Faculty Bibliography

OBJECTIVE:To examine the longitudinal postoperative outcomes of open versus closed conjunctiva implantation of the XEN45 gel stent. DESIGN/METHODS:Retrospective multicenter study. SUBJECTS/METHODS:One hundred ninety-three patients with glaucoma underwent XEN45 implantation via an open or closed conjunctiva approach. METHODS:Data on patient demographics; diagnoses; preoperative and postoperative clinical data; outcome measures, including intraocular pressure (IOP); use of glaucoma medications; visual acuity; and complications were collected. Statistical analyses were performed with P < 0.05 as significant. MAIN OUTCOME MEASURES/METHODS:Failure was defined as < 20% reduction in IOP from the medicated baseline or a IOP of > 21 mmHg at 2 consecutive visits at postoperative month 1 and beyond, the need for subsequent operative intervention or additional glaucoma surgery, or a catastrophic event, such as loss of light perception. Eyes that had not failed by these criteria and were not on glaucoma medications were considered complete successes. Overall success was defined as those who achieved success either with or without topical medications. RESULTS:Patients were followed for an average of 17 months. Complete success was achieved in 42.5% and 24.7% of the open and closed groups, respectively (P = 0.01). Overall success was achieved in 64.2% and 37.0% of the open and closed groups, respectively (P < 0.001) at the last follow-up. Bleb needling was performed in 12.4% of eyes in the open group compared with 40% of eyes in the closed group. An IOP spike of ≥ 10 mmHg was twice as likely to occur in the closed group compared with the open group during the postoperative period (40% vs. 18%; P = 0.001). CONCLUSIONS:Implantation of XEN45 with opening of the conjunctiva resulted in a lower IOP with greater success and lower needling rate compared with those achieved with the closed conjunctiva technique. Similar rates of postoperative complications and vision loss were noted in each group. Although both procedures provide substantial IOP reduction, the open technique appears to result in higher success rates and fewer postoperative interventions. FINANCIAL DISCLOSURE(S)/BACKGROUND:Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

PURPOSE/UNASSIGNED:To establish generalizable pointwise spatial relationship between structure and function through occlusion analysis of a deep-learning (DL) model for predicting the visual field (VF) sensitivities from 3-dimensional (3D) OCT scan. DESIGN/UNASSIGNED:Retrospective cross-sectional study. PARTICIPANTS/UNASSIGNED:A total of 2151 eyes from 1129 patients. METHODS/UNASSIGNED:A DL model was trained to predict 52 VF sensitivities of 24-2 standard automated perimetry from 3D spectral-domain OCT images of the optic nerve head (ONH) with 12 915 OCT-VF pairs. Using occlusion analysis, the contribution of each individual cube covering a 240 × 240 × 31.25 μm region of the ONH to the model's prediction was systematically evaluated for each OCT-VF pair in a separate test set that consisted of 996 OCT-VF pairs. After simple translation (shifting in x- and y-axes to match the ONH center), group t-statistic maps were derived to visualize statistically significant ONH regions for each VF test point within a group. This analysis allowed for understanding the importance of each super voxel (240 × 240 × 31.25 μm covering the entire 4.32 × 4.32 × 1.125 mm ONH cube) in predicting VF test points for specific patient groups. MAIN OUTCOME MEASURES/UNASSIGNED:The region at the ONH corresponding to each VF test point and the effect of the former on the latter. RESULTS/UNASSIGNED:The test set was divided to 2 groups, the healthy-to-early-glaucoma group (792 OCT-VF pairs, VF mean deviation [MD]: -1.32 ± 1.90 decibels [dB]) and the moderate-to-advanced-glaucoma group (204 OCT-VF pairs, VF MD: -17.93 ± 7.68 dB). Two-dimensional group t-statistic maps (x, y projection) were generated for both groups, assigning related ONH regions to visual field test points. The identified influential structural locations for VF sensitivity prediction at each test point aligned well with existing knowledge and understanding of structure-function spatial relationships. CONCLUSIONS/UNASSIGNED:This study successfully visualized the global trend of point-by-point spatial relationships between OCT-based structure and VF-based function without the need for prior knowledge or segmentation of OCTs. The revealed spatial correlations were consistent with previously published mappings. This presents possibilities of learning from trained machine learning models without applying any prior knowledge, potentially robust, and free from bias. FINANCIAL DISCLOSURES/UNASSIGNED:Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

OBJECTIVE:To assess the feasibility of remotely training glaucoma patients to take a 10-session clustered virtual reality (VR) visual field (VF) test (Vivid Vision Perimetry [VVP-10]) at home, analyze results for test-retest variability, and assess correspondence with conventional perimetry. DESIGN/METHODS:Cross-sectional study. SUBJECTS/METHODS:Twenty-one subjects with glaucoma were enrolled and included in the feasibility assessment of remote training. Thirty-six eyes were used for test-retest analysis and determination of concordance with the Humphrey Field Analyzer (HFA). METHODS:Subjects were provided with a mobile VR headset containing the VVP-10 test software and trained remotely via video conferencing. Subjects were instructed to complete 10 sessions over a 14-day period. MAIN OUTCOME MEASURES/METHODS:Feasibility was determined by the number of subjects who were able to independently complete VVP-10 over the 14-day period after 1 remote training session. The intraclass correlation coefficient (ICC) for average fraction seen across 10 sessions and the standard error (SE) of the mean were primary outcome measures for assessing test-retest variability. Correlation with HFA mean sensitivity (MS) across eyes, was a secondary outcome measure. RESULTS:Twenty subjects (95%) successfully completed the VVP-10 test series after 1 training session. The ICC for VVP-10 was 0.95 (95% confidence interval [CI], 0.92-0.97). The mean SE in units of fraction seen was 0.012. The Spearman correlations between VVP-10 average fraction seen and HFA MS were 0.87 (95% CI, 0.66-0.98) for moderate-to-advanced glaucoma eyes, and decreased to 0.67 (95% CI, 0.28-0.94) when all eyes were included. CONCLUSIONS:Remote training of patients at home is feasible, and subsequent remote clustered VF testing using VVP-10 by patients on their own, without any further interactions with caregivers or study staff, was possible. At-home VVP-10 results demonstrated low test-retest variability. Future studies must be conducted to determine if VVP-10, taken at home as convenient for the patient, may be a viable supplement to provide equivalent or complementary results to that of standard in-clinic assessment of visual function in glaucoma. FINANCIAL DISCLOSURE(S)/BACKGROUND:Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

PURPOSE/UNASSIGNED:Race disparities in the healthcare system and the resulting inequality in clinical data among different races hinder the ability to generate equitable prediction results. This study aims to reduce healthcare disparities arising from data imbalance by leveraging advanced transfer learning (TL) methods. METHOD/UNASSIGNED:We examined the ophthalmic healthcare disparities at a population level using electronic medical records data from a study cohort (N = 785) receiving care at an academic institute. Regression-based TL models were usesd, transferring valuable information from the dominant racial group (White) to improve visual field mean deviation (MD) rate of change prediction particularly for data-disadvantaged African American (AA) and Asian racial groups. Prediction results of TL models were compared with two conventional approaches. RESULTS/UNASSIGNED:Disparities in socioeconomic status and baseline disease severity were observed among the AA and Asian racial groups. The TL approach achieved marked to comparable improvement in prediction accuracy compared to the two conventional approaches as evident by smaller mean absolute errors or mean square errors. TL identified distinct key features of visual field MD rate of change for each racial group. CONCLUSIONS/UNASSIGNED:The study introduces a novel application of TL that improved reliability of the analysis in comparison with conventional methods, especially in small sample size groups. This can improve assessment of healthcare disparity and subsequent remedy approach. TRANSLATIONAL RELEVANCE/UNASSIGNED:TL offers an equitable and efficient approach to mitigate healthcare disparities analysis by enhancing prediction performance for data-disadvantaged group.

Glaucoma causes progressive visual field deterioration and is the leading cause of blindness worldwide. Glaucomatous damage is irreversible and greatly impacts quality of life. Therefore, it is critically important to detect glaucoma early and closely monitor progression to preserve functional vision. Glaucoma is routinely monitored in the clinical setting using optical coherence tomography (OCT) for derived measures such as the thickness of important visual structures. There is not a consensus of what measures represent the most relevant biomarkers of glaucoma progression. Further, despite the increasing availability of longitudinal OCT data, a quantitative model of 3D structural change over time associated with glaucoma does not exist. In this paper we present an algorithm that will perform hierarchical geodesic modeling at the imaging level, considering 3D OCT images as observations of structural change over time. Hierarchical modeling includes subject-wise trajectories as geodesics in the space of diffeomorphisms and population level (glaucoma vs control) trajectories are also geodesics which explain subject-wise trajectories as deviations from the mean. Our preliminary experiments demonstrate a greater magnitude of structural change associated with glaucoma compared to normal aging. Our algorithm has the potential application in patient-specific monitoring and analysis of glaucoma progression as well as a statistical model of population trends and population variability.