Comparing the Accuracy of Peripapillary OCT Scans and Visual Fields to Detect Glaucoma Worsening.

PURPOSE: To compare the accuracy of detecting moderate and rapid rates of glaucoma worsening over a 2-year period with different numbers of OCT scans and visual field (VF) tests in a large sample of glaucoma and glaucoma suspect eyes. DESIGN: Descriptive and simulation study. PARTICIPANTS: The OCT sample comprised 12 150 eyes from 7392 adults with glaucoma or glaucoma suspect status followed up at the Wilmer Eye Institute from 2013 through 2021. The VF sample comprised 20 583 eyes from 10 958 adults from the same database. All eyes had undergone at least 5 measurements over follow-up from the Zeiss Cirrus OCT or Humphrey Field Analyzer. METHODS: Within-eye rates of change in retinal nerve fiber layer (RNFL) thickness and mean deviation (MD) were measured using linear regression. For each measured rate, simulated measurements of RNFL thickness and MD were generated using the distributions of residuals. Simulated rates of change for different numbers of OCT scans and VF tests over a 2-year period were used to estimate the accuracy of detecting moderate (75th percentile) and rapid (90th percentile) worsening for OCT and VF. Accuracy was defined as the percentage of simulated eyes in which the true rate of worsening (the rate without measurement error) was at or less than a criterion rate (e.g., 75th or 90th percentile). MAIN OUTCOME MEASURES: The accuracy of diagnosing moderate and rapid rates of glaucoma worsening for different numbers of OCT scans and VF tests over a 2-year period. RESULTS: Accuracy was less than 50% for both OCT and VF when diagnosing worsening after a 2-year period. OCT accuracy was 5 to 10 percentage points higher than VF accuracy at detecting moderate worsening and 10 to 15 percentage points higher for rapid worsening. Accuracy increased by more than 17 percentage points when using both OCT and VF to detect worsening, that is, when relying on either OCT or VF to be accurate. CONCLUSIONS: More frequent OCT scans and VF tests are needed to improve the accuracy of diagnosing glaucoma worsening. Accuracy greatly increases when relying on both OCT and VF to detect worsening. FINANCIAL DISCLOSURE(S): The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Evidence-Based Guidelines for the Number of Peripapillary OCT Scans Needed to Detect Glaucoma Worsening.

PURPOSE: To estimate the number of OCT scans necessary to detect moderate and rapid rates of retinal nerve fiber layer (RNFL) thickness worsening at different levels of accuracy using a large sample of glaucoma and glaucoma-suspect eyes. DESIGN: Descriptive and simulation study. PARTICIPANTS: Twelve thousand one hundred fifty eyes from 7392 adult patients with glaucoma or glaucoma-suspect status followed up at the Wilmer Eye Institute from 2013 through 2021. All eyes had at least 5 measurements of RNFL thickness on the Cirrus OCT (Carl Zeiss Meditec) with signal strength of 6 or more. METHODS: Rates of RNFL worsening for average RNFL thickness and for the 4 quadrants were measured using linear regression. Simulations were used to estimate the accuracy of detecting worsening-defined as the percentage of patients in whom the true rate of RNFL worsening was at or less than different criterion rates of worsening when the OCT-measured rate was also at or less than these criterion rates-for two different measurement strategies: evenly spaced (equal time intervals between measurements) and clustered (approximately half the measurements at each end point of the period). MAIN OUTCOME MEASURES: The 75th percentile (moderate) and 90th percentile (rapid) rates of RNFL worsening for average RNFL thickness and the accuracy of diagnosing worsening at these moderate and rapid rates. RESULTS: The 75th and 90th percentile rates of worsening for average RNFL thickness were -1.09 µm/year and -2.35 µm/year, respectively. Simulations showed that, for the average measurement frequency in our sample of approximately 3 OCT scans over a 2-year period, moderate and rapid RNFL worsening were diagnosed accurately only 47% and 40% of the time, respectively. Estimates for the number of OCT scans needed to achieve a range of accuracy levels are provided. For example, 60% accuracy requires 7 measurements to detect both moderate and rapid worsening within a 2-year period if the more efficient clustered measurement strategy is used. CONCLUSIONS: To diagnose RNFL worsening more accurately, the number of OCT scans must be increased compared with current clinical practice. A clustered measurement strategy reduces the number of scans required compared with evenly spacing measurements.

Predicting Visual Field Worsening with Longitudinal OCT Data Using a Gated Transformer Network.

PURPOSE: To identify visual field (VF) worsening from longitudinal OCT data using a gated transformer network (GTN) and to examine how GTN performance varies for different definitions of VF worsening and different stages of glaucoma severity at baseline. DESIGN: Retrospective longitudinal cohort study. PARTICIPANTS: A total of 4211 eyes (2666 patients) followed up at the Johns Hopkins Wilmer Eye Institute with at least 5 reliable VF results and 1 reliable OCT scan within 1 year of each reliable VF test. METHODS: For each eye, we used 3 trend-based methods (mean deviation [MD] slope, VF index slope, and pointwise linear regression) and 3 event-based methods (Guided Progression Analysis, Collaborative Initial Glaucoma Treatment Study scoring system, and Advanced Glaucoma Intervention Study [AGIS] scoring system) to define VF worsening. Additionally, we developed a "majority of 6" algorithm (M6) that classifies an eye as worsening if 4 or more of the 6 aforementioned methods classified the eye as worsening. Using these 7 reference standards for VF worsening, we trained 7 GTNs that accept a series of at least 5 as input OCT scans and provide as output a probability of VF worsening. Gated transformer network performance was compared with non-deep learning models with the same serial OCT input from previous studies-linear mixed-effects models (MEMs) and naive Bayes classifiers (NBCs)-using the same training sets and reference standards as for the GTN. MAIN OUTCOME MEASURES: Area under the receiver operating characteristic curve (AUC). RESULTS: The M6 labeled 63 eyes (1.50%) as worsening. The GTN achieved an AUC of 0.97 (95% confidence interval, 0.88-1.00) when trained with M6. Gated transformer networks trained and optimized with the other 6 reference standards showed an AUC ranging from 0.78 (MD slope) to 0.89 (AGIS). The 7 GTNs outperformed all 7 MEMs and all 7 NBCs accordingly. Gated transformer network performance was worse for eyes with more severe glaucoma at baseline. CONCLUSIONS: Gated transformer network models trained with OCT data may be used to identify VF worsening. After further validation, implementing such models in clinical practice may allow us to track functional worsening of glaucoma with less onerous structural testing. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

Effectiveness of Netarsudil versus Brimonidine in Eyes already Being Treated with Glaucoma Medications at a Single Academic Tertiary Care Practice: A Comparative Study.

Background: Rho kinase inhibitors, such as netarsudil, are a relatively new class of medications recently introduced into the market for the treatment of glaucoma, the leading cause of irreversible blindness in the world. Previous clinical trials have studied netarsudil's efficacy when used as a first- or second-line agent but limited studies have investigated its effectiveness in the real world where it is more commonly used as a third, fourth, or fifth agent in combination with other topical medications. Equally important, prior studies have not compared its effectiveness to its peer medications in these settings. Objective: To compare intraocular pressure (IOP) lowering after initiation of netarsudil or brimonidine therapy in patients with glaucoma using >2 medications for IOP management. Methods: A chart review of 369 eyes from 279 patients followed at a single academic tertiary practice was performed with an institutional review board waiver of consent to compare IOP lowering after prescription of netarsudil (n = 176) versus brimonidine (n = 193) as a third, fourth, or fifth IOP-lowering agent. Patients were identified by querying the electronic medical record for those with a glaucoma-related diagnosis who were prescribed either medication. Five sequential IOP measurements were obtained to determine the mean change in IOP before and after treatment (ΔIOP = mean IOP4,5 - mean IOP1,2,3). A multilevel linear mixed-effects model assessed the influence of medication (independent variable) on ΔIOP (dependent variable). Additional independent variables of interest included the number of glaucoma medications at baseline, age, sex, glaucoma type and severity, race, and pretreatment IOP. Bootstrap analysis was performed to remove sampling bias and confirm mixed-effects model findings. Kaplan-Meier survival analysis evaluated the probability of requiring additional intervention within 3 years following the date of medication prescription. Results: The unadjusted mean (SD) ΔIOP for netarsudil and brimonidine was -2.20 (4.11) mm Hg and -2.21 (3.25) mm Hg, respectively (P = 0.484). The adjusted linear mixed-effects models and bootstrap analysis demonstrated that there was no statistical difference in IOP-lowering effectiveness between the medications. Netarsudil and brimonidine failed to adequately control IOP at similar rates with 42% and 47% probabilities of survival respectively by the 3-year follow-up (P = 0.520). Conclusions: When escalating pharmacologic therapy, the IOP-lowering effect of netarsudil appeared to be similar to that produced by brimonidine. (Curr Ther Res Clin Exp. 2023; 84:XXX-XXX).

The Effect of Achieving Target Intraocular Pressure on Visual Field Worsening.

PURPOSE: To estimate the effect of achieving target intraocular pressure (IOP) values on visual field (VF) worsening in a treated clinical population. DESIGN: Retrospective analysis of longitudinal data. PARTICIPANTS: A total of 2852 eyes of 1688 patients with glaucoma-related diagnoses treated in a tertiary care practice. All included eyes had at least 5 reliable VF tests and 5 IOP measures on separate visits along with at least 1 target IOP defined by a clinician on the first or second visit. METHODS: The primary dependent variable was the slope of the mean deviation (MD) over time (decibels [dB]/year). The primary independent variable was mean target difference (measured IOP - target IOP). We created simple linear regression models and mixed-effects linear models to study the relationship between MD slope and mean target difference for individual eyes. In the mixed-effects models, we included an interaction term to account for disease severity (mild/suspect, moderate, or advanced) and a spline term to account for the differing effects of achieving target IOP (target difference ≤0) and failing to achieve target IOP (target difference >0). MAIN OUTCOME MEASURES: Rate of change in MD slope (changes in dB/year) per 1 mmHg change in target difference at different stages of glaucoma severity. RESULTS: Across all eyes, a simple linear regression model demonstrated that a 1 mmHg increase in target difference had a -0.018 dB/year (confidence interval [CI], -0.026 to -0.011; P < 0.05) effect on MD slope. The mixed-effects model shows that eyes with moderate disease that fail to achieve their target IOP experience the largest effects, with a 1 mmHg increase in target difference resulting in a -0.119 dB/year (CI, -0.168 to -0.070; P < 0.05) worse MD slope. The effects of missing target IOP on VF worsening were more pronounced than the effect of absolute level of IOP on VF worsening, where a 1 mmHg increase in IOP had a -0.004 dB/year (CI, -0.011 to 0.003; P > 0.05) effect on the MD slope. CONCLUSIONS: In treated patients, failing to achieve target IOP was associated with more rapid VF worsening. Eyes with moderate glaucoma experienced the greatest VF worsening from failing to achieve target IOP.

Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data.

PURPOSE: Rule-based approaches to determining glaucoma progression from visual fields (VFs) alone are discordant and have tradeoffs. To detect better when glaucoma progression is occurring, we used a longitudinal data set of merged VF and clinical data to assess the performance of a convolutional long short-term memory (LSTM) neural network. DESIGN: Retrospective analysis of longitudinal clinical and VF data. PARTICIPANTS: From 2 initial datasets of 672 123 VF results from 213 254 eyes and 350 437 samples of clinical data, persons at the intersection of both datasets with 4 or more VF results and corresponding baseline clinical data (cup-to-disc ratio, central corneal thickness, and intraocular pressure) were included. After exclusion criteria-specifically the removal of VFs with high false-positive and false-negative rates and entries with missing data-were applied to ensure reliable data, 11 242 eyes remained. METHODS: Three commonly used glaucoma progression algorithms (VF index slope, mean deviation slope, and pointwise linear regression) were used to define eyes as stable or progressing. Two machine learning models, one exclusively trained on VF data and another trained on both VF and clinical data, were tested. MAIN OUTCOME MEASURES: Area under the receiver operating characteristic curve (AUC) and area under the precision-recall curve (AUPRC) calculated on a held-out test set and mean accuracies from threefold cross-validation were used to compare the performance of the machine learning models. RESULTS: The convolutional LSTM network demonstrated 91% to 93% accuracy with respect to the different conventional glaucoma progression algorithms given 4 consecutive VF results for each participant. The model that was trained on both VF and clinical data (AUC, 0.89-0.93) showed better diagnostic ability than a model exclusively trained on VF results (AUC, 0.79-0.82; P < 0.001). CONCLUSIONS: A convolutional LSTM architecture can capture local and global trends in VFs over time. It is well suited to assessing glaucoma progression because of its ability to extract spatiotemporal features that other algorithms cannot. Supplementing VF results with clinical data improves the model's ability to assess glaucoma progression and better reflects the way clinicians manage data when managing glaucoma.

The Effect of Transitioning from SITA Standard to SITA Faster on Visual Field Performance.

PURPOSE: To determine the effect of transitioning from Swedish Interactive Thresholding Algorithm (SITA) Standard to SITA Faster on visual field (VF) performance in glaucomatous eyes with a broad spectrum of disease severity undergoing longitudinal VF testing in a real-world clinical setting. DESIGN: Retrospective, longitudinal study. PARTICIPANTS: A total of 421 patients (766 eyes) with manifest or suspect glaucoma followed at a single institution. METHODS: Each included eye received the following sequence of VF examinations during routine clinical care: (1) SITA Standard, (2) SITA Standard, and (3) SITA Faster (mean time between tests = 13.9 months). Intra-eye comparisons were made between the first 2 VFs (Standard-Standard sequence) and the last 2 VFs (Standard-Faster sequence). The primary dependent variable was the difference in mean deviation (MD) between the second and first VF of the sequence (ΔMD, calculated as MDVF2 - MDVF1). The primary independent variable was the VF sequence (Standard-Standard or Standard-Faster). Linear mixed-effects models were used to study the effect of testing sequence on ΔMD, adjusting for confounders including time between VFs and change in false-positive (FP) errors. Results were stratified to understand the effect of glaucoma severity on the relationship between testing sequence and ΔMD. MAIN OUTCOME MEASURES: The difference in ΔMD between Standard-Standard and Standard-Faster sequence by mild, moderate, and advanced disease severity. RESULTS: In eyes with mild or suspect glaucoma, there was no significant difference in ΔMD between Standard-Faster and Standard-Standard sequences (-0.23 decibels [dB]; 95% confidence interval [CI], -0.60 to 0.15 dB). However, the Standard-Faster sequence was associated with a 0.87 dB (95% CI, 0.18-1.57 dB) improvement in ΔMD compared with the Standard-Standard sequence in eyes with moderate glaucoma and a 1.49 dB (95% CI, 0.79-2.19 dB) improvement in ΔMD in eyes with advanced glaucoma. CONCLUSIONS: Converting to SITA Faster in eyes that were previously followed with SITA Standard led to similar VF performance in mild glaucoma but resulted in higher MD values in moderate and advanced glaucoma. For patients with moderate or severe glaucoma, this may conceal disease progression when transitioning testing strategies.

Evidence-Based Criteria for Determining Peripapillary OCT Reliability.

PURPOSE: To assess the impact of OCT signal strength (SS) and artifact on retinal nerve fiber layer (RNFL) measurement reliability and to understand whether glaucoma severity modifies this relationship. DESIGN: Retrospective, longitudinal cohort study. PARTICIPANTS: Two thousand nine hundred ninety-two OCT scans from 474 eyes of 241 patients with glaucoma or glaucoma suspect status. METHODS: We extracted mean RNFL thickness and SS and manually graded scans for artifact. To analyze the effect of SS and artifact on OCT reliability, we (1) created a multilevel linear model using measured RNFL thickness values and demographic and clinical data to estimate the true (predicted) RNFL thickness, (2) calculated model residuals (ΔRNFL) as our reliability measure, and (3) created a second multilevel linear model with splines and interaction terms that modeled overall and quadrant specific reliability (ΔRNFL) as the outcome, using SS and artifact as predictors. MAIN OUTCOME MEASURES: Impact of SS and artifact on ΔRNFL. RESULTS: For SS between 10 and 3, the impact of decreases in SS on OCT reliability is modest (-0.67 to -1.25 ΔRNFL per 1-point decrease in SS; P < 0.05). But at less than 3, changes in SS have a large impact on reliability (-15.70 to -16.34 ΔRNFL per 1-point decrease in SS; P < 0.05). At SS between 10 and 3, decreases in SS tend to have a larger impact on reliability in eyes with severe glaucoma (-1.25 per 1-point decrease in SS; P < 0.05) compared with eyes with mild or moderate glaucoma (-0.67 to -0.75 per 1-point decrease in SS; P < 0.05). The presence of artifact has a significant impact on OCT reliability independent of the effects of SS (-4.76 ΔRNFL; P < 0.05). Artifact affects reliability solely in the quadrant in which it occurs, with artifact in one quadrant showing no impact on ΔRNFL in the opposite quadrant (P > 0.05). CONCLUSIONS: Signal strength decreases down to 3 have relatively mild impacts on OCT reliability. At less than 3, the impact of further decreases in SS on reliability are substantial. The effect of SS on reliability is greater in severe glaucoma. Artifacts result in a decrease in reliability independent of the effect of SS. We propose evidence-based guidelines to guide physicians on whether to trust the results of an OCT scan.

The Evolving Role of the Relationship between Optic Nerve Structure and Function in Glaucoma.

The relationship between functional vision loss and structural changes of the optic nerve head and retinal ganglion cells is the hallmark of glaucoma diagnosis. Understanding and measuring this relationship has been the focus of numerous studies, the goal of which have been to improve glaucoma diagnosis and detection of glaucoma worsening. In this review, a historical perspective is used to understand structure-function relationships in glaucoma and their application to improve glaucoma diagnosis and monitoring. Initially, histologic studies that link visual field sensitivity to retinal ganglion cell count are discussed. Additionally, those studies that determined the mathematical relationship between visual field sensitivity and ganglion cell number are reviewed. Next, those studies that attempt to create a map of the structure-function relationship using fundus photography and visual field sensitivity are examined. Subsequently, studies that use more recent imaging technology, such as optical coherence tomography, confocal scanning laser ophthalmoscopy, or scanning laser perimetry, to measure structure quantitatively in vivo and to correlate these measures with automated perimetry are explored. Among these studies that use advanced imaging, those that use cross-sectional data to explore structure-function relationships to improve glaucoma diagnosis first are discussed. Second, those studies that use longitudinal data to improve detection of worsening are reviewed. Finally, areas of further research and steps needed to implement structure-function relationships clinically are explored.

Evidence-based Criteria for Assessment of Visual Field Reliability.

PURPOSE: Assess the impact of false-positives (FP), false-negatives (FN), fixation losses (FL), and test duration (TD) on visual field (VF) reliability at different stages of glaucoma severity. DESIGN: Retrospective. PARTICIPANTS: A total of 10 262 VFs from 1538 eyes of 909 subjects with suspect or manifest glaucoma and ≥5 VF examinations. METHODS: Predicted mean deviation (MD) was calculated with multilevel modeling of longitudinal data. Differences between predicted and observed MD (ΔMD) were calculated as a reliability measure. The impact of FP, FN, FL, and TD on ΔMD was assessed using multilevel modeling. MAIN OUTCOME MEASURES: ΔMD associated with a 10% increment in FP, FN, and FL, or a 1-minute increase in TD. RESULTS: FL had little impact on ΔMD (<0.2 decibels [dB] per 10% abnormal catch trials), and no level of FL produced ≥1 dB of ΔMD at any disease stage. FP yielded greater than expected MD, with a 10% increment in abnormal catch trials associated with a ΔMD = 0.42, 0.73, and 0.66 dB in mild (MD >-6 dB), moderate (-6 ≤MD <-12 dB), and severe (-12 ≤MD ≤-20 dB) disease, respectively, up to 20% abnormal catch trials, and a ΔMD = 1.57, 2.06, and 3.53 dB beyond 20% abnormal catch trials. FNs generally produced observed MDs below expected MDs. FN were minimally impactful up to 20% abnormal catch trials (ΔMD per 10% increment >-0.14 dB at all levels of severity). Beyond 20% abnormal catch trials, each 10% increment in abnormal catch trials was associated with a ΔMD = -1.27, -0.53, and -0.51 dB in mild, moderate, and severe disease, respectively. |ΔMD| ≥1 dB occurred with 22% FP and 26% FN in early, 14% FP and 34% FN in moderate, and 16% FP and 51% FN in severe disease. A 1-minute increment in TD produced ΔMDs between -0.35 and -0.40 dB. CONCLUSIONS: FL have little impact on reliability in patients with established glaucoma. FP, and to a lesser extent FNs and TD, significantly affect reliability. The impact of FP and FN varies with disease severity and over the range of abnormal catch trials. On the basis of our findings, we present evidence-based, severity-specific standards for classifying VF reliability for clinical or research applications.

The Impact of Social Vulnerability on Structural and Functional Glaucoma Severity, Worsening, and Variability.

PURPOSE: To determine the associations between social vulnerability index (SVI) and baseline severity, worsening, and variability of glaucoma, as assessed by visual field (VF) and OCT. DESIGN: Retrospective longitudinal cohort study. PARTICIPANTS: Adults with glaucoma or glaucoma suspect status in 1 or both eyes. Visual fields were derived from 7897 eyes from 4482 patients, while OCTs were derived from 6271 eyes from 3976 patients. All eyes had a minimum of 5 tests over follow-up using either the Humphrey Field Analyzer or the Cirrus HD-OCT. METHODS: Social vulnerability index, which measures neighborhood-level environmental factors, was linked to patients' addresses at the census tract level. Rates of change in mean deviation (MD) and retinal nerve fiber layer (RNFL) thickness were computed using linear regression. The slope of the regression line was used to assess worsening, while the standard deviation of residuals was used as a measure of variability. Multivariable linear mixed-effects models were used to investigate the impact of SVI on baseline, worsening, and variability in both MD and RNFL. We further explored the interaction effect of mean intraocular pressure (IOP) and SVI on worsening in MD and RNFL. MAIN OUTCOME MEASURES: Glaucoma severity defined based on baseline MD and RNFL thickness. Worsening defined as MD and RNFL slope. Variability defined as the standard deviation of the residuals obtained from MD and RNFL slopes. RESULTS: Increased (worse) SVI was significantly associated with worse baseline MD (ß = -1.07 dB, 95% confidence interval [CI]: [-1.54, -0.60]), thicker baseline RNFL (ß = 2.46 µm, 95% CI: [0.75, 4.17]), greater rates of RNFL loss (ß = -0.12 µm, 95% CI: [-0.23, -0.02]), and greater VF variability (ß = 0.16 dB, 95% CI: [0.07, 0.24]). Having worse SVI was associated with worse RNFL loss with increases in IOP (ßinteraction = -0.07, 95% CI: [-0.12, -0.02]). CONCLUSIONS: Increased SVI score is associated with worse functional (VF) loss at baseline, higher rates of structural (OCT) worsening over time, higher VF variability, and a greater effect of IOP on RNFL loss. Further studies are needed to enhance our understanding of these relationships and establish their cause. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Deep learning-based identification of eyes at risk for glaucoma surgery.

To develop and evaluate the performance of a deep learning model (DLM) that predicts eyes at high risk of surgical intervention for uncontrolled glaucoma based on multimodal data from an initial ophthalmology visit. Longitudinal, observational, retrospective study. 4898 unique eyes from 4038 adult glaucoma or glaucoma-suspect patients who underwent surgery for uncontrolled glaucoma (trabeculectomy, tube shunt, xen, or diode surgery) between 2013 and 2021, or did not undergo glaucoma surgery but had 3 or more ophthalmology visits. We constructed a DLM to predict the occurrence of glaucoma surgery within various time horizons from a baseline visit. Model inputs included spatially oriented visual field (VF) and optical coherence tomography (OCT) data as well as clinical and demographic features. Separate DLMs with the same architecture were trained to predict the occurrence of surgery within 3 months, within 3-6 months, within 6 months-1 year, within 1-2 years, within 2-3 years, within 3-4 years, and within 4-5 years from the baseline visit. Included eyes were randomly split into 60%, 20%, and 20% for training, validation, and testing. DLM performance was measured using area under the receiver operating characteristic curve (AUC) and precision-recall curve (PRC). Shapley additive explanations (SHAP) were utilized to assess the importance of different features. Model prediction of surgery for uncontrolled glaucoma within 3 months had the best AUC of 0.92 (95% CI 0.88, 0.96). DLMs achieved clinically useful AUC values (> 0.8) for all models that predicted the occurrence of surgery within 3 years. According to SHAP analysis, all 7 models placed intraocular pressure (IOP) within the five most important features in predicting the occurrence of glaucoma surgery. Mean deviation (MD) and average retinal nerve fiber layer (RNFL) thickness were listed among the top 5 most important features by 6 of the 7 models. DLMs can successfully identify eyes requiring surgery for uncontrolled glaucoma within specific time horizons. Predictive performance decreases as the time horizon for forecasting surgery increases. Implementing prediction models in a clinical setting may help identify patients that should be referred to a glaucoma specialist for surgical evaluation.

Assessment of linear regression of peripapillary optical coherence tomography retinal nerve fiber layer measurements to forecast glacuoma trajectory.

Linear regression of optical coherence tomography measurements of peripapillary retinal nerve fiber layer thickness is often used to detect glaucoma progression and forecast future disease course. However, current measurement frequencies suggest that clinicians often apply linear regression to a relatively small number of measurements (e.g., less than a handful). In this study, we estimate the accuracy of linear regression in predicting the next reliable measurement of average retinal nerve fiber layer thickness using Zeiss Cirrus optical coherence tomography measurements of average retinal nerve fiber layer thickness from a sample of 6,471 eyes with glaucoma or glaucoma-suspect status. Linear regression is compared to two null models: no glaucoma worsening, and worsening due to aging. Linear regression on the first M ≥ 2 measurements was significantly worse at predicting a reliable M+1st measurement for 2 ≤ M ≤ 6. This range was reduced to 2 ≤ M ≤ 5 when retinal nerve fiber layer thickness measurements were first "corrected" for scan quality. Simulations based on measurement frequencies in our sample-on average 393 ± 190 days between consecutive measurements-show that linear regression outperforms both null models when M ≥ 5 and the goal is to forecast moderate (75th percentile) worsening, and when M ≥ 3 for rapid (90th percentile) worsening. If linear regression is used to assess disease trajectory with a small number of measurements over short time periods (e.g., 1-2 years), as is often the case in clinical practice, the number of optical coherence tomography examinations needs to be increased.

Opportunities for Improving Glaucoma Clinical Trials via Deep Learning-Based Identification of Patients with Low Visual Field Variability.

PURPOSE: Develop and evaluate the performance of a deep learning model (DLM) that forecasts eyes with low future visual field (VF) variability, and study the impact of using this DLM on sample size requirements for neuroprotective trials. DESIGN: Retrospective cohort and simulation study. METHODS: We included 1 eye per patient with baseline reliable VFs, OCT, clinical measures (demographics, intraocular pressure, and visual acuity), and 5 subsequent reliable VFs to forecast VF variability using DLMs and perform sample size estimates. We estimated sample size for 3 groups of eyes: all eyes (AE), low variability eyes (LVE: the subset of AE with a standard deviation of mean deviation [MD] slope residuals in the bottom 25th percentile), and DLM-predicted low variability eyes (DLPE: the subset of AE predicted to be low variability by the DLM). Deep learning models using only baseline VF/OCT/clinical data as input (DLM1), or also using a second VF (DLM2) were constructed to predict low VF variability (DLPE1 and DLPE2, respectively). Data were split 60/10/30 into train/val/test. Clinical trial simulations were performed only on the test set. We estimated the sample size necessary to detect treatment effects of 20% to 50% in MD slope with 80% power. Power was defined as the percentage of simulated clinical trials where the MD slope was significantly worse from the control. Clinical trials were simulated with visits every 3 months with a total of 10 visits. RESULTS: A total of 2817 eyes were included in the analysis. Deep learning models 1 and 2 achieved an area under the receiver operating characteristic curve of 0.73 (95% confidence interval [CI]: 0.68, 0.76) and 0.82 (95% CI: 0.78, 0.85) in forecasting low VF variability. When compared with including AE, using DLPE1 and DLPE2 reduced sample size to achieve 80% power by 30% and 38% for 30% treatment effect, and 31% and 38% for 50% treatment effect. CONCLUSIONS: Deep learning models can forecast eyes with low VF variability using data from a single baseline clinical visit. This can reduce sample size requirements, and potentially reduce the burden of future glaucoma clinical trials. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Association of retinal sensitivity to integrity of photoreceptor inner/outer segment junction in patients with diabetic macular edema.

PURPOSE: To evaluate the relationship between retinal sensitivity and the photoreceptor inner segment/outer segment (IS/OS) layer status in patients with diabetic macular edema (DME). DESIGN: Cross-sectional study. PARTICIPANTS: Twenty-five adult patients (37 eyes) diagnosed with DME and managed at the Wilmer Eye Institute, Johns Hopkins University (Baltimore, MD). METHODS: We obtained simultaneous fundus microperimetry (MP) and optical coherence tomography (OCT) of patients with DME using a combined MP/OCT system. The device recorded retinal sensitivity and retinal thickness on a 3-dimensional tomography map, and we performed a point-by-point analysis of the IS/OS layer integrity at every MP point. We also reviewed OCT scans to determine the type of DME, cystoid macular edema, or diffuse macular edema (absence of any cysts). In addition, fixation stability and fixation location were analyzed. MAIN OUTCOME MEASURES: Retinal point sensitivity measured by MP. RESULTS: Twenty-five patients (37 eyes: 29 male and 8 female; mean age, 64.16 years) with DME were enrolled. Fixation was centric in 30 eyes (81%), paracentric in 3 eyes (8%), and eccentric in 4 eyes (11%). Twenty-seven eyes had cystoid macular edema, and 10 eyes had diffuse macular edema. Mean central subfield thickness was 325 µm. We analyzed a total of 1036 individual MP points. Mean point sensitivity was 10.51 dB. A total of 793 points (76.5%) had IS/OS layer present, and 243 points (23.5%) had IS/OS layer disrupted. A mixed linear model, constructed to adjust for potential confounders and account for dependence between retinal points, revealed that the absence of the IS/OS junction was significantly associated with a 3.28-dB decrease in retinal point sensitivity (P<0.001). CONCLUSIONS: This novel index study demonstrates that disruption of the IS/OS junction is correlated with a significant decrease in point sensitivity in eyes with DME. Further studies are indicated to confirm and validate this relationship. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

A deep learning model incorporating spatial and temporal information successfully detects visual field worsening using a consensus based approach.

Glaucoma is a leading cause of irreversible blindness, and its worsening is most often monitored with visual field (VF) testing. Deep learning models (DLM) may help identify VF worsening consistently and reproducibly. In this study, we developed and investigated the performance of a DLM on a large population of glaucoma patients. We included 5099 patients (8705 eyes) seen at one institute from June 1990 to June 2020 that had VF testing as well as clinician assessment of VF worsening. Since there is no gold standard to identify VF worsening, we used a consensus of six commonly used algorithmic methods which include global regressions as well as point-wise change in the VFs. We used the consensus decision as a reference standard to train/test the DLM and evaluate clinician performance. 80%, 10%, and 10% of patients were included in training, validation, and test sets, respectively. Of the 873 eyes in the test set, 309 [60.6%] were from females and the median age was 62.4; (IQR 54.8-68.9). The DLM achieved an AUC of 0.94 (95% CI 0.93-0.99). Even after removing the 6 most recent VFs, providing fewer data points to the model, the DLM successfully identified worsening with an AUC of 0.78 (95% CI 0.72-0.84). Clinician assessment of worsening (based on documentation from the health record at the time of the final VF in each eye) had an AUC of 0.64 (95% CI 0.63-0.66). Both the DLM and clinician performed worse when the initial disease was more severe. This data shows that a DLM trained on a consensus of methods to define worsening successfully identified VF worsening and could help guide clinicians during routine clinical care.

Short-Term Outcomes of Hydrus Microstent With and Without Additional Canaloplasty During Cataract Surgery.

PRCIS: Compared with phacoemulsification and microstent alone, we observed that phacoemulsification with combined microstent and canaloplasty resulted in a significantly greater reduction in glaucoma medications while maintaining similar rates of intraocular pressure reduction and low complications. PURPOSE: The purpose of this study was to compare the outcomes of phacoemulsification combined with Hydrus Microstent (Alcon Inc.) implantation alone or in combination with canaloplasty (OMNI Surgical System, Sight Sciences Inc.). MATERIALS AND METHODS: Retrospective study of mild-to-moderate primary open angle glaucoma patients who underwent phacoemulsification with microstent alone (42 eyes of 42 patients) or in combination with canaloplasty (canaloplasty-microstent, 32 eyes of 32 patients). The mean number of ocular hypotensive medications and intraocular pressure were assessed preoperatively and postoperatively at 1 week and at 1, 3, and 6 months. Complications and secondary surgical interventions were recorded. Outcomes measures included the percentage of unmedicated eyes and surgical success at 6 months. Surgical success was defined as reaching the target intraocular pressure without medications or secondary surgical interventions. RESULTS: Mean intraocular pressure at 6 months was 14.1±3.5 mm Hg (13% reduction) after microstent alone and 13.6±3.1 mm Hg (17% reduction) after canaloplasty-microstent. Mean medications at 6 months were 0.57±0.9 (67% reduction) after microstent alone and 0.16±0.4 (88% reduction) after canaloplasty-microstent ( P< 0.05). At 6 months, 64.3% of microstent alone and 87.3% of canaloplasty-microstent were off all medications ( P =0.02). Success probabilities at 6 months were 44.5% for microstent alone and 70.0% for canaloplasty-microstent ( P =0.04). No secondary surgical interventions occurred in either group. CONCLUSIONS: Microstent combined with canaloplasty resulted in a significantly higher rate of medication-free status compared with microstent alone through 6 months.

Prediction of MIGS outcomes on second eyes using first eyes' response.

To evaluate how well outcomes following cataract extraction and microinvasive glaucoma surgery in one eye predict outcomes in sequential second eye. Retrospective study of 78 patients who underwent cataract extraction and microinvasive glaucoma surgery in both eyes. Linear regressions using Pearson correlation coefficients were used to evaluate correlations in intraocular pressure and glaucoma medication change between eyes. Multivariable logistic regression models were used to evaluate the associations between first-eye variables and the likelihood of second-eye surgical success at 6 months. Surgical success was defined as meeting target intraocular pressure without additional medications compared to baseline or secondary surgical interventions. Baseline ocular characteristics were comparable between fellow eyes, with the majority having mild glaucoma. Intraocular pressure changes between fellow eyes at 6 months were modestly correlated between eyes (R = 0.48; P < .001). Changes in glaucoma medications were strongly correlated between eyes at all time points, and month 6 demonstrated the most significant correlation (R = 0.80; P < .001). First and second eye cohorts achieved 82% and 83% surgical success. Multivariate analysis for predictive factors of successful second eye surgery showed patients with successful first eye surgery at 6 months were significantly more likely to have successful second eye surgery (odds ratio, 20.67; P < .001). Reductions in intraocular pressure and glaucoma medications at 6 months following surgery in first eyes are correlated to second eye reductions. Successful surgical outcomes at 6 months following first eye surgeries are strongly associated with successful sequential second eye outcomes.

The Impact of Achieving Target Intraocular Pressure on Glaucomatous Retinal Nerve Fiber Layer Thinning in a Treated Clinical Population.

PURPOSE: To estimate the effect of being below and above the clinician-set target intraocular pressure (IOP) on rates of glaucomatous retinal nerve fiber layer (RNFL) thinning in a treated real-world clinical population. DESIGN: Retrospective cohort study. METHODS: A total of 3256 eyes (1923 patients) with ≥5 reliable optical coherence tomography scans and 1 baseline visual field test were included. Linear mixed-effects modeling estimated the effects of the primary independent variables (mean target difference [measured IOP - target IOP] and mean IOP, mm Hg) on the primary dependent variable (RNFL slope, µm/y) while accounting for additional confounding variables (age, biological sex, race, baseline RNFL, baseline pachymetry, and disease severity). A spline term accounted for differential effects when above (target difference >0 mm Hg) and below (target difference ≤0 mm Hg) target pressure. RESULTS: Eyes below and above target had significantly different mean RNFL slopes (-0.44 vs -0.71 µm/y, P < .001). Each 1 mm Hg increase above target had a 0.143 µm/y faster rate of RNFL thinning (P < .001). Separating by disease severity, suspect, mild, moderate, and advanced glaucoma had 0.135 (P = .002), 0.116 (P = .009), 0.203 (P = .02), and 0.65 (P = .22) µm/y faster rates of RNFL thinning per 1 mm Hg increase, respectively. CONCLUSIONS: Being above the clinician-set target pressure is associated with more rapid RNFL thinning in suspect, mild, and moderate glaucoma. Faster rates of thinning were also present in advanced glaucoma, but statistical significance was limited by the lower sample size of eyes above target and the optical coherence tomography floor effect.