized clinical trial comparing the safety and efficacy of tube shunt surgery (350-mm2 Baerveldt implant) and trabeculectomy with mitomycin C (MMC) (0.4 mg/ml for 4 minutes) in patients with previous cataract or glaucoma surgery. Enrolled patients underwent perimetry at baseline and annual follow-up visits. The VFs were included if the false-positive rate was ≤20% and false-negative rate was ≤35%. The VFs were excluded if visual acuity <20/400 or loss of ≥2 Snellen lines from baseline was attributed to an etiology other than glaucoma. Longitudinal linear mixed-effects models with best linear unbiased predictions (BLUPs) were applied to estimate rates of change in mean deviation (MD) for each treatment group. MAIN OUTCOME MEASURE: Rate of MD change during follow-up period. RESULTS: A total of 436 reliable VFs were analyzed, with an average of 3.6 VFs per eye. Baseline MD was -13.07 ± 8.4 decibels (dB) in the tube shunt group and -13.18 ± 8.2 dB in the trabeculectomy group (P = 0.99). The rate of change in MD was -0.60 dB/year in the tube group and -0.38 dB/year in the trabeculectomy group (P = 0.34). The 95% confidence intervals for the rates of MD change were -0.77 to -0.44 dB/year in the tube group and -0.56 to -0.20 dB/year in the trabeculectomy group. No significant difference in MD slope was seen when patients were categorized by percentage of visits with intraocular pressure (IOP) <18 mmHg or by average IOP. Univariable and multivariable risk factor analyses identified history of diabetes, elevated IOP, and worse MD as baseline factors associated with more rapid VF loss. CONCLUSIONS: Slow rates of VF loss were observed after randomized surgical treatment in the TVT Study, but no significant difference in the rate of VF loss was seen after tube shunt implantation and trabeculectomy with MMC. Patients with diabetes, higher IOP, and more severe VF loss at baseline were at higher risk for VF progression.

From Machine to Machine: An OCT-Trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs.

PURPOSE: Previous approaches using deep learning (DL) algorithms to classify glaucomatous damage on fundus photographs have been limited by the requirement for human labeling of a reference training set. We propose a new approach using quantitative spectral-domain (SD) OCT data to train a DL algorithm to quantify glaucomatous structural damage on optic disc photographs. DESIGN: Cross-sectional study. PARTICIPANTS: A total of 32 820 pairs of optic disc photographs and SD OCT retinal nerve fiber layer (RNFL) scans from 2312 eyes of 1198 participants. METHODS: The sample was divided randomly into validation plus training (80%) and test (20%) sets, with randomization performed at the patient level. A DL convolutional neural network was trained to assess optic disc photographs and predict SD OCT average RNFL thickness. MAIN OUTCOME MEASURES: The DL algorithm performance was evaluated in the test sample by evaluating correlation and agreement between the predictions and actual SD OCT measurements. We also assessed the ability to discriminate eyes with glaucomatous visual field loss from healthy eyes with area under the receiver operating characteristic (ROC) curves. RESULTS: The mean prediction of average RNFL thickness from all 6292 optic disc photographs in the test set was 83.3±14.5 µm, whereas the mean average RNFL thickness from all corresponding SD OCT scans was 82.5±16.8 µm (P = 0.164). There was a very strong correlation between predicted and observed RNFL thickness values (Pearson r = 0.832; R2 = 69.3%; P < 0.001), with mean absolute error of the predictions of 7.39 µm. The area under the ROC curves for discriminating glaucomatous from healthy eyes with the DL predictions and actual SD OCT average RNFL thickness measurements were 0.944 (95% confidence interval [CI], 0.912-0.966) and 0.940 (95% CI, 0.902-0.966), respectively (P = 0.724). CONCLUSIONS: We introduced a novel DL approach to assess fundus photographs and provide quantitative information about the amount of neural damage that can be used to diagnose and stage glaucoma. In addition, training neural networks to predict SD OCT data objectively represents a new approach that overcomes limitations of human labeling and could be useful in other areas of ophthalmology.

Corneal Biomechanics and Visual Field Progression in Eyes with Seemingly Well-Controlled Intraocular Pressure.

PURPOSE: To investigate the incidence and risk factors for glaucomatous visual field progression in eyes with well-controlled intraocular pressure (IOP). DESIGN: Prospective cohort. PARTICIPANTS: A total of 460 eyes of 334 patients with glaucoma under treatment. METHODS: Study subjects had a mean follow-up of 4.3±0.8 years. Patients were classified as well controlled if all IOP measurements were less than 18 mmHg. Rates of visual field progression were calculated using ordinary least-squares linear regression of standard automated perimetry (SAP) mean deviation (MD) values over time. Progression was defined as a significantly negative MD slope (alpha = 0.05). MAIN OUTCOME MEASURES: Rates of SAP MD change; mean and peak IOP, and IOP fluctuation; and corneal biomechanics: corneal hysteresis (CH), central corneal thickness (CCT), and corneal index. RESULTS: Of the 179 eyes with well-controlled IOP, 42 (23.5%) demonstrated visual field progression. There was no significant difference between progressing and stable patients in baseline MD (-6.4±7.1 decibels [dB] vs. -6.0±6.2 dB; P = 0.346), mean IOP (11.7±2.0 mmHg vs. 12.1±2.3 mmHg; P = 0.405), IOP fluctuation (1.6±0.6 mmHg vs. 1.6±0.5 mmHg; P = 0.402), or peak IOP (14.3±1.9 mmHg vs. 14.6±2.1 mmHg; P = 0.926). Progressing eyes had significantly lower CH (8.6±1.3 mmHg vs. 9.4±1.6 mmHg; P = 0.014) and thinner CCT (515.1±33.1 µm vs. 531.1±42.4 µm; P = 0.018, respectively) compared with stable eyes. In the multivariate analysis, a 1 standard deviation lower corneal index, a summation of normalized versions of CH and CCT, resulted in a 68% higher risk of progression (odds ratio, 1.68; 95% confidence interval, 1.08-2.62; P = 0.021). CONCLUSIONS: Approximately one-quarter of eyes with well-controlled IOP may show visual field progression over time. Thin cornea and low CH are main risk factors.

Event-based analysis of visual field change can miss fast glaucoma progression detected by a combined structure and function index.

PURPOSE: To investigate the relationship between progression assessed by the visual field guided progression analysis (GPA) and rates of structural and functional change in glaucoma eyes. METHODS: This was a longitudinal observational study of 135 eyes of 97 patients with glaucoma followed for an average of 3.5 ± 0.9 years. All patients had standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) analysis with spectral domain optical coherence tomography (SDOCT), with an average of 6.8 ± 2.3 visits. A control group of healthy eyes followed longitudinally was used to estimate age-related change. Visual field progression was assessed using the Humphrey Field Analyzer GPA. Estimates of retinal ganglion cell counts from SAP and SDOCT were used to obtain a combined index of glaucomatous damage (RGC index) according to a previously described algorithm. Progression by SDOCT and the retinal ganglion cell (RGC) index were defined as statistically significant (P < 0.05) slopes of change that were also faster than age-related change estimated from healthy eyes. RESULTS: From the 135 eyes, 15 (11%) progressed by GPA, 21 (16%) progressed by SDOCT, and 31 (23%) progressed by the RGC index. Twenty-one eyes showed progression by the RGC index that was missed by the GPA. These eyes had an average rate of change in estimated RGC counts of - 28,910 cells/year, ranging from two to nine times faster than expected age-related losses. CONCLUSION: Many glaucomatous eyes that are not found to be progressing by GPA may actually have fast rates of change as detected by a combined index of structure and function.

Progression or Aging? A Deep Learning Approach for Distinguishing Glaucoma Progression From Age-Related Changes in OCT Scans.

PURPOSE: To develop deep learning (DL) algorithm to detect glaucoma progression using optical coherence tomography (OCT) images, in the absence of a reference standard. DESIGN: Retrospective cohort study. METHODS: Glaucomatous and healthy eyes with ≥5 reliable peripapillary OCT (Spectralis, Heidelberg Engineering) circle scans were included. A weakly supervised time-series learning model, called noise positive-unlabeled (Noise-PU) DL was developed to classify whether sequences of OCT B-scans showed glaucoma progression. The model used 2 learning schemes, one to identify age-related changes by differentiating test sequences from glaucoma vs healthy eyes, and the other to identify test-retest variability based on scrambled OCTs of glaucoma eyes. Both models' bases were convolutional neural networks (CNN) and long short-term memory (LSTM) networks which were combined to form a CNN-LSTM model. Model features were combined and jointly trained to identify glaucoma progression, accounting for age-related loss. The DL model's outcomes were compared with ordinary least squares (OLS) regression of retinal nerve fiber layer (RNFL) thickness over time, matched for specificity. The hit ratio was used as a proxy for sensitivity. RESULTS: Eight thousand seven hundred eighty-five follow-up sequences of 5 consecutive OCT tests from 3253 eyes (1859 subjects) were included in the study. The mean follow-up time was 3.5 ± 1.6 years. In the test sample, the hit ratios of the DL and OLS methods were 0.498 (95%CI: 0.470-0.526) and 0.284 (95%CI: 0.258-0.309) respectively (P < .001) when the specificities were equalized to 95%. CONCLUSION: A DL model was able to identify longitudinal glaucomatous structural changes in OCT B-scans using a surrogate reference standard for progression.

Social history and glaucoma progression: the effect of body mass index, tobacco and alcohol consumption on the rates of structural change in patients with glaucoma.

BACKGROUND/AIMS: Although obesity, tobacco and alcohol consumption were linked to the progression of numerous chronic diseases, an association of these social history aspects with glaucoma progression is not yet determined. This study aims to investigate the effect of body mass index (BMI) and history of tobacco and alcohol use on the rates of retinal nerve fibre layer (RNFL) change over time in glaucoma patients. METHODS: 2839 eyes of 1584 patients with glaucoma from the Duke Ophthalmic Registry were included. Patients had at least two spectral-domain optical coherency tomography (SD-OCT) tests over a minimum 6-month follow-up. Self-reported history of alcohol and tobacco consumption was extracted from electronic health records and mean BMI was calculated. Univariable and multivariable linear mixed models were used to determine the effect of each parameter on RNFL change over time. RESULTS: Mean follow-up time was 4.7±2.1 years, with 5.1±2.2 SD-OCT tests per eye. 43% and 54% of eyes had tobacco or alcohol consumption history, respectively, and 34% were classified as obese. Higher BMI had a protective effect on glaucoma progression (0.014 µm/year slower per each 1 kg/m2 higher; p=0.011). Tobacco and alcohol consumption were not significantly associated with RNFL change rates (p=0.473 and p=0.471, respectively). Underweight subjects presented significantly faster rates of structural loss (-0.768 µm/year; p=0.002) compared with normal weight. CONCLUSIONS: In a large clinical population with glaucoma, habits of tobacco and alcohol consumption showed no significant effect on the rates of RNFL change. Higher BMI was significantly associated with slower rates of RNFL loss.

Improved Prediction of Perimetric Loss in Glaucomatous Eyes Using Latent Class Mixed Modeling.

PURPOSE: To evaluate whether the identification of distinct classes within a population of glaucoma patients improves estimates of future perimetric loss. DESIGN: Longitudinal cohort study. PARTICIPANTS: A total of 6558 eyes of 3981 subjects from the Duke Ophthalmic Registry with ≥ 5 reliable standard automated perimetry (SAP) tests and ≥ 2 years of follow-up. METHODS: Standard automated perimetry mean deviation (MD) values were extracted with associated timepoints. Latent class mixed models (LCMMs) were used to identify distinct subgroups (classes) of eyes according to rates of perimetric change over time. Rates for individual eyes were then estimated by considering both individual eye data and the most probable class membership for that eye. Data were split into training (80%) and test sets (20%), and test set mean squared prediction errors (MSPEs) were estimated using LCMM and ordinary least squares (OLS) regression. MAIN OUTCOME MEASURES: Rates of change in SAP MD in each class and MSPE. RESULTS: The dataset contained 52 900 SAP tests with an average of 8.1 ± 3.7 tests per eye. The best-fitting LCMM contained 5 classes with rates of -0.06, -0.21, -0.87, -2.15, and +1.28dB/year (80.0%, 10.2%, 7.5%, 1.3%, and 1.0% of the population, respectively) labeled as slow, moderate, fast, catastrophic progressors, and "improvers" respectively. Fast and catastrophic progressors were older (64.1 ± 13.7 and 63.5 ± 16.9 vs. 57.8 ± 15.8, P < 0.001) and had generally mild-moderate disease at baseline (65.7% and 71% vs. 52%, P < 0.001) than slow progressors. The MSPE was significantly lower for LCMM compared to OLS, regardless of the number of tests used to obtain the rate of change (5.1 ± 0.6 vs. 60.2 ± 37.9, 4.9 ± 0.5 vs. 13.4 ± 3.2, 5.6 ± 0.8 vs. 8.1 ± 1.1, 3.4 ± 0.3 vs. 5.5 ± 1.1 when predicting the fourth, fifth, sixth, and seventh visual fields (VFs) respectively; P < 0.001 for all comparisons). MSPE of fast and catastrophic progressors was significantly lower with LCMM versus OLS (17.7 ± 6.9 vs. 48.1 ± 19.7, 27.1 ± 8.4 vs. 81.3 ± 27.1, 49.0 ± 14.7 vs. 183.9 ± 55.2, 46.6 ± 16.0 vs. 232.4 ± 78.0 when predicting the fourth, fifth, sixth, and seventh VFs respectively; P < 0.001 for all comparisons). CONCLUSIONS: Latent class mixed model successfully identified distinct classes of progressors within a large glaucoma population that seemed to reflect subgroups observed in clinical practice. Latent class mixed models were superior to OLS regression in predicting future VF observations. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosuremay be found after the references.

C-DU(KE) Calculator: A Clinical Tool for Risk Stratification in Infectious Keratitis.

PURPOSE: The aim of this study was to identify characteristics of infectious keratitis predictive of poor outcome to develop a web-based predictive calculator. METHOD: A retrospective chart review was performed at the Duke Eye Center. Two hundred fifteen adult patients with culture-proven infectious keratitis presenting between January 1, 2016, and December 31, 2020, were separated into a derivation set (136 patients, 53 positives; 83 controls) and a temporal validation set (79 patients, 26 positives; 53 controls). The poor outcome group consisted of patients requiring penetrating keratoplasty for visually significant scarring, penetrating keratoplasty for ulcer progression, or evisceration/enucleation for endophthalmitis. Univariable analysis was performed followed by stepwise multivariable logistic regression to obtain a predictive model in the derivation data set. Culture-naïve and postculture models were constructed. Discrimination and calibration were assessed using the area under the curve (AUC) and calibration plots, respectively. RESULTS: The culture-naïve model consisted of corticosteroid drop use postsymptom onset [Odds Ratio (OR) = 2.3, P = 0.054], decreased vision (OR = 2.4, P = 0.001), and increased ulcer area (OR = 1.017, P = 0.017). The postculture model additionally included fungal keratitis (OR = 5.4, P = 0.006) and elapsed time from symptoms to organism-sensitive therapy (OR = 1.027, P = 0.014). The models were summarized by the acronym C-DU(KE). The AUCs for the culture-naïve model were 0.794 in the derivation set and 0.850 in the validation set. The AUCs for the postculture model were 0.898 in the derivation set and 0.946 in the validation set. Calibration plots indicated goodness of fit in the data sets for both models. The calculator was deployed under the URL: https://duke-eye-calculator.shinyapps.io/Corneal_Ulcers/ . CONCLUSIONS: The C-DU(KE) calculator permits a data-driven prediction of outcome in infectious keratitis that can supplement clinical judgment.

Association of an Objective Structural and Functional Reference Standard for Glaucoma with Quality of Life Outcomes.

PURPOSE: To compare self-reported quality-of-life (QoL) outcomes of patients diagnosed as normal, glaucoma suspect, and glaucoma based on an objective reference standard for glaucomatous optic neuropathy (GON). DESIGN: Cross-sectional study. PARTICIPANTS: 1884 eyes of 1019 patients were included in the study. METHODS: The data was sourced from the Duke Glaucoma Registry. Eyes were classified according to the presence and topographic correspondence of functional and structural damage, as assessed by parameters from standard automated perimetry (SAP) and spectral-domain OCT (SD-OCT). The objective diagnosis of the worse eye was used to define patient-level diagnosis. To assess QoL in the diagnostic groups, 14 unidimensional vision-related items of the National Eye Institute Visual Functioning Questionnaire (NEI VFQ-25) were used to assess QoL in the diagnostic groups. Association between NEI VFQ-25 Rasch-calibrated scores and diagnostic groups was assessed through multivariable regression that controlled for confounding demographic and socioeconomic variables such as age, sex, race, income, marriage status, insurance status, and highest education level. MAIN OUTCOME MEASURES: NEI VFQ-25 Rasch scores compared with objective criteria diagnosis based on SAP mean deviation (MD) and SD-OCT retinal nerve fiber layer (RNFL) thickness. RESULTS: Overall, eyes classified as normal, glaucoma suspect, and glaucoma had decreasing mean scores in SAP MD (0.2 ± 1.0 dB, -0.9 ± 2.4 dB, -6.2 ± 7.0 dB, respectively; P < 0.001) and SD-OCT RNFL thickness (97.8 ± 9.5 µm, 89.0 ± 13.1 µm, 64.5 ± 12.8 µm, respectively; P < 0.001). The mean Rasch-calibrated NEI VFQ-25 score was significantly different among normal, suspect, and glaucoma groups (82.9 ± 13.0, 78.2 ± 14.8, and 72.6 ± 16.2, respectively; P < 0.001). When adjusted for confounding socioeconomic variables, glaucoma patients had significantly worse QoL than those classified as normal (ß = -6.8 Rasch score units; P < 0.001). CONCLUSION: A glaucoma diagnosis, based on an objective reference standard for GON, was significantly associated with worse Rasch-adjusted scores of QoL. Utilization of such objective criteria may provide clinically relevant metrics with potential to improve comparability of research findings and validation of newly proposed diagnostic tools. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

Intraocular Pressure and Rates of Macular Thinning in Glaucoma.

PURPOSE: To evaluate the effect of intraocular pressure (IOP) on the rates of macular thickness (ganglion cell layer [GCL] and ganglion cell-inner plexiform layer [GCIPL]) change over time measured by spectral-domain (SD) OCT. DESIGN: Retrospective cohort study. PARTICIPANTS: Overall, 451 eyes of 256 patients with primary open-angle glaucoma. METHODS: Data were extracted from the Duke Ophthalmic Registry, a database of electronic medical records of patients observed under routine clinical care at the Duke Eye Center, and satellite clinics. All records from patients with a minimum of 6 months of follow-up and at least 2 good-quality Spectralis SD-OCT macula scans were included. Linear mixed models were used to investigate the relationship between average IOP during follow-up and rates of GCL and GCIPL thickness change over time. MAIN OUTCOME MEASURES: The effect of IOP on the rates of GCL and GCIPL thickness loss measured by SD-OCT. RESULTS: Eyes had a mean follow-up of 1.8 ± 1.3 years, ranging from 0.5 to 10.2 years. The average rate of change for GCL thickness was -0.220 µm/year (95% confidence interval [CI], -0.268 to -0.172 µm/year) and for GCIPL thickness was -0.231 µm/year (95% CI, -0.302 to -0.160 µm/year). Each 1-mmHg higher mean IOP during follow-up was associated with an additional loss of -0.021 µm/year of GCL thickness (P = 0.001) and -0.032 µm/year of GCIPL thickness (P = 0.001) after adjusting for potentially confounding factors, such as baseline age, disease severity, sex, race, central corneal thickness, and follow-up time. CONCLUSIONS: Higher IOP was significantly associated with faster rates of GCL and GCIPL loss over time measured by SD-OCT, even during relatively short follow-up times. These findings support the use of SD-OCT GCL and GCIPL thickness measurements as structural biomarkers for the evaluation of the efficacy of IOP-lowering therapies in slowing down the progression of glaucoma. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Increasing Rates of Herpes Zoster Ophthalmicus and the COVID-19 Pandemic.

Purpose: This epidemiologic study evaluates the variance in incidence of Herpes Zoster (HZ) and Herpes Zoster Ophthalmicus (HZO) within a single healthcare system with an aim to analyze their relationship to the COVID-19 pandemic. Methods: All patients attending the Duke University Health System (DUHS) from January 1, 2018, to December 31, 2021, were included. General and COVID-related trends of HZO and HZ were analyzed based on new ICD-9 or ICD-10 diagnosis codes, compared with the total number of patients seen at DUHS during this period, and the number of reported COVID-19 cases in North Carolina obtained using the CDC data tracker. Results: This study included 16,287 cases of HZ of whom 1,294 (7.94%) presented with HZO. The overall incidence of HZO showed an average yearly increase of 5.6%, however HZ incidence decreased by 5.3% per year. When comparing incidence rates of HZO in the 12-months before and after the COVID-19 pandemic onset in the United States (March 2020), the average incidence from March 2020 to February 2021 was 27.6 ± 11.6 compared to 18.0 ± 2.7 from March 2019 to February 2020 (p = 0.01). Moreover, 10/12 (83.3%) of the months had a higher incidence rate of HZO in the post-COVID onset year compared to their corresponding month in the pre-COVID year. Conclusion: The results show HZO incidence may be increasing, despite an overall lower HZ incidence. This could suggest a distinct mechanism for HZO appearance. The COVID pandemic, directly or indirectly, may have accelerated the already increasing HZO incidence.

The Definition of Glaucomatous Optic Neuropathy in Artificial Intelligence Research and Clinical Applications.

OBJECTIVE: Although artificial intelligence (AI) models may offer innovative and powerful ways to use the wealth of data generated by diagnostic tools, there are important challenges related to their development and validation. Most notable is the lack of a perfect reference standard for glaucomatous optic neuropathy (GON). Because AI models are trained to predict presence of glaucoma or its progression, they generally rely on a reference standard that is used to train the model and assess its validity. If an improper reference standard is used, the model may be trained to detect or predict something that has little or no clinical value. This article summarizes the issues and discussions related to the definition of GON in AI applications as presented by the Glaucoma Workgroup from the Collaborative Community for Ophthalmic Imaging (CCOI) US Food and Drug Administration Virtual Workshop, on September 3 and 4, 2020, and on January 28, 2022. DESIGN: Review and conference proceedings. SUBJECTS: No human or animal subjects or data therefrom were used in the production of this article. METHODS: A summary of the Workshop was produced with input and approval from all participants. MAIN OUTCOME MEASURES: Consensus position of the CCOI Workgroup on the challenges in defining GON and possible solutions. RESULTS: The Workshop reviewed existing challenges that arise from the use of subjective definitions of GON and highlighted the need for a more objective approach to characterize GON that could facilitate replication and comparability of AI studies and allow for better clinical validation of proposed AI tools. Different tests and combination of parameters for defining a reference standard for GON have been proposed. Different reference standards may need to be considered depending on the scenario in which the AI models are going to be applied, such as community-based or opportunistic screening versus detection or monitoring of glaucoma in tertiary care. CONCLUSIONS: The development and validation of new AI-based diagnostic tests should be based on rigorous methodology with clear determination of how the reference standards for glaucomatous damage are constructed and the settings where the tests are going to be applied. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.