Detecting Visual Field Worsening From Optic Nerve Head and Macular Optical Coherence Tomography Thickness Measurements.

Purpose: Compare the use of optic disc and macular optical coherence tomography measurements to predict glaucomatous visual field (VF) worsening. Methods: Machine learning and statistical models were trained on 924 eyes (924 patients) with circumpapillary retinal nerve fiber layer (cp-RNFL) or ganglion cell inner plexiform layer (GC-IPL) thickness measurements. The probability of 24-2 VF worsening was predicted using both trend-based and event-based progression definitions of VF worsening. Additionally, the cp-RNFL and GC-IPL predictions were combined to produce a combined prediction. A held-out test set of 617 eyes was used to calculate the area under the curve (AUC) to compare cp-RNFL, GC-IPL, and combined predictions. Results: The AUCs for cp-RNFL, GC-IPL, and combined predictions with the statistical and machine learning models were 0.72, 0.69, 0.73, and 0.78, 0.75, 0.81, respectively, when using trend-based analysis as ground truth. The differences in performance between the cp-RNFL, GC-IPL, and combined predictions were not statistically significant. AUCs were highest in glaucoma suspects using cp-RNFL predictions and highest in moderate/advanced glaucoma using GC-IPL predictions. The AUCs for the statistical and machine learning models were 0.63, 0.68, 0.69, and 0.72, 0.69, 0.73, respectively, when using event-based analysis. AUCs decreased with increasing disease severity for all predictions. Conclusions: cp-RNFL and GC-IPL similarly predicted VF worsening overall, but cp-RNFL performed best in early glaucoma stages and GC-IPL in later stages. Combining both did not enhance detection significantly. Translational Relevance: cp-RNFL best predicted trend-based 24-2 VF progression in early-stage disease, while GC-IPL best predicted progression in late-stage disease. Combining both features led to minimal improvement in predicting progression.

Self-Reported Functional Vision in USH2A-Associated Retinal Degeneration as Measured by the Michigan Retinal Degeneration Questionnaire.

Purpose: The purpose of this study was to evaluate self-reported functional vision (FV) and the impact of vision loss in patients with USH2A-associated retinal degeneration using a patient-reported outcome (PRO) measure, the Michigan Retinal Degeneration Questionnaire (MRDQ), to correlate MRDQ scores with well-established visual function measurements. Design: An observational cross-sectional study (n = 93) of participants who had Usher Syndrome Type 2 (USH2, n = 55) or autosomal recessive non-syndromic retinitis pigmentosa (ARRP; n = 38) associated with biallelic variants in the USH2A gene. Methods: The study protocol was approved by all ethics boards and informed consent was obtained from each participant. Participants completed the MRDQ at the 48-month study follow-up visit. Disease duration was self-reported by participants. One-way ANOVA was used to compare subgroups (clinical diagnosis, age, disease duration, and full-field stimulus threshold [FST] Blue-Red mediation) on mean scores per domain. Spearman correlation coefficients were used to assess associations between MRDQ domains and visual/retinal function assessments. Results: Of the study sample, 58% were female participants and the median disease duration was 13 years. MRDQ domains were sensitive to differences between subgroups of clinical diagnosis, age, disease duration, and FST Blue-Red mediation. MRDQ domains correlated with static perimetry, microperimetry, full-field stimulus testing, and best-corrected visual acuity (BCVA). Conclusions: Self-reported FV measured by the MRDQ, when applied to USH2 and ARRP participants, had good distributional characteristics and correlated well with visual function tests. MRDQ adds a new dimension of understanding on vision-related functioning and establishes this PRO tool as an informative measure in evaluating USH2A outcomes.

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.

Development and validation of a questionnaire for assessing visual and auditory spatial localization abilities in dual sensory impairment.

Spatial localization is important for social interaction and safe mobility, and relies heavily on vision and hearing. While people with vision or hearing impairment compensate with their intact sense, people with dual sensory impairment (DSI) may require rehabilitation strategies that take both impairments into account. There is currently no tool for assessing the joint effect of vision and hearing impairment on spatial localization in this large and increasing population. To this end, we developed a novel Dual Sensory Spatial Localization Questionnaire (DS-SLQ) that consists of 35 everyday spatial localization tasks. The DS-SLQ asks participants about their difficulty completing different tasks using only vision or hearing, as well as the primary sense they rely on for each task. We administered the DS-SLQ to 104 participants with heterogenous vision and hearing status. Rasch analysis confirmed the psychometric validity of the DS-SLQ and the feasibility of comparing vision and hearing spatial abilities in a unified framework. Vision and hearing impairment were associated with decreased visual and auditory spatial abilities. Differences between vision and hearing abilities predicted overall sensory reliance patterns. In DSI rehabilitation, DS-SLQ may be useful for measuring vision and hearing spatial localization abilities and predicting the better sense for completing different spatial localization tasks.

Validation of a Wearable Virtual Reality Perimeter for Glaucoma Staging, The NOVA Trial: Novel Virtual Reality Field Assessment.

Purpose: Compare estimated sensitivities of SITA-Standard to the RATA-Standard algorithm of the Radius virtual reality perimeter (VRP), and measure concordance in glaucoma staging. Methods: One hundred adult glaucoma patients-half with suspect or mild glaucoma, and half with moderate or severe-from five clinics performed four 24-2 visual field tests during a single visit, two with the Humphrey Field Analyzer (HFA) and two with Radius, in randomized order: HRHR or RHRH. Only one eye was tested per participant. We used the Wilcoxon rank sum test with Bonferroni correction to compare distributions of estimated sensitivities across all 54 test locations over the 15 to 40 dB measurement range of the Radius. Weighted kappa measured concordance in glaucoma staging between two masked glaucoma experts using Medicare definitions of severity. Results: A total of 62 OD and 38 OS eyes were tested. Estimated sensitivities for SITA-Standard and RATA-Standard were not significantly different for OD, but were for OS-likely because of SITA-Standard OD and OS being significantly different in our sample, but not for RATA-Standard. Low agreement was observed between 15 to 22 dB. Concordance in glaucoma staging was high for both graders: kappa = 0.91 and kappa = 0.93. Average test duration was 298 seconds for RATA-Standard and 341 seconds for SITA-Standard. The correlation in mean deviation was 0.94. Conclusions: Estimated sensitivities of RATA-Standard are comparable to SITA-Standard between 23 to 40 dB with high concordance in glaucoma staging. Translational Relevance: Radius VRP is statistically noninferior to HFA when staging glaucoma using Medicare definitions.

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.

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.

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.

Functional Vision in Patients With Biallelic USH2A Variants.

PURPOSE: To describe functional vision (FV) and investigate the relationship between FV, visual acuity (VA), and hill of vision (VTOT) at baseline in patients with biallelic USH2A variants. DESIGN: Multicenter, international, cross-sectional study. METHODS: In individuals with biallelic disease-causing variants in USH2A, clinical diagnosis of Usher syndrome type 2 (USH2) or autosomal recessive nonsyndromic retinitis pigmentosa (ARRP) was based on history of hearing loss and audiology examinations. The VALVVFQ-48 was administered verbally to participants ≥18 years old. VA was measured in both eyes; VTOT was determined from static perimetry in the study eye (better VA). FV scores were calculated using Rasch analysis. RESULTS: Median age of 121 participants (76 with USH2, 45 with ARRP) was 41 years (range: 19-80); 54% were female. FV scores varied from -2.0 to 7.6 logits (median [interquartile range (IQR)]: 2.8 [1.5-3.8]). ARRP and USH2 participants had similar FV scores, both before [mean (95% CI): 2.8 (2.3-3.4) and 2.7 (2.3-3.2), respectively], and after [mean (95% CI): 2.5 (2.1-3.0) and 2.9 (2.6-3.3), respectively; P = .24] adjusting for age, VA, disease duration, and VTOT. VA and VTOT accounted for 29% and 26% of the variance in FV scores, respectively (P < .001 for each). Together, they accounted for 36% of variance observed. CONCLUSIONS: Biallelic USH2A variants were associated with a large range of FV, yet similar in ARRP and USH2, despite hearing loss in USH2. The modified VALVVFQ-48 we evaluated is not ideal for detecting the impact of USH2A-associated retinal degenerations on activities of daily living.

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.

Self-Reported Visual Ability Versus Task Performance in Individuals With Ultra-Low Vision.

Purpose: Visual functioning questionnaires are commonly used as patient-reported outcome measures to estimate visual ability. Performance measures, on the other hand, provide a direct measure of visual ability. For individuals with ultra-low vision (ULV; visual acuity (VA) <20/1600), the ultra-low vision visual functioning questionnaire (ULV-VFQ) and the Wilmer VRI-a virtual reality-based performance test-estimate self-reported and actual visual ability, respectively, for activities of daily living. But how well do self-reports from ULV-VFQ predict actual task performance in the Wilmer VRI? Methods: We administered a subset of 10 matching items from the ULV-VFQ and Wilmer VRI to 27 individuals with ULV. We estimated item measures (task difficulty) and person measures (visual ability) using Rasch analysis for ULV-VFQ and using latent variable signal detection theory for the Wilmer VRI. We then used regression analysis to compare person and item measure estimates from self-reports and task performance. Results: Item and person measures were modestly correlated between the two instruments, with r2 = 0.47 (P = 0.02) and r2 = 0.36 (P = 0.001), demonstrating that self-reports are an imperfect predictor of task difficulty and performance. Conclusions: While self-reports impose a lower demand for equipment and personnel, actual task performance should be measured to assess visual ability in ULV. Translational Relevance: Visual performance measures should be the preferred outcome measure in clinical trials recruiting individuals with ULV. Virtual reality can be used to standardize tasks.

Mitigating age-related somatic mutation burden.

Genomes are inherently unstable and require constant DNA repair to maintain their genetic information. However, selective pressure has optimized repair mechanisms in somatic cells only to allow transmitting genetic information to the next generation, not to maximize sequence integrity long beyond the reproductive age. Recent studies have confirmed that somatic mutations, due to errors during genome repair and replication, accumulate in tissues and organs of humans and model organisms. Here, we describe recent advances in the quantitative analysis of somatic mutations in vivo. We also review evidence for or against a possible causal role of somatic mutations in aging. Finally, we discuss options to prevent, delay or eliminate de novo, random somatic mutations as a cause of aging.

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.

Forecasting Risk of Future Rapid Glaucoma Worsening Using Early Visual Field, OCT, and Clinical Data.

PURPOSE: To assess whether we can forecast future rapid visual field (VF) worsening using deep learning models (DLMs) trained on early VF, OCT, and clinical data. DESIGN: A retrospective cohort study. SUBJECTS: In total, 4536 eyes from 2962 patients. Overall, 263 (5.80%) eyes underwent rapid VF worsening (mean deviation slope less than -1 dB/year across all VFs). METHODS: We included eyes that met the following criteria: (1) followed for glaucoma or suspect status; (2) had at least 5 longitudinal reliable VFs (VF1, VF2, VF3, VF4, and VF5); and (3) had 1 reliable baseline OCT scan (OCT1) and 1 set of baseline clinical measurements (clinical1) at the time of VF1. We designed a DLM to forecast future rapid VF worsening. The input consisted of spatially oriented total deviation values from VF1 (including or not including VF2 and VF3 in some models) and retinal nerve fiber layer thickness values from the baseline OCT. We passed this VF/OCT stack into a vision transformer feature extractor, the output of which was concatenated with baseline clinical data before putting it through a linear classifier to predict the eye's risk of rapid VF worsening across the 5 VFs. We compared the performance of models with differing inputs by computing area under the curve (AUC) in the test set. Specifically, we trained models with the following inputs: (1) model V: VF1; (2) VC: VF1+ Clinical1; (3) VO: VF1+ OCT1; (4) VOC: VF1+ Clinical1+ OCT1; (5) V2: VF1 + VF2; (6) V2OC: VF1 + VF2 + Clinical1 + OCT1; (7) V3: VF1 + VF2 + VF3; and (8) V3OC: VF1 + VF2 + VF3 + Clinical1 + OCT1. MAIN OUTCOME MEASURES: The AUC of DLMs when forecasting rapidly worsening eyes. RESULTS: Model V3OC best forecasted rapid worsening with an AUC (95% confidence interval [CI]) of 0.87 (0.77-0.97). Remaining models in descending order of performance and their respective AUC (95% CI) were as follows: (1) model V3 (0.84 [0.74-0.95]), (2) model V2OC (0.81 [0.70-0.92]), (3) model V2 (0.81 [0.70-0.82]), (4) model VOC (0.77 [0.65-0.88]), (5) model VO (0.75 [0.64-0.88]), (6) model VC (0.75 [0.63-0.87]), and (7) model V (0.74 [0.62-0.86]). CONCLUSIONS: Deep learning models can forecast future rapid glaucoma worsening with modest to high performance when trained using data from early in the disease course. Including baseline data from multiple modalities and subsequent visits improves performance beyond using VF data alone. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

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.

Measuring visual information gathering in individuals with ultra low vision using virtual reality.

People with ULV (visual acuity ≤ 20/1600 or 1.9 logMAR) lack form vision but have rudimentary levels of vision that can be used for a range of activities in daily life. However, current clinical tests are designed to assess form vision and do not provide information about the range of visually guided activities that can be performed in daily life using ULV. This is important to know given the growing number of clinical trials that recruit individuals with ULV (e.g., gene therapy, stem cell therapy) or restore vision to the ULV range in the blind (visual prosthesis). In this study, we develop a set of 19 activities (items) in virtual reality involving spatial localization/detection, motion detection, and direction of motion that can be used to assess visual performance in people with ULV. We estimated measures of item difficulty and person ability on a relative d prime (d') axis using a signal detection theory based analysis for latent variables. The items represented a range of difficulty levels (- 1.09 to 0.39 in relative d') in a heterogeneous group of individuals with ULV (- 0.74 to 2.2 in relative d') showing the instrument's utility as an outcome measure in clinical trials.

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.

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.

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).

Measuring visually guided motor performance in ultra low vision using virtual reality.

Introduction: Ultra low vision (ULV) refers to profound visual impairment where an individual cannot read even the top line of letters on an ETDRS chart from a distance of 0.5 m. There are limited tools available to assess visual ability in ULV. The aim of this study was to develop and calibrate a new performance test, Wilmer VRH, to assess hand-eye coordination in individuals with ULV. Methods: A set of 55 activities was developed for presentation in a virtual reality (VR) headset. Activities were grouped into 2-step and 5-step items. Participants performed a range of tasks involving reaching and grasping, stacking, sorting, pointing, throwing, and cutting. Data were collected from 20 healthy volunteers under normal vision (NV) and simulated ULV (sULV) conditions, and from 33 participants with ULV. Data were analyzed using the method of successive dichotomizations (MSD), a polytomous Rasch model, to estimate item (difficulty) and person (ability) measures. MSD was applied separately to 2-step and 5-step performance data, then merged to a single equal interval scale. Results: The mean ±SD of completion rates were 98.6 ± 1.8%, 78.2 ± 12.5% and 61.1 ±34.2% for NV, sULV and ULV, respectively. Item measures ranged from -1.09 to 5.7 logits and - 4.3 to 4.08 logits and person measures ranged from -0.03 to 4.2 logits and -3.5 to 5.2 logits in sULV and ULV groups, respectively. Ninety percent of item infits were within the desired range of [0.5,1.5], and 97% of person infits were within that range. Together with item and person reliabilities of 0.94 and 0.91 respectively, this demonstrates unidimensionality of Wilmer VRH. A Person Item map showed that the items were well-targeted to the sample of individuals with ULV in the study. Discussion: We present the development of a calibrated set of activities in VR that can be used to assess hand-eye coordination in individuals with ULV. This helps bridge a gap in the field by providing a validated outcome measure that can be used in vision restoration trials that recruit people with ULV, and to assess rehabilitation outcomes in people with ULV.