Ocular Hypertension Treatment Study Research Articles

Primary Open-Angle Glaucoma Polygenic Risk Score and Risk of Disease Onset

Primary open-angle glaucoma (POAG) is a heritable disease. A polygenic risk score (PRS) threshold may be used to identify individuals at low risk of disease onset. To assess the utility of a POAG PRS to identify ocular hypertensive individuals at low risk of disease onset. This is a post hoc analysis of the Ocular Hypertension Treatment Study (OHTS), a multicenter randomized clinical trial across 22 centers in the US conducted among 1636 participants with ocular hypertension from February 1994 to April 2019 with available genetic data. Of the 1636 original participants, 1077 had available genetic data; after excluding 67 for missing data, data quality concerns, or ancestry other than European or African, 1010 were included in the present analysis. Data for this report were analyzed from November 2023 to June 2024. From 1994-2002, participants were randomized to receive topical intraocular pressure (IOP)-lowering medications. From 2002 onwards, all participants were given topical IOP-lowering medications. Twenty-year conversion rates by POAG PRS threshold, baseline randomization status, and OHTS clinical risk tertile. Among the 1010 participants in this study, 563 (65.8%) were female, and the mean (SD) age was 55.9 (9.4) years. In a mixed-effects logistic regression model adjusted for OHTS risk factors for conversion to POAG and randomization status, a PRS under the 48th percentile was associated with a 1.49 times higher likelihood of disease-free status after 20 years of follow-up (95% CI, 1.04-2.15; P = .03; unadjusted hazard ratio [HR], 1.64; 95% CI, 1.13-2.38; P = .009), compared with high polygenic risk. When we stratified the trial cohort into nongenetic OHTS clinical risk tertiles, the largest differences in survival probability at 20 years based on PRS threshold was observed in eyes in the highest tertile, initial observation group (20-year conversion rate: 61.1% in the high polygenic risk group vs 23.8% in the low polygenic risk group; 95% CI, -63.0 to -11.6; P = .01), with randomization to early treatment partially mitigating the effect of high genetic risk (20-year conversion rate: 37.3% in the high polygenic risk group vs 24.1% in the low polygenic risk group; 95% CI, -35.6 to 9.3%; P = .32). These findings support considering use of a POAG PRS threshold to identify individuals at low risk of disease onset, with those below the PRS threshold more likely to have lower conversion rates over 20 years. Among those considered at highest risk based on the OHTS clinical risk model, early treatment may partially offset the association with high genetic risk but provide limited benefit for those with low genetic risk. ClinicalTrials.gov Identifier: NCT00000125.

Quality of Life and Primary Open-Angle Glaucoma in the Ocular Hypertension Treatment Study

If preperimetric glaucoma reduces patient-reported vision-related quality of life (VRQoL), clinicians might consider earlier and more aggressive treatment of some patients with ocular hypertension and early glaucoma. To determine the impact of preperimetric glaucoma and early glaucomatous visual field (VF) loss on participants' VRQoL compared with participants who did not develop glaucoma in the Ocular Hypertension Treatment Study (OHTS). This cross-sectional study used data from participants enrolled in the OHTS from 1994 to 1996 who completed 20-year examination follow-up and VRQoL surveys from January 7, 2016, to November 19, 2019. Diagnosis of primary open-angle glaucoma (POAG) required reproducible changes in VFs or optic discs as determined by reading centers and attributed to glaucoma by a masked end point committee. Data analysis was performed from June 27, 2023 to March 7, 2024. Ocular hypertension and glaucoma. Cross-sectional analysis of Rasch-calibrated total scores of the National Eye Institute Visual Function Questionnaire (VFQ) and the Glaucoma Quality of Life (GQL) survey was performed. Total scores were standardized from 0 (poor) to 100 (good). The main outcomes were differences in total VRQoL scores between participants who did not develop POAG (control) and 4 mutually exclusive POAG groups: unilateral or bilateral disc POAG with no glaucomatous VF loss in either eye and unilateral or bilateral VF POAG with or without disc changes. Differences in total VRQoL scores were analyzed using univariate and multivariate linear regression models. Of 1636 participants originally enrolled in the OHTS, 679 (42%) completed the 20-year examination and VRQoL surveys. Of those participants completing 20-year follow-up, mean (SD) participant age at follow-up was 73.8 (7.7) years, and 412 of 679 participants (60.7%) self-reported as female. The mean (SD) Rasch-calibrated total VFQ scores were 72.5 (13.4) in the no POAG group (control) vs 72.7 (13.8) in the unilateral disc POAG group (difference, 0.17; 95% CI, -3.17 to 3.41; P = .92), 73.4 (14.0) in the bilateral disc POAG group (difference, 0.92; 95% CI, -2.93 to 4.77; P = .64), 69.2 (14.5) in the unilateral VF POAG group (difference, 3.33; 95% CI, -6.38 to -0.27; P = .03), and 58.5 (16.9) in the bilateral VF POAG group (difference, -13.96; 95% CI, -17.73 to -10.19; P<.001). Similar results were found for the GQL questionnaire. In this cross-sectional study, among the surviving participants of OHTS who completed the 20-year follow-up examination, those participants who developed preperimetric POAG but no glaucomatous VF loss did not report lower VFQoL compared with participants who did not develop POAG. This finding should allow clinicians and patients the time to determine the frequency of examinations and whether the initiation of treatment is appropriate.

Diagnosing Glaucoma Based on the Ocular Hypertension Treatment Study Dataset Using Chat Generative Pre-Trained Transformer as a Large Language Model

To evaluate the capabilities of Chat Generative Pre-Trained Transformer (ChatGPT), as a large language model (LLM), for diagnosing glaucoma using the Ocular Hypertension Treatment Study (OHTS) dataset, and comparing the diagnostic capability of ChatGPT 3.5 and ChatGPT 4.0. Prospective data collection study. A total of 3170 eyes of 1585 subjects from the OHTS were included in this study. We selected demographic, clinical, ocular, visual field, optic nerve head photo, and history of disease parameters of each participant and developed case reports by converting tabular data into textual format based on information from both eyes of all subjects. We then developed a procedure using the application programming interface of ChatGPT, a LLM-based chatbot, to automatically input prompts into a chat box. This was followed by querying 2 different generations of ChatGPT (versions 3.5 and 4.0) regarding the underlying diagnosis of each subject. We then evaluated the output responses based on several objective metrics. Area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, and F1 score. Chat Generative Pre-Trained Transformer 3.5 achieved AUC of 0.74, accuracy of 66%, specificity of 64%, sensitivity of 85%, and F1 score of 0.72. Chat Generative Pre-Trained Transformer 4.0 obtained AUC of 0.76, accuracy of 87%, specificity of 90%, sensitivity of 61%, and F1 score of 0.92. The accuracy of ChatGPT 4.0 in diagnosing glaucoma based on input data from OHTS was promising. The overall accuracy of ChatGPT 4.0 was higher than ChatGPT 3.5. However, ChatGPT 3.5 was found to be more sensitive than ChatGPT 4.0. In its current forms, ChatGPT may serve as a useful tool in exploring disease status of ocular hypertensive eyes when specific data are available for analysis. In the future, leveraging LLMs with multimodal capabilities, allowing for integration of imaging and diagnostic testing as part of the analyses, could further enhance diagnostic capabilities and enhance diagnostic accuracy. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling

Deep learning has enabled breakthroughs in automated diagnosis from medical imaging, with many successful applications in ophthalmology. However, standard medical image classification approaches only assess disease presence at the time of acquisition, neglecting the common clinical setting of longitudinal imaging. For slow, progressive eye diseases like age-related macular degeneration (AMD) and primary open-angle glaucoma (POAG), patients undergo repeated imaging over time to track disease progression and forecasting the future risk of developing a disease is critical to properly plan treatment. Our proposed Longitudinal Transformer for Survival Analysis (LTSA) enables dynamic disease prognosis from longitudinal medical imaging, modeling the time to disease from sequences of fundus photography images captured over long, irregular time periods. Using longitudinal imaging data from the Age-Related Eye Disease Study (AREDS) and Ocular Hypertension Treatment Study (OHTS), LTSA significantly outperformed a single-image baseline in 19/20 head-to-head comparisons on late AMD prognosis and 18/20 comparisons on POAG prognosis. A temporal attention analysis also suggested that, while the most recent image is typically the most influential, prior imaging still provides additional prognostic value.

Identifying Factors Associated With Fast Visual Field Progression in Patients With Ocular Hypertension Based on Unsupervised Machine Learning.

We developed unsupervised machine learning models to identify different subtypes of patients with ocular hypertension in terms of visual field (VF) progression and discovered 4 subtypes with different trends of VF worsening. We then identified factors associated with fast VF progression. To identify ocular hypertension (OHT) subtypes with different trends of visual field (VF) progression based on unsupervised machine learning and to discover factors associated with fast VF progression. Cross-sectional and longitudinal study. A total of 3133 eyes of 1568 ocular hypertension treatment study (OHTS) participants with at least 5 follow-up VF tests were included in the study. We used a latent class mixed model (LCMM) to identify OHT subtypes using standard automated perimetry (SAP) mean deviation (MD) trajectories. We characterized the subtypes based on demographic, clinical, ocular, and VF factors at the baseline. We then identified factors driving fast VF progression using generalized estimating equation (GEE) and justified findings qualitatively and quantitatively. Rates of SAP mean deviation (MD) change. The LCMM model discovered four clusters (subtypes) of eyes with different trajectories of MD worsening. The number of eyes in clusters were 794 (25%), 1675 (54%), 531 (17%), and 133 (4%). We labeled the clusters as improvers (cluster 1), stables (cluster 2), slow progressors (cluster 3), and fast progressors (cluster 4) based on their mean of MD decline rate, which were 0.08, -0.06, -0.21, and -0.45dB/year, respectively. Eyes with fast VF progression had higher baseline age, intraocular pressure (IOP), pattern standard deviation (PSD) and refractive error (RE), but lower central corneal thickness (CCT). Fast progression was associated with being male, heart disease history, diabetes history, African American race, and stroke history. Unsupervised clustering can objectively identify OHT subtypes including those with fast VF worsening without human expert intervention. Fast VF progression was associated with higher history of stroke, heart disease and diabetes. Fast progressors were more from African American race, males, and had higher incidence of glaucoma conversion. Subtyping can provide guidance for adjusting treatment plans to slow vision loss and improve quality of life of patients with a faster progression course.

Standardizing the diagnosis and treatment of ocular hypertension based on evidence-based medicine

Ocular hypertension (OHT) refers to a condition in which the intraocular pressure increases without causing glaucomatous optic nerve changes or visual field damage. The incidence rate of OHT in people over 40 years old is as high as 4% to 10%. According to the OHT Treatment Study (OHTS), the incidence of primary open angle glaucoma (POAG) among OHT patients is increasing year by year, so it is necessary to conduct long-term follow-up. This article elaborates on five major risk factors for the progression of OHT to POAG: age, intraocular pressure, vertical cup-disc ratio, pattern standard deviation of visual field, and central corneal thickness. It also summarizes other potential risk factors, such as long-term fluctuations in intraocular pressure, asymmetry of intraocular pressure and visual field between the two eyes, structural phenotypes of the optic disk, and optic disk hemorrhage. Predicting the risk of OHT progression to POAG based on risk factors, patients with different risk levels require different timing for treatment initiation and follow-up intervals. Those with higher risks should start preventive treatment earlier and have shorter follow-up intervals. Both drug therapy and selective laser trabeculoplasty can serve as initial treatment options for OHT. Combining evidence-based medicine research and individualized evaluation of treatment can enhance the clinical diagnosis and treatment level of OHT.

Predicting Glaucoma Before Onset Using a Large Language Model Chatbot

To investigate the capability of ChatGPT for forecasting the conversion from ocular hypertension (OHT) to glaucoma based on the Ocular Hypertension Treatment Study (OHTS). Retrospective case-control study. A total of 3008 eyes of 1504 subjects from the OHTS were included in the study. We selected demographic, clinical, ocular, optic nerve head, and visual field (VF) parameters 1 year before glaucoma development from the OHTS participants. Subsequently, we developed queries by converting tabular parameters into textual format based on both eyes of all participants. We used the ChatGPT application program interface (API) to automatically perform ChatGPT prompting for all subjects. We then investigated whether ChatGPT can accurately forecast conversion from OHT to glaucoma based on various objective metrics. Accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and weighted F1 score. ChatGPT4.0 demonstrated an accuracy of 75%, AUC of 0.67, sensitivity of 56%, specificity of 78%, and weighted F1 score of 0.77 in predicting conversion to glaucoma 1 year before onset. ChatGPT3.5 provided an accuracy of 61%, AUC of 0.62, sensitivity of 64%, specificity of 59%, and weighted F1 score of 0.63 in predicting conversion to glaucoma 1 year before onset. The performance of ChatGPT4.0 in forecasting development of glaucoma 1 year before onset was reasonable. The overall performance of ChatGPT4.0 was consistently higher than ChatGPT3.5. Large language models (LLMs) hold great promise for augmenting glaucoma research capabilities and enhancing clinical care. Future efforts in creating ophthalmology-specific LLMs that leverage multimodal data in combination with active learning may lead to more useful integration with clinical practice and deserve further investigations.

The need for more pragmatic trials in glaucoma research.

There have been a number of clinical trials in glaucoma research published in the past two decades. Most of these trials were designed to evaluate very specific issues in selected populations placing them in the explanatory end of the pragmatic-explanatory continuum. The purpose of this study was to assess the level of pragmatism of published randomized controlled trials in glaucoma. A PubMed search using 'glaucoma' from 1995 to 2022 and randomized controlled trial (RCT) article type was done. Each study was assessed by three independent examiners using the Pragmatic-Explanatory Continuum Indicator Summary version 2 (PRECIS-2) toolkit. Scores were calculated for each study to determine the level of pragmatism. A summed score ≥36 was indicative of a very pragmatic study. Thirty-two different articles were included in the analysis. These papers represented 13 different landmark trials. The median PRECIS-2 score was 32 (range, 25 for the Early Manifest Glaucoma Trial (EMGT) to 34 to the Collaborative Normal Tension Glaucoma Study (CNTGS) and the Ocular Hypertension Treatment Study). The Treatment of Advanced Glaucoma Study (TAGS), was considered very pragmatic and scored 33 points. Despite the number of RCTs in glaucoma, there is still a need for more pragmatic studies.

Polygenic Risk Scores for Glaucoma Onset in the Ocular Hypertension Treatment Study

Primary open-angle glaucoma (POAG) is a highly heritable disease, with 127 identified risk loci to date. Polygenic risk score (PRS) may provide a clinically useful measure of aggregate genetic burden and improve patient risk stratification. To assess whether a PRS improves prediction of POAG onset in patients with ocular hypertension. This was a post hoc analysis of the Ocular Hypertension Treatment Study. Data were collected from 22 US sites with a mean (SD) follow-up of 14.0 (6.9) years. A total of 1636 participants were followed up from February 1994 to December 2008; 1077 participants were enrolled in an ancillary genetics study, of which 1009 met criteria for this analysis. PRS was calculated using summary statistics from the largest cross-ancestry POAG meta-analysis, with weights trained using 8 813 496 variants from 449 186 cross-ancestry participants in the UK Biobank. Data were analyzed from July 2022 to December 2023. From February 1994 to June 2002, participants were randomized to either topical intraocular pressure-lowering medication or close observation. After June 2002, both groups received medication. Outcome measures were hazard ratios for POAG onset. Concordance index and time-dependent areas under the receiver operating characteristic curve were used to compare the predictive performance of multivariable Cox proportional hazards models. Of 1009 included participants, 562 (55.7%) were female, and the mean (SD) age was 55.9 (9.3) years. The mean (SD) PRS was significantly higher for 350 POAG converters (0.24 [0.95]) compared with 659 nonconverters (-0.12 [1.00]) (P < .001). POAG risk increased 1.36% (95% CI, 1.08-1.64) with each higher PRS decile, with conversion ranging from 9.52% (95% CI, 7.09-11.95) in the lowest PRS decile to 21.81% (95% CI, 19.37-24.25) in the highest decile. Comparison of low-risk and high-risk PRS tertiles showed a 2.0-fold increase in 20-year POAG risk for participants of European and African ancestries. In the subgroup randomized to delayed treatment, each increase in PRS decile was associated with a 0.52-year (95% CI, 0.01-1.03) decrease in age at diagnosis (P = .047). No significant linear association between PRS and age at POAG diagnosis was present in the early treatment group. Prediction models significantly improved with the addition of PRS as a covariate (C index = 0.77) compared with the Ocular Hypertension Treatment Study baseline model (C index = 0.75) (P < .001). Each 1-SD higher PRS conferred a mean hazard ratio of 1.25 (95% CI, 1.13-1.44) for POAG onset. Higher PRS was associated with increased risk for POAG in patients with ocular hypertension. The inclusion of a PRS improved the prediction of POAG onset. ClinicalTrials.gov Identifier: NCT00000125.

Machine Learning-Derived Baseline Visual Field Patterns Predict Future Glaucoma Onset in the Ocular Hypertension Treatment Study.

The Ocular Hypertension Treatment Study (OHTS) identified risk factors for primary open-angle glaucoma (POAG) in patients with ocular hypertension, including pattern standard deviation (PSD). Archetypal analysis, an unsupervised machine learning method, may offer a more interpretable approach to risk stratification by identifying patterns in baseline visual fields (VFs). There were 3272 eyes available in the OHTS. Archetypal analysis was applied using 24-2 baseline VFs, and model selection was performed with cross-validation. Decomposition coefficients for archetypes (ATs) were calculated. A penalized Cox proportional hazards model was implemented to select discriminative ATs. The AT model was compared to the OHTS model. Associations were identified between ATs with both POAG onset and VF progression, defined by mean deviation change per year. We selected 8494 baseline VFs. Optimal AT count was 19. The highest prevalence ATs were AT9, AT11, and AT7. The AT-based prediction model had a C-index of 0.75 for POAG onset. Multivariable models demonstrated that a one-interquartile range increase in the AT5 (hazard ratio [HR] = 1.14; 95% confidence interval [CI], 1.04-1.25), AT8 (HR = 1.22; 95% CI, 1.09-1.37), AT15 (HR = 1.26; 95% CI, 1.12-1.41), and AT17 (HR = 1.17; 95% CI, 1.03-1.31) coefficients conferred increased risk of POAG onset. AT5, AT10, and AT14 were significantly associated with rapid VF progression. In a subgroup analysis by high-risk ATs (>95th percentile or <75th percentile coefficients), PSD lost significance as a predictor of POAG in the low-risk group. Baseline VFs, prior to detectable glaucomatous damage, contain occult patterns representing early changes that may increase the risk of POAG onset and VF progression in patients with ocular hypertension. The relationship between PSD and POAG is modified by the presence of high-risk patterns at baseline. An AT-based prediction model for POAG may provide more interpretable glaucoma-specific information in a clinical setting.

Visual field progression patterns in the ocular hypertension treatment study correspond to vulnerability regions of the disc.

To determine the locations on the 24-2 visual field (VF) testing grid that are most likely to progress in patients with ocular hypertension (OHTN). Based on a structural model of superior and inferior areas of relative vulnerability at the optic disc, we hypothesized that the nasal and paracentral regions are more prone to show a reduction in sensitivity. Posthoc analysis of data collected in phases 1 and 2 of the Ocular Hypertension Treatment Study (OHTS). A pointwise analysis was applied to determine the progression patterns in the early and delayed treatment groups. Each group's progression rate and frequency were calculated for each of the 52 locations corresponding to the 24-2 VF strategy, using trend- and event-based analyses, respectively. For the event-based analysis, the events were most commonly found in the nasal and paracentral regions. The same regions, with some modest variation, were found to have the fastest rates of progression (ROP) measured with trend analysis. A similar pattern of progression was observed in both the early and delayed treatment groups. The difference in event rates and ROP between the early and delayed treatment groups was also greatest in the nasal and paracentral regions. Development of VF loss in ocular hypertensive eyes appears to be consistent with the vulnerability zones previously described in glaucomatous eyes with established VF loss. Ocular hypotensive treatment likely helps to slow the rate of progression in these regions. This suggests that careful monitoring of these locations may be useful.

Deep Learning Identifies High-Quality Fundus Photographs and Increases Accuracy in Automated Primary Open Angle Glaucoma Detection.

To develop and evaluate a deep learning (DL) model to assess fundus photograph quality, and quantitatively measure its impact on automated POAG detection in independent study populations. Image quality ground truth was determined by manual review of 2815 fundus photographs of healthy and POAG eyes from the Diagnostic Innovations in Glaucoma Study and African Descent and Glaucoma Evaluation Study (DIGS/ADAGES), as well as 11,350 from the Ocular Hypertension Treatment Study (OHTS). Human experts assessed a photograph as high quality if of sufficient quality to determine POAG status and poor quality if not. A DL quality model was trained on photographs from DIGS/ADAGES and tested on OHTS. The effect of DL quality assessment on DL POAG detection was measured using area under the receiver operating characteristic (AUROC). The DL quality model yielded an AUROC of 0.97 for differentiating between high- and low-quality photographs; qualitative human review affirmed high model performance. Diagnostic accuracy of the DL POAG model was significantly greater (P < 0.001) in good (AUROC, 0.87; 95% CI, 0.80-0.92) compared with poor quality photographs (AUROC, 0.77; 95% CI, 0.67-0.88). The DL quality model was able to accurately assess fundus photograph quality. Using automated quality assessment to filter out low-quality photographs increased the accuracy of a DL POAG detection model. Incorporating DL quality assessment into automated review of fundus photographs can help to decrease the burden of manual review and improve accuracy for automated DL POAG detection.

Deep learning models to predict primary open-angle glaucoma.

Glaucoma is a major cause of blindness and vision impairment worldwide, and visual field (VF) tests are essential for monitoring the conversion of glaucoma. While previous studies have primarily focused on using VF data at a single time point for glaucoma prediction, there has been limited exploration of longitudinal trajectories. Additionally, many deep learning techniques treat the time-to-glaucoma prediction as a binary classification problem (glaucoma Yes/No), resulting in the misclassification of some censored subjects into the nonglaucoma category and decreased power. To tackle these challenges, we propose and implement several deep-learning approaches that naturally incorporate temporal and spatial information from longitudinal VF data to predict time-to-glaucoma. When evaluated on the Ocular Hypertension Treatment Study (OHTS) dataset, our proposed convolutional neural network (CNN)-long short-term memory (LSTM) emerged as the top-performing model among all those examined. The implementation code can be found online (https://github.com/rivenzhou/VF_prediction).

What is the Optimal Frequency of Visual Field Testing to Detect Rapid Progression Among Hypertensive Eyes?

We evaluated 16,351 visual field (VF) tests from Ocular Hypertension Treatment Study (OHTS) database and showed that more frequent testing resulted in a shorter time to detect glaucoma progression, with the best trade-off being the 6-month intervals for high-risk and 12 months for low-risk patients. To investigate the effect of different testing intervals on time to detect visual field progression in eyes with ocular hypertension. A total of 16,351 reliable 30-2 VF tests from 1575 eyes of the OHTS-1 observation arm with a mean (95% CI) follow-up of 4.8 (4.7-4.8) years were analyzed. Computer simulations (n = 10,000 eyes) based on mean deviation values and the residuals of risk groups (according to their baseline 5 y risk of developing primary open angle glaucoma: low, medium, and high risk) were performed to estimate time to detect progression with testing intervals of 4, 6, 12, and 24 months using linear regression. The time to detect VF progression ( P < 5%) at 80% power was calculated based on the mean deviation slope of -0.42 dB/year. We assessed the time to detect a -3 dB loss as an estimate of clinically meaningful perimetric loss. At 80% power, based on the progression of -0.42 dB/year, the best trade-off to detect significant rates of VF change to clinically meaningful perimetric loss in high, medium, and low-risk patients was 6, 6, and 12-month intervals, respectively. Given the importance of not missing the conversion to glaucoma, the frequency of testing used in OHTS (6 mo) was optimal for the detection of progression in high-risk patients. Low-risk patients could potentially be tested every 12 months to optimize resource utilization.

Risk Calculation in the Medication Arm of the Ocular Hypertension Treatment Study

Risk assessment is integral to the management of individuals with ocular hypertension (OHTN). This study aims to determine the predictive accuracy of the Ocular Hypertension Treatment Study 5-year risk calculator (OHTS calculator) among treated patients with OHTN by applying it to patients randomized to the Ocular Hypertension Treatment Study (OHTS) medication arm. Post hoc secondary analysis of a randomized clinical trial. Individuals participating in the OHTS who were randomized to the medication arm. Only participants with complete baseline data in both eyes were included (n=726). The hazard ratios (HRs) of the medication group in OHTS were compared to the HR used for the OHTS calculator using the z-test statistic to establish the OHTS calculator's generalizability to the OHTS medication arm. The performance of the OHTS calculator among the OHTS medication group was evaluated twice, using both untreated baseline intraocular pressure (IOP) and average treated IOP during the first 24 months for the IOP variable. The performance was determined based on the model's accuracy in estimating the risk of reaching an OHTS primary open-angle glaucoma (POAG) end point using calibration chi-square and discriminating between participants who did or did not develop POAG. The HRs for the OHTS medication arm were not significantly different from those used in the OHTS calculator for untreated OHTN derived from observation arm data (P>0.1). Based on the calibration chi-square test for the medication group, the OHTS calculator prediction model had good predictive accuracy when using the mean treated IOP and poorer predictive accuracy with the untreated baseline IOP (chi-square 10 and 29, respectively). The model had good discrimination with treated IOP (c-statistic=0.77), comparable to what has been reported for the OHTS calculator in the OHTS observation group. The OHTS calculator can be applied to treated patients with OHTN, and repeat risk calculation after initiating IOP reduction may provide useful information that can aid in disease management. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Retinal Nerve Fiber Layer Optical Texture Analysis: Detecting Axonal Fiber Bundle Defects in Patients with Ocular Hypertension

To apply retinal nerve fiber layer (RNFL) optical texture analysis (ROTA) to investigate the prevalence, patterns, and risk factors of RNFL defects in patients with ocular hypertension (OHT) who showed normal optic disc and RNFL configuration in clinical examination, normal RNFL thickness on OCT analysis, and normal visual field (VF) results. Cross-sectional study. Six hundred eyes of 306 patients with OHT. All participants underwent clinical examination of the optic disc and RNFL, OCT RNFL imaging, and 24-2 standard automated perimetry. To detect RNFL defects, ROTA was applied. The risk score for glaucoma development was calculated according to the Ocular Hypertension Treatment Study and European Glaucoma Prevention Study (OHTS-EGPS) risk prediction model. Risk factors associated with RNFL defects were analyzed using multilevel logistic regression analysis. Prevalence of RNFL defects. The average intraocular pressure (IOP) measured from 3 separate visits within 6 months was 24.9 ± 1.8 mmHg for the eye with higher IOP and 23.7 ± 1.7 mmHg for the eye with lower IOP; the respective central corneal thicknesses were 568.7 ± 30.8 μm and 568.8 ± 31.2 μm. Of 306 patients with OHT, 10.8% (33 patients, 37 eyes) demonstrated RNFL defects in ROTA in at least 1 eye. Of the 37 eyes with RNFL defects, the superior arcuate bundle was the most frequently involved (62.2%), followed by the superior papillomacular bundle (27.0%) and the inferior papillomacular bundle (21.6%). Papillofoveal bundle defects were observed in 10.8% of eyes. The smallest RNFL defect spanned 0.0° along Bruch's membrane opening margin, whereas the widest RNFL defect extended over 29.3°. Age (years) (odds ratio [OR], 1.08; 95% confidence interval [CI], 1.03-1.13), VF pattern standard deviation (decibels [dB]) (OR, 1.82; 95% CI, 1.01-3.29), cup volume (mm3) (OR, 1.24; 95% CI, 1.01-1.53), and the OHTS-EPGS risk score (OR, 1.04; 95% CI, 1.01-1.07) were associated with RNFL defects. A considerable proportion of patients with OHT who showed no signs of optic disc and RNFL thickness abnormalities on clinical and OCT examination exhibited RNFL defects on ROTA. Axonal fiber bundle defects on ROTA may represent the earliest discernible sign of glaucoma in the glaucoma continuum. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

The Patterns of Visual Field Defects in Primary Angle-Closure Glaucoma Compared to High-Tension Glaucoma and Normal-Tension Glaucoma

Introduction: The aim of this study was to compare the patterns of visual field (VF) defects in primary angle-closure glaucoma (PACG) to control groups of eyes with high-tension glaucoma (HTG) and normal-tension glaucoma (NTG). Methods: Forty-eight eyes with PACG were enrolled, and control eyes with HTG and NTG matched for age, sex, and mean deviation of VF defect were selected. VF tests were performed using the 24-2 program of the Humphrey field analyzer. VF defects were classified into six patterns with the Ocular Hypertension Treatment Study classification system and were categorized into three stages (early, moderate, and advanced). Each hemifield was divided into five regions according to the Glaucoma Hemifield Test (GHT). The mean total deviation (TD) of each GHT region was calculated. Results: Compared with HTG and NTG groups, the partial arcuate VF defects were more common in the PACG group. In the PACG group, the nasal GHT region in the inferior hemifield had the worst mean TD (−8.48 ± 8.62 dB), followed by the arcuate 1 (−7.81 ± 7.91 dB), arcuate 2 (−7.46 ± 7.43 dB), paracentral (−7.19 ± 7.98 dB), and central (−5.14 ± 6.24 dB) regions; the mean TD of the central region was significantly better than those for all other regions (all p < 0.05). A similar trend was observed in the superior hemifield in the PACG group but not the VF hemifields of the HTG and NTG groups. Conclusion: Patterns of VF defect in PACG patients differ from those with HTG and NTG. This discrepancy might be due to the differences in the pathogenic mechanisms of glaucomatous optic neuropathy.

Primary Open-Angle Glaucoma Diagnosis from Optic Disc Photographs Using a Siamese Network.

Primary open-angle glaucoma (POAG) is one of the leading causes of irreversible blindness in the United States and worldwide. Although deep learning methods have been proposed to diagnose POAG, these methods all used a single image as input. Contrastingly, glaucoma specialists typically compare the follow-up image with the baseline image to diagnose incident glaucoma. To simulate this process, we proposed a Siamese neural network, POAGNet, to detect POAG from optic disc photographs. The POAGNet, an algorithm for glaucoma diagnosis, is developed using optic disc photographs. The POAGNet was trained and evaluated on 2 data sets: (1) 37 339 optic disc photographs from 1636 Ocular Hypertension Treatment Study (OHTS) participants and (2) 3684 optic disc photographs from the Sequential fundus Images for Glaucoma (SIG) data set. Gold standard labels were obtained using reading center grades. We proposed a Siamese network model, POAGNet, to simulate the clinical process of identifying POAG from optic disc photographs. The POAGNet consists of 2 side outputs for deep supervision and uses convolution to measure the similarity between 2 networks. The main outcome measures are the area under the receiver operating characteristiccurve, accuracy, sensitivity, and specificity. In POAG diagnosis, extensive experiments show that POAGNet performed better than the best state-of-the-art model on the OHTS test set (area under the curve [AUC] 0.9587 versus 0.8750). It also outperformed the baseline models on the SIG test set (AUC 0.7518 versus 0.6434). To assess the transferability of POAGNet, we also validated the impact of cross-data set variability on our model. The model trained on OHTS achieved an AUC of 0.7490 on SIG, comparable to the previous model trained on the same data set. When using the combination of SIG and OHTS for training, our model achieved superior AUC to the single-data model (AUC 0.8165 versus 0.7518). These demonstrate the relative generalizability of POAGNet. By simulating the clinical grading process, POAGNet demonstrated high accuracy in POAG diagnosis. These results highlight the potential of deep learning to assist and enhance clinical POAG diagnosis. The POAGNet is publicly available on https://github.com/bionlplab/poagnet.

Follow-up studies of the classical landmark studies in Glaucoma.

It was not until the nineteen nineties that there was scientific evidence for the primary treatment concept of glaucoma, lowering intraocular pressure. The treatment concept of lowering intraocular pressure is now proven by randomized controlled clinical trials (landmark studies). This review provides an overview of the follow-up studies to these landmark studies from the last 18 months. The 20-year data of the ocular hypertension treatment study showed a 49% incidence of primary open-angle glaucoma. The data of this landmark study was used for developing different machine learning algorithms. Bilateral disease, disc hemorrhages, and higher intraocular pressure (IOP) were risk factors for visual field deterioration in the United Kingdom Glaucoma Treatment Study. The Advanced Glaucoma Intervention Trial and the Collaborative Initial Glaucoma Treatment Study identified demographic factors, comorbidity, and clinical variables associated with visual field damage. Analysis was performed on Chinese subsets from the Laser in Glaucoma and Ocular Hypertension Study (LiGHT). Looking at all the follow-up studies to the tube shunt landmark studies, preoperative IOP, neovascular glaucoma, Ahmed implantation, and younger age were predictors of failure. A follow-up study to the Effectiveness in Angle-Closure Glaucoma in Lens Extraction Study showed that patients were ten times more likely to maintain a drop-free target IOP after lens extraction than after laser iridotomy. A superior location for iridotomy results in significantly more significant angle widening than temporal or nasal locations, as shown by a follow-up study to the Zhongshan Angle Closure Prevention Trial using OCT and gonioscopy. The number of published follow-up studies to the landmark studies in glaucoma show the ongoing development of clinical questions in management of glaucoma.

Potential for Collider Bias in Studies Examining the Association of Central Corneal Thickness With Glaucoma.

Central corneal thickness (CCT) may be biologically related to glaucoma or observed as associated with glaucoma simply due to its effect on intraocular pressure (IOP) measurement. We aimed to determine if the previously reported CCT-glaucoma associations, in which the analyses were adjusted for IOP or participants were selected on IOP, could be explained by collider bias. We simulated datasets mimicking a longitudinal population-based study (Los Angeles Latino Eye Study) and a trial (Ocular Hypertension Treatment Study) such that: (i) CCT was not truly associated with glaucoma, (ii) CCT and true IOP both contribute to measured IOP, and (iii) true IOP contributes to glaucoma risk. We then tested whether an association between CCT and glaucoma could be spuriously induced simply by adjusting for or selecting on measured IOP. A thinner CCT was significantly associated with higher glaucoma incidence in the simulated longitudinal population-based study when adjusted for measured IOP, but not crudely (unadjusted). A thinner CCT was crudely associated with glaucoma incidence in the simulated trial in which the participants were selected for high measured IOP. Effect sizes in the simulations were similar to those observed in the original studies. Our findings question whether CCT is biologically associated with glaucoma and suggest that current evidence may be due to collider bias. This indicates that CCT alone cannot be used as a factor to identify people at high risk of glaucoma in the general population. Using CCT in combination with IOP may be superior to using IOP alone.