Transformer-Based Deep Learning Prediction of 10-Degree Humphrey Visual Field Tests From 24-Degree Data.

Purpose: To predict 10-2 Humphrey visual fields (VFs) from 24-2 VFs and associated non-total deviation features using deep learning. Methods: We included 5189 reliable 24-2 and 10-2 VF pairs from 2236 patients, and 28,409 reliable pairs of macular OCT scans and 24-2 VF from 19,527 eyes of 11,560 patients. We developed a transformer-based deep learning model using 52 total deviation values and nine VF test features to predict 68 10-2 total deviation values. The mean absolute error, root mean square error, and the R2 were evaluation metrics. We further evaluated whether the predicted 10-2 VFs can improve the structure-function relationship between macular thinning and paracentral VF loss in glaucoma. Results: The average mean absolute error and R2 for 68 10-2 VF test points were 3.30 ± 0.52 dB and 0.70 ± 0.11, respectively. The accuracy was lower in the inferior temporal region. The model placed greater emphasis on 24-2 VF points near the central fixation point when predicting the 10-2 VFs. The inclusion of nine VF test features improved the mean absolute error and R2 up to 0.17 ± 0.06 dB and 0.01 ± 0.01, respectively. Age was the most important 24-2 VF test parameter for 10-2 VF prediction. The predicted 10-2 VFs achieved an improved structure-function relationship between macular thinning and paracentral VF loss, with the R2 at the central 4, 12, and 16 locations of 24-2 VFs increased by 0.04, 0.05 and 0.05, respectively (P < 0.001). Conclusions: The 10-2 VFs may be predicted from 24-2 data. Translational Relevance: The predicted 10-2 VF has the potential to improve glaucoma diagnosis.

The Effect of Changing Fellowship Interview Format on Candidate Ranking Variabilities: The COVID-19 Experience.

Purpose: This study investigated the effect of interview format changes (in-person to virtual, one-to-one to multiple-to-one) necessitated by the COVID-19 travel restrictions on preliminary fellowship candidate ranking variabilities. Design: Cross-sectional observational study. Method: In 2018 and 2019, the glaucoma fellowship interviews were conducted in-person in a one-to-one format, whereas in 2020, interviews were virtual and in a multiple (interviewers)-to-one (candidate) format. We compared ranking ranges of interviewers within the same virtual room (WSR) and not within the same virtual room (NWSR) to assess the effect of WSR versus NWSR on ranking variabilities. We also compared ranking categories ("accept," "alternate," and "pass") agreements between 2018, 2019, and 2020 to assess the effect of virtual versus in-person interviews on ranking variabilities. Results: NWSR and WSR mean rankings differed by 1.33 (95% confidence interval difference 0.61 to 2.04, p = 0.0003), with WSR interviewers having less variability than NWSR pairs. The variability between 2018/2019 (in-person interviews) and 2020 (virtual interviews) showed no differences between in-person and virtual interviews (weighted Kappa statistic 0.086 for 2018, 0.158 for 2019, and 0.101 for 2020; p < 0.05 for all years). The overall least attractive candidate has the lowest variability; the most attractive candidate has the second lowest variability. Conclusion: Grouping interviewers WSR during the interview decreased ranking variabilities compared to NWSR, while a change from in-person to virtual interview format did not increase the ranking variabilities. This suggests that the decreased nonverbal interactions in virtual interviews do not decrease interviewers' perceptions as applied to preliminary rankings.

The influence of etiology on surgical outcomes in neovascular glaucoma.

BACKGROUND: The authors sought to evaluate visual outcomes in patients with varying etiologies of neovascular glaucoma (NVG), who were treated with glaucoma drainage devices (GDD). METHODS: This was a retrospective case series of patients at a large academic teaching institution who had surgical intervention for neovascular glaucoma between September 2011 and May 2019. Eyes were included if there was documented neovascularization of the iris/angle with an intraocular pressure (IOP) > 21 mmHg at presentation. Eyes must also have been treated with surgical intervention that included a GDD. Primary outcome measure was visual acuity at the 1-year post-operative visit. Secondary outcome measure was qualified success after surgery defined by: pressure criteria (5 mmHg < IOP ≤ 21 mmHg), no re-operation for elevated IOP, and no loss of LP vision. RESULTS: One hundred twenty eyes met inclusion criteria. 61.7% had an etiology of proliferative diabetic retinopathy (PDR), 23.3% had retinal vein occlusions (RVO), and the remaining 15.0% suffered from other etiologies. Of patients treated with GDD, eyes with PDR had better vision compared to eyes with RVO at final evaluation (p = 0.041). There was a statistically significant difference (p = 0.027) in the mean number of glaucoma medications with Ahmed eyes (n = 70) requiring 1.9 medications and Baerveldt eyes (n = 46) requiring 1.3 medications at final evaluation. CONCLUSIONS: In our study, many patients with NVG achieved meaningful vision, as defined by World Health Organization (WHO) guidelines, and IOP control after GDD. Outcomes differed between patients with PDR and RVO in favor of the PDR group. Different GDD devices had similar performance profiles for VA and IOP outcomes. Direct prospective comparison of Baerveldt, Ahmed, and cyclophotocoagulation represents the next phase of discovery.

The Effect of Ametropia on Glaucomatous Visual Field Loss.

Myopia has been discussed as a risk factor for glaucoma. In this study, we characterized the relationship between ametropia and patterns of visual field (VF) loss in glaucoma. Reliable automated VFs (SITA Standard 24-2) of 120,019 eyes from 70,495 patients were selected from five academic institutions. The pattern deviation (PD) at each VF location was modeled by linear regression with ametropia (defined as spherical equivalent (SE) starting from extreme high myopia), mean deviation (MD), and their interaction (SE × MD) as regressors. Myopia was associated with decreased PD at the paracentral and temporal VF locations, whereas hyperopia was associated with decreased PD at the Bjerrum and nasal step locations. The severity of VF loss modulated the effect of ametropia: with decreasing MD and SE, paracentral/nasal step regions became more depressed and Bjerrum/temporal regions less depressed. Increasing degree of myopia was positively correlated with VF depression at four central points, and the correlation became stronger with increasing VF loss severity. With worsening VF loss, myopes have increased VF depressions at the paracentral and nasal step regions, while hyperopes have increased depressions at the Bjerrum and temporal locations. Clinicians should be aware of these effects of ametropia when interpreting VF loss.

Development and Comparison of Machine Learning Algorithms to Determine Visual Field Progression.

Purpose: To develop and test machine learning classifiers (MLCs) for determining visual field progression. Methods: In total, 90,713 visual fields from 13,156 eyes were included. Six different progression algorithms (linear regression of mean deviation, linear regression of the visual field index, Advanced Glaucoma Intervention Study algorithm, Collaborative Initial Glaucoma Treatment Study algorithm, pointwise linear regression [PLR], and permutation of PLR) were applied to classify each eye as progressing or stable. Six MLCs were applied (logistic regression, random forest, extreme gradient boosting, support vector classifier, convolutional neural network, fully connected neural network) using a training and testing set. For MLC input, visual fields for a given eye were divided into the first and second half and each location averaged over time within each half. Each algorithm was tested for accuracy, sensitivity, positive predictive value, and class bias with a subset of visual fields labeled by a panel of three experts from 161 eyes. Results: MLCs had similar performance metrics as some of the conventional algorithms and ranged from 87% to 91% accurate with sensitivity ranging from 0.83 to 0.88 and specificity from 0.92 to 0.96. All conventional algorithms showed significant class bias, meaning each individual algorithm was more likely to grade uncertain cases as either progressing or stable (P ≤ 0.01). Conversely, all MLCs were balanced, meaning they were equally likely to grade uncertain cases as either progressing or stable (P ≥ 0.08). Conclusions: MLCs showed a moderate to high level of accuracy, sensitivity, and specificity and were more balanced than conventional algorithms. Translational Relevance: MLCs may help to determine visual field progression.

Virtual Versus In-Person Surgical Fellowship Interviews and Ranking Variability: the COVID-19 Experience.

Objective: To investigate the effect of interview format changes (in-person to virtual, one-to-one to multiple-to-one) necessitated by the COVID-19 travel restrictions on candidate ranking variabilities. Method: In 2018/2019, the glaucoma fellowship interviews were conducted in-person and one-to-one, whereas in 2020, interviews were virtual and multiple (interviewers)-to-one (candidate). We compared ranking ranges of interviewers within the same virtual room (WSR) and not within the same virtual room (NWSR) to assess the effect of this change on ranking variabilities. We also compared ranking categories ("accept," "alternate," and "pass") agreements between in-person and virtual interviews to assess the effect of this change on ranking variabilities. Results: NWSR and WSR mean rankings differed by 1.33 (95% confidence interval difference 0.61 to 2.04, p = 0.0003), with WSR interviewers having less variability than NWSR pairs. The variability of in-person interviews and later virtual interviews showed no differences (weighted Kappa statistic 0.086 for 2018, 0.158 for 2019, and 0.101 for 2020; p < 0.05 for all years). The overall least attractive candidate has the lowest variability; the most attractive candidate has the second lowest variability. Conclusion: Grouping interviewers decreased ranking variabilities, while a change from in-person to virtual interview format did not increase the ranking variabilities.

Variability and Power to Detect Progression of Different Visual Field Patterns.

PURPOSE: To compare the variability and ability to detect visual field (VF) progression of 24-2, central 12 locations of the 24-2 and 10-2 VF tests in eyes with abnormal VFs. DESIGN: Retrospective, multisite cohort. PARTICIPANTS: A total of 52 806 24-2 and 11 966 10-2 VF tests from 7307 eyes from the Glaucoma Research Network database were analyzed. Only eyes with ≥ 5 visits and ≥ 2 years of follow-up were included. METHODS: Linear regression models were used to calculate the rates of mean deviation (MD) change (slopes), whereas their residuals were used to assess variability across the entire MD range. Computer simulations (n = 10 000) based on real MD residuals of our sample were performed to estimate power to detect significant progression (P < 5%) at various rates of MD change. MAIN OUTCOME MEASURES: Time required to detect progression. RESULTS: For all 3 patterns, the MD variability was highest within the -5 to -20 decibel (dB) range and consistently lower with the 10-2 compared with 24-2 or central 24-2. Overall, time to detect confirmed significant progression at 80% power was the lowest with 10-2 VF, with a decrease of 14.6% to 18.5% when compared with 24-2 and a decrease of 22.9% to 26.5% when compared with central 24-2. CONCLUSIONS: Time to detect central VF progression was reduced with 10-2 MD compared with 24-2 and C24-2 MD in glaucoma eyes in this large dataset, in part because 10-2 tests had lower variability. These findings contribute to current evidence of the potential value of 10-2 testing in the clinical management of patients with glaucoma and in clinical trial design.

Predicting Global Test-Retest Variability of Visual Fields in Glaucoma.

PURPOSE: To model the global test-retest variability of visual fields (VFs) in glaucoma. DESIGN: Retrospective cohort study. PARTICIPANTS: Test-retest VFs from 4044 eyes of 4044 participants. METHODS: We selected 2 reliable VFs per eye measured with the Humphrey Field Analyzer (Swedish interactive threshold algorithm 24-2) within 30 days of each other. Each VF had fixation losses (FLs) of 33% or less, false-negative results (FNRs) of 20% or less, and false-positive results (FPRs) of 20% or less. Stepwise linear regression was applied to select the model best predicting the global test-retest variability from 3 categories of features of the first VF: (1) base parameters (age, mean deviation, pattern standard deviation, glaucoma hemifield test results, FPR, FNR, and FL); (2) total deviation (TD) at each location; and (3) computationally derived archetype VF loss patterns. The global test-retest variability was defined as root mean square deviation (RMSD) of TD values at all 52 VF locations. MAIN OUTCOME MEASURES: Archetype models to predict the global test-retest variability. RESULTS: The mean ± standard deviation of the root mean square deviation was 4.39 ± 2.55 dB. Between the 2 VF tests, TD values were correlated more strongly in central than in peripheral VF locations (intraclass coefficient, 0.66-0.89; P < 0.001). Compared with the model using base parameters alone (adjusted R2 = 0.45), adding TD values improved prediction accuracy of the global variability (adjusted R2 = 0.53; P < 0.001; Bayesian information criterion [BIC] decrease of 527; change of >6 represents strong improvement). Lower TD sensitivity in the outermost peripheral VF locations was predictive of higher global variability. Adding archetypes to the base model improved model performance with an adjusted R2 of 0.53 (P < 0.001) and lowering of BIC by 583. Greater variability was associated with concentric peripheral defect, temporal hemianopia, inferotemporal defect, near total loss, superior peripheral defect, and central scotoma (listed in order of decreasing statistical significance), and less normal VF results and superior paracentral defect. CONCLUSIONS: Inclusion of archetype VF loss patterns and TD values based on first VF improved the prediction of the global test-retest variability than using traditional global VF indices alone.

Inter-Eye Association of Visual Field Defects in Glaucoma and Its Clinical Utility.

Purpose: To investigate intereye associations of visual field (VF) defects. Methods: We selected 24-2 VF pairs of both eyes from 63,604 patients tested on the same date with mean deviation (MD) ≥ -12 dB. VFs were decomposed into one normal and 15 defect patterns previously identified using archetypal analysis. VF pattern weighting coefficients were correlated between the worse and better eyes, as defined by MD. VF defect patterns (weighting coefficients > 10%) in the better eye were predicted from weighting coefficients of the worse eye by logistic regression models, which were evaluated by area under the receiver operating characteristic curve (AUC). Results: Intereye correlations of archetypal VF patterns were strongest for the same defect pattern between fellow eyes. The AUCs for predicting the presence of 15 defect patterns in the better eye based on the worse eye ranged from 0.69 (superior nasal step) to 0.92 (near total loss). The AUC for predicting superior paracentral loss was 0.89. Superior paracentral loss in the better eye was positively correlated with coefficients of superior paracentral loss, central scotoma, superior altitudinal defect, nasal hemianopia, and inferior paracentral loss in the worse eye, and negatively correlated with coefficients of the normal VF, superior peripheral defect, concentric peripheral defect, and temporal wedge. The parameters are presented in the descending order of statistical significance. Conclusions: VF patterns of the worse eye are predictive of VF defects in the better eye. Translational Relevance: Our models can potentially assist clinicians to better interpret VF loss under measurement uncertainty.

Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard.

PURPOSE: To develop an artificial intelligence (AI) dashboard for monitoring glaucomatous functional loss. DESIGN: Retrospective, cross-sectional, longitudinal cohort study. PARTICIPANTS: Of 31 591 visual fields (VFs) on 8077 subjects, 13 231 VFs from the most recent visit of each patient were included to develop the AI dashboard. Longitudinal VFs from 287 eyes with glaucoma were used to validate the models. METHOD: We entered VF data from the most recent visit of glaucomatous and nonglaucomatous patients into a "pipeline" that included principal component analysis (PCA), manifold learning, and unsupervised clustering to identify eyes with similar global, hemifield, and local patterns of VF loss. We visualized the results on a map, which we refer to as an "AI-enabled glaucoma dashboard." We used density-based clustering and the VF decomposition method called "archetypal analysis" to annotate the dashboard. Finally, we used 2 separate benchmark datasets-one representing "likely nonprogression" and the other representing "likely progression"-to validate the dashboard and assess its ability to portray functional change over time in glaucoma. MAIN OUTCOME MEASURES: The severity and extent of functional loss and characteristic patterns of VF loss in patients with glaucoma. RESULTS: After building the dashboard, we identified 32 nonoverlapping clusters. Each cluster on the dashboard corresponded to a particular global functional severity, an extent of VF loss into different hemifields, and characteristic local patterns of VF loss. By using 2 independent benchmark datasets and a definition of stability as trajectories not passing through over 2 clusters in a left or downward direction, the specificity for detecting "likely nonprogression" was 94% and the sensitivity for detecting "likely progression" was 77%. CONCLUSIONS: The AI-enabled glaucoma dashboard, developed using a large VF dataset containing a broad spectrum of visual deficit types, has the potential to provide clinicians with a user-friendly tool for determination of the severity of glaucomatous vision deficit, the spatial extent of the damage, and a means for monitoring the disease progression.

Artificial Intelligence Classification of Central Visual Field Patterns in Glaucoma.

PURPOSE: To quantify the central visual field (VF) loss patterns in glaucoma using artificial intelligence. DESIGN: Retrospective study. PARTICIPANTS: VFs of 8712 patients with 13 951 Humphrey 10-2 test results from 13 951 eyes for cross-sectional analyses, and 824 patients with at least 5 reliable 10-2 test results at 6-month intervals or more from 1191 eyes for longitudinal analyses. METHODS: Total deviation values were used to determine the central VF patterns using the most recent 10-2 test results. A 24-2 VF within a 3-month window of the 10-2 tests was used to stage eyes into mild, moderate, or severe functional loss using the Hodapp-Anderson-Parrish scale at baseline. Archetypal analysis was applied to determine the central VF patterns. Cross-validation was performed to determine the optimal number of patterns. Stepwise regression was applied to select the optimal feature combination of global indices, average baseline decomposition coefficients from central VFs archetypes, and other factors to predict central VF mean deviation (MD) slope based on the Bayesian information criterion (BIC). MAIN OUTCOME MEASURES: The central VF patterns stratified by severity stage based on 24-2 test results and a model to predict the central VF MD change over time using baseline test results. RESULTS: From cross-sectional analysis, 17 distinct central VF patterns were determined for the 13 951 eyes across the spectrum of disease severity. These central VF patterns could be divided into isolated superior loss, isolated inferior loss, diffuse loss, and other loss patterns. Notably, 4 of the 5 patterns of diffuse VF loss preserved the less vulnerable inferotemporal zone, whereas they lost most of the remaining more vulnerable zone described by the Hood model. Inclusion of coefficients from central VF archetypical patterns strongly improved the prediction of central VF MD slope (BIC decrease, 35; BIC decrease of >6 indicating strong prediction improvement) than using only the global indices of 2 baseline VF results. Eyes with baseline VF results with more superonasal and inferonasal loss were more likely to show worsening MD over time. CONCLUSIONS: We quantified central VF patterns in glaucoma, which were used to improve the prediction of central VF worsening compared with using only global indices.

Characterization of Central Visual Field Loss in End-stage Glaucoma by Unsupervised Artificial Intelligence.

Importance: Although the central visual field (VF) in end-stage glaucoma may substantially vary among patients, structure-function studies and quality-of-life assessments are impeded by the lack of appropriate characterization of end-stage VF loss. Objective: To provide a quantitative characterization and classification of central VF loss in end-stage glaucoma. Design, Setting, and Participants: This retrospective cohort study collected data from 5 US glaucoma services from June 1, 1999, through October 1, 2014. A total of 2912 reliable 10-2 VFs of 1103 eyes from 1010 patients measured after end-stage 24-2 VFs with a mean deviation (MD) of -22 dB or less were included in the analysis. Data were analyzed from March 28, 2018, through May 23, 2019. Main Outcomes and Measures: Central VF patterns were determined by an artificial intelligence algorithm termed archetypal analysis. Longitudinal analyses were performed to investigate whether the development of central VF defect mostly affects specific vulnerability zones. Results: Among the 1103 patients with the most recent VFs, mean (SD) age was 70.4 (14.3) years; mean (SD) 10-2 MD, -21.5 (5.6) dB. Fourteen central VF patterns were determined, including the most common temporal sparing patterns (304 [27.5%]), followed by mostly nasal loss (280 [25.4%]), hemifield loss (169 [15.3%]), central island (120 [10.9%]), total loss (91 [8.3%]), nearly intact field (56 [5.1%]), inferonasal quadrant sparing (42 [3.8%]), and nearly total loss (41 [3.7%]). Location-specific median total deviation analyses partitioned the central VF into a more vulnerable superonasal zone and a less vulnerable inferotemporal zone. At 1-year and 2-year follow-up, new defects mostly occurred in the more vulnerable zone. Initial encroachments on an intact central VF at follow-up were more likely to be from nasal loss (11 [18.4%]; P < .001). One of the nasal loss patterns had a substantial chance at 2-year follow-up (8 [11.0%]; P = .004) to shift to total loss, whereas others did not. Conclusions and Relevance: In this study, central VF loss in end-stage glaucoma was found to exhibit characteristic patterns that might be associated with different subtypes. Initial central VF loss is likely to be nasal loss, and 1 specific type of nasal loss is likely to develop into total loss.

Baseline Age and Mean Deviation Affect the Rate of Glaucomatous Vision Loss.

PURPOSE: The purpose of this study was to assess the relationship between the rate of the glaucomatous visual field (VF) worsening and baseline age and baseline VF mean deviation (MD). DESIGN: This study was a retrospective, multisite cohort. PARTICIPANTS: A total of 84,711 reliable Swedish Interactive Thresholding Algorithm 24-2 VF tests from 8167 eyes from 5644 patients with ≥6 VF tests, ≥5 years of follow-up, baseline age 18 years or above and baseline MD ≥-10 dB, and at least 2 abnormal VF tests were included from the Glaucoma Research Network Database. METHODS: The global mean deviation rates (MDRs) and pointwise total deviation rates (TDRs) of VF progression (dB/y) were calculated for each eye using linear regression. The relationships between MDR and baseline age and MD were determined using linear mixed-effects models and logistic regression, with rapid progression defined as an MDR≤-1.0 dB/y. The relationships between TDR and baseline age and baseline MD were determined using linear mixed-effects models. MAIN OUTCOME MEASURES: Coefficients of the regression models. RESULTS: In individual mixed-effects models both baseline age (ß=-0.0079 dB/y; P<0.001) and baseline MD (ß=0.012/y; P<0.001) were associated with faster progression. All parameters were statistically significant in the full model with both parameters and their interaction (ß=0.00065; P=0.0017) as covariates. With logistic regression, each year increase in baseline age increased the odds of belonging to the rapid-progressing group by a factor of 1.033, and each unit increase in baseline MD (less severe visual loss) decreased the odds by a factor of 0.8821. The mean pointwise TDR ranged from -0.21 to -0.55 dB/y, with the most rapid pointwise progression observed in the nasal and paracentral regions of the field. CONCLUSIONS: Older age and worse MD at baseline are associated with more rapid VF progression in this large dataset. The effect of age on MDR is influenced by baseline MD severity, supporting the importance of early detection and more aggressive therapy in older patients with worse VF damage. The pointwise rate of VF loss varies across the VF, providing a means for physicians to more effectively monitor progression.

Dry Eye Symptoms and Ocular Pain in Veterans with Glaucoma.

Dry eye and glaucoma are two frequently encountered ocular conditions, which can lead to substantial morbidity and decreased quality of life. Patients on topical glaucoma medications are known to be at greater risk for ocular surface symptoms. Veterans seen in the eye clinics at the Miami Veterans Affairs Hospital from January to July 2016 completed surveys assessing dry eye and ocular pain symptoms, including the five item Dry Eye Questionnaire (DEQ5). A total of 62 patients with glaucoma completed the survey. Of those, 52 were on glaucoma medications at the time of the survey, with the majority requiring more than one medication to control intraocular pressure. The frequency of mild or greater dry eye symptoms (defined as DEQ5 >6) tended to increase with increasing medication burden, and patients on brimonidine were more likely to report a DEQ5 >6. Patients on three or more glaucoma medications were more likely to report symptoms of shooting pain, dryness, and itchiness. Patients using timolol were more likely to report throbbing and pain by light, while those on latanoprost reported stinging. Our data support an association between increasing number of glaucoma medications and worsening of dry eye symptoms. Patient and medication-associated symptoms can be used to tailor individual medication regimens.

Visual and anatomic outcomes of cataract surgery with intraoperative or postoperative complications in a teaching institution.

OBJECTIVE: To review visual and anatomic outcomes after cataract surgery with complications in a teaching institution. METHODS: Consecutive case series. A chart review was conducted of patients who underwent phacoemulsification with intraoperative or postoperative complications, performed by ophthalmology residents under direct supervision of experienced ophthalmology attending physicians. Best corrected visual acuity (BCVA), OCT parameters, and postoperative treatments were reviewed at 1, 3, 6, and 12 months postoperatively. RESULTS: One hundred thirty-three eyes were analyzed. Mean BCVA was 50 ± 23 approximate Early Treatment Diabetic Retinopathy Study letters at the preoperative visit and improved by a mean of 8 letters (n = 128; p = 0.001), 16 letters (n = 117; p < 0.001), 14 letters (n = 79; p < 0.001), and 4 letters (n = 34; p = 0.37) at 1, 3, 6, and 12 months. The mean OCT central subfoveal thickness increased by less than 50 µm at all time points and this change was not statistically significant at 12 months. BCVA increased by 3 lines in 41%, 56%, 57%, and 44% of eyes at 1, 3, 6, and 12 months. Median BCVA was 20/40 or better at each follow-up period. Fifty-three (40%) eyes required a secondary surgical procedure due to intraoperative or postoperative complication. A significant proportion of eyes received anti-inflammatory drops through 1 year. CONCLUSIONS: After cataract surgery with intraoperative or postoperative complications, a majority of eyes experienced substantial visual gains and only mild retinal thickening while being managed with long-term anti-inflammatory drops and additional surgical procedures.

Agreement and Predictors of Discordance of 6 Visual Field Progression Algorithms.

PURPOSE: To determine the agreement of 6 established visual field (VF) progression algorithms in a large dataset of VFs from multiple institutions and to determine predictors of discordance among these algorithms. DESIGN: Retrospective longitudinal cohort study. PARTICIPANTS: Visual fields from 5 major eye care institutions in the United States were analyzed, including a subset of eyes with at least 5 Swedish interactive threshold algorithm standard 24-2 VFs that met our reliability criteria. Of a total of 831 240 VFs, a subset of 90 713 VFs from 13 156 eyes of 8499 patients met the inclusion criteria. METHODS: Six commonly used VF progression algorithms (mean deviation [MD] slope, VF index slope, Advanced Glaucoma Intervention Study, Collaborative Initial Glaucoma Treatment Study, pointwise linear regression, and permutation of pointwise linear regression) were applied to this cohort, and each eye was determined to be stable or progressing using each measure. Agreement between individual algorithms was tested using Cohen's κ coefficient. Bivariate and multivariate analyses were used to determine predictors of discordance (3 algorithms progressing and 3 algorithms stable). MAIN OUTCOME MEASURES: Agreement and discordance between algorithms. RESULTS: Individual algorithms showed poor to moderate agreement with each other when compared directly (κ range, 0.12-0.52). Based on at least 4 algorithms, 11.7% of eyes progressed. Major predictors of discordance or lack of agreement among algorithms were more depressed initial MD (P < 0.01) and older age at first available VF (P < 0.01). A greater number of VFs (P < 0.01), more years of follow-up (P < 0.01), and eye care institution (P = 0.03) also were associated with discordance. CONCLUSIONS: This extremely large comparative series demonstrated that existing algorithms have limited agreement and that agreement varies with clinical parameters, including institution. These issues underscore the challenges to the clinical use and application of progression algorithms and of applying big-data results to individual practices.

An Artificial Intelligence Approach to Detect Visual Field Progression in Glaucoma Based on Spatial Pattern Analysis.

Purpose: To detect visual field (VF) progression by analyzing spatial pattern changes. Methods: We selected 12,217 eyes from 7360 patients with at least five reliable 24-2 VFs and 5 years of follow-up with an interval of at least 6 months. VFs were decomposed into 16 archetype patterns previously derived by artificial intelligence techniques. Linear regressions were applied to the 16 archetype weights of VF series over time. We defined progression as the decrease rate of the normal archetype or any increase rate of the 15 VF defect archetypes to be outside normal limits. The archetype method was compared with mean deviation (MD) slope, Advanced Glaucoma Intervention Study (AGIS) scoring, Collaborative Initial Glaucoma Treatment Study (CIGTS) scoring, and the permutation of pointwise linear regression (PoPLR), and was validated by a subset of VFs assessed by three glaucoma specialists. Results: In the method development cohort of 11,817 eyes, the archetype method agreed more with MD slope (kappa: 0.37) and PoPLR (0.33) than AGIS (0.12) and CIGTS (0.22). The most frequently progressed patterns included decreased normal pattern (63.7%), and increased nasal steps (16.4%), altitudinal loss (15.9%), superior-peripheral defect (12.1%), paracentral/central defects (10.5%), and near total loss (10.4%). In the clinical validation cohort of 397 eyes with 27.5% of confirmed progression, the agreement (kappa) and accuracy (mean of hit rate and correct rejection rate) of the archetype method (0.51 and 0.77) significantly (P < 0.001 for all) outperformed AGIS (0.06 and 0.52), CIGTS (0.24 and 0.59), MD slope (0.21 and 0.59), and PoPLR (0.26 and 0.60). Conclusions: The archetype method can inform clinicians of VF progression patterns.

The Effect of Patient Characteristics and Sleep Quality on Visual Field Performance Reliability.

PURPOSE: To investigate the association of automated visual field (VF) reliability indices (false positive [FP], false negative [FN], and fixation loss [FL]) and sleep quality, VF experience, and age. METHODS: Prospective, cross-sectional study. Adult patients (age ≥ 18 years) completing automated VF testing were invited to participate. Baseline participant characteristics were obtained, and all participants were asked to complete the Pittsburgh Sleep Quality Index (PSQI) questionnaire. Nonparametric Spearman correlations and logistical regression models were performed. RESULTS: 63 patients were enrolled. Lower PSQI score was correlated with higher percentage (%) FL in the right eye (p = 0.03). Fewer prior VF was significantly correlated with higher %FP in the right eye (p = 0.008). Older age was significantly correlated with higher %FN in the left eye (p = 0.01). Greater mean deviation (MD) and pattern standard deviation (PSD) were strongly correlated with higher %FN in the right (p = 0.02 and 0.002, resp.) and left eyes (p = 0.01 and 0.02, resp.). CONCLUSION: In this prospective, cross-sectional study, worse MD and PSD are strongly correlated with increased FN in both eyes. Increased FN in the left eye associated with older age might be attributable to test fatigue. Worse sleep quality is associated with decreased FL in the right eye.