Short-term Detection of Fast Progressors in Glaucoma: The Fast Progression Assessment through Clustered Evaluation (Fast-PACE) Study.

PURPOSE: To evaluate the performance of an intensive, clustered testing approach in identifying eyes with rapid glaucoma progression over 6 months in the Fast Progression Assessment through Clustered Evaluation (Fast-PACE) Study. DESIGN: Prospective cohort study. PARTICIPANTS: A total of 125 eyes from 65 primary open-angle glaucoma (POAG) subjects. METHODS: Subjects underwent 2 sets of 5 weekly visits (clusters) separated by an average of 6 months and then were followed with single visits every 6 months for an overall mean follow-up of 25 months (mean of 17 tests). Each visit consisted of testing with standard automated perimetry (SAP) 24-2 and 10-2, and spectral-domain OCT (SD-OCT). Progression was assessed using trend analyses of SAP mean deviation (MD) and retinal nerve fiber layer (RNFL) thickness. Generalized estimating equations were applied to adjust for correlations between eyes for confidence interval (CI) estimation and hypothesis testing. MAIN OUTCOME MEASURES: Diagnostic accuracy of the 6-month clustering period to identify progression detected during the overall follow-up. RESULTS: A total of 19 of 125 eyes (15%, CI, 9%-24%) progressed based on SAP 24-2 MD over the 6-month clustering period. A total of 14 eyes (11%, CI, 6%-20%) progressed on SAP 10-2 MD, and 16 eyes (13%, CI, 8%-21%) progressed by RNFL thickness, with 30 of 125 eyes (24%, CI, 16%-34%) progressing by function, structure, or both. Of the 35 eyes progressing during the overall follow-up, 25 had progressed during the 6-month clustering period, for a sensitivity of 71% (CI, 53%-85%). Of the 90 eyes that did not progress during the overall follow-up, 85 also did not progress during the 6-month period, for a specificity of 94% (CI, 88%-98%). Of the 14 eyes considered fast progressors by SAP 24-2, SAP 10-2, or SD-OCT during the overall follow-up, 13 were identified as progressing during the 6-month cluster period, for a sensitivity of 93% (CI, 66%-100%) for identifying fast progression with a specificity of 85% (CI, 77%-90%). CONCLUSIONS: Clustered testing in the Fast-PACE Study detected fast-progressing glaucoma eyes over 6 months. The methodology could be applied in clinical trials investigating interventions to slow glaucoma progression and may be of value for short-term assessment of high-risk subjects. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references in the Footnotes and Disclosures at the end of this article.

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

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

Effectiveness of Microinvasive Glaucoma Surgery in the United States: Intelligent Research in Sight Registry Analysis 2013-2019.

PURPOSE: To evaluate the effectiveness of microinvasive glaucoma surgery (MIGS) with and without concurrent phacoemulsification. DESIGN: Multicenter, retrospective cohort study. PARTICIPANTS: Patients in the Intelligent Research in Sight (IRIS®) Registry who underwent Xen gel stent (ab interno) implantation, endoscopic cyclophotocoagulation (ECP), or goniotomy or canaloplasty from 2013 through 2019. METHODS: Kaplan-Meier survival analysis was used to assess reoperation rates. We defined reoperation as any subsequent glaucoma surgery occurring 1 month to 3 years after the initial procedure. Multivariable Cox proportional hazard models were used to determine factors predictive of reoperation. MAIN OUTCOME MEASURES: Reoperation rate, mean intraocular pressure (IOP) and visual acuity (VA), postoperative complications, predictors of reoperation, and reoperation procedure type. RESULTS: A total of 79 363 eyes from 57 561 patients were included, with 15 118 eyes (19%) receiving stand-alone MIGS and 64 245 eyes (81%) receiving MIGS concurrent with phacoemulsification. Overall, patients who underwent MIGS concurrently with phacoemulsification showed lower reoperation rates compared with stand-alone MIGS, most pronounced in ECP and goniotomy or canaloplasty. At postoperative year 2, the cumulative reoperation rate for stand-alone procedures was 15% for ECP, 24% for Xen implantation, and 24% for goniotomy or canaloplasty compared with 3% for ECP, 19% for Xen implantation, and 6% for goniotomy or canaloplasty concurrent with phacoemulsification (P < 0.001 for each stand-alone MIGS vs. MIGS with phacoemulsification). Black race, older age, moderate and severe glaucoma, higher baseline IOP, and glaucoma subtype were associated with higher reoperation risk. Although IOP decreased in all groups, stand-alone MIGS showed a more substantial decrease in mean IOP. Complication rates from MIGS were low overall: 1% for ECP, 1% for Xen implantation, and 2% for goniotomy or canaloplasty. CONCLUSIONS: In current United States clinical practice, MIGS has substantially lower reoperation rates when performed with phacoemulsification, especially for ECP and goniotomy or canaloplasty. Approximately one-sixth of patients undergoing stand-alone ECP and one-quarter of patients undergoing stand-alone Xen implantation or goniotomy or canaloplasty require reoperation by 2 years. Black race, diagnosis coding of moderate to severe glaucoma, and higher baseline IOP were associated with higher risk of reoperation after MIGS procedures. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

Machine-Identified Patterns of Visual Field Loss and an Association with Rapid Progression in the Ocular Hypertension Treatment Study.

PURPOSE: To identify patterns of visual field (VF) loss based on unsupervised machine learning and to identify patterns that are associated with rapid progression. DESIGN: Cross-sectional and longitudinal study. PARTICIPANTS: A total of 2231 abnormal VFs from 205 eyes of 176 Ocular Hypertension Treatment Study (OHTS) participants followed over approximately 16 years. METHODS: Visual fields were assessed by an unsupervised deep archetypal analysis algorithm and an OHTS-certified VF reader to identify prevalent patterns of VF loss. Machine-identified patterns of glaucoma damage were compared against those patterns previously identified (expert-identified) in the OHTS in 2003. Based on the longitudinal VFs of each eye, VF loss patterns that were strongly associated with rapid glaucoma progression were identified. MAIN OUTCOME MEASURES: Machine-expert correspondence and type of patterns of VF loss associated with rapid progression. RESULTS: The average VF mean deviation (MD) at conversion to glaucoma was -2.7 decibels (dB) (standard deviation [SD] = 2.4 dB), whereas the average MD of the eyes at the last visit was -5.2 dB (SD = 5.5 dB). Fifty out of 205 eyes had MD rate of -1 dB/year or worse and were considered rapid progressors. Eighteen machine-identified patterns of VF loss were compared with expert-identified patterns, in which 13 patterns of VF loss were similar. The most prevalent expert-identified patterns included partial arcuate, paracentral, and nasal step defects, and the most prevalent machine-identified patterns included temporal wedge, partial arcuate, nasal step, and paracentral VF defects. One of the machine-identified patterns of VF loss predicted future rapid VF progression after adjustment for age, sex, and initial MD. CONCLUSIONS: An automated machine learning system can identify patterns of VF loss and could provide objective and reproducible nomenclature for characterizing early signs of visual defects and rapid progression in patients with glaucoma.

Sex-Based Differences in Medicare Reimbursements among Ophthalmologists Persist across Time.

PURPOSE: To evaluate differences in Medicare reimbursements between male and female ophthalmologists between 2013 and 2019. DESIGN: Retrospective cohort study. PARTICIPANTS: Ophthalmologists receiving Medicare reimbursements between 2013 and 2019. METHODS: The Centers for Medicare and Medicaid Services Physician and Other Supplier Public Use File was used to determine total reimbursements and number of services submitted by ophthalmologists between 2013 and 2019. Reimbursements were standardized to account for geographic differences in Medicare reimbursement per service. Data from the American Community Survey (ACS) were used to determine socioeconomic characteristics (unemployment, poverty, income, and education) by zip code for the location of each physician's practice. A multivariate linear regression model was used to evaluate differences in annual reimbursements by sex, accounting for calendar year, years of experience, total number of services, ACS zip code data, and proportion of procedural services. MAIN OUTCOME MEASURES: Annual Medicare reimbursement and use of billing codes (e.g., outpatient office visits and eye examinations, diagnostic testing, laser treatment, and surgery). RESULTS: Among 20 281 ophthalmologists who received Medicare reimbursements between 2013 and 2019, 15 451 (76%) were men. The most common billing codes submitted were for outpatient visits and eye examinations (13.8 million charges/year), diagnostic imaging of the retina (5.6 million charges/year), intravitreal injections (2.9 million charges/year), and removal of cataract with insertion of lens (2.4 million charges/year). Compared with men, female ophthalmologists received less in median annual reimbursements (median, $94 734.21 [interquartile range (IQR), $30 944.52-$195 701.70] for women vs. $194 176.90 [IQR, $76 380.76-$355 790.80] for men; P < 0.001) and billed for fewer annual median services (median, 1228 [IQR, 454-2433] vs. 2259 [IQR, 996-4075, respectively]; P < 0.001). After adjustment for covariates, female ophthalmologists billed for 1015 fewer services (95% confidence interval [CI], 1001-1029; P < 0.001) and received $20 209.12 less in reimbursements than men (95% CI, -$21 717.57 to -$18 700.66; P < 0.001). CONCLUSIONS: Female ophthalmologists billed for fewer services and received less in reimbursement from Medicare than men over time and across all categories of billing codes. Disparities persisted after controlling for physician and practice characteristics.

Use of Teleophthalmology for Evaluation of Ophthalmic Emergencies by Ophthalmology Residents in the Emergency Department.

Background:Utilizing telemedicine is one approach to reduce the ever-increasing burden of patients on emergency departments (EDs) and consulting physicians. Utilization of telemedicine services in the ED may also benefit resident education.Materials and Methods:Ten first-year ophthalmology residents were trained to use a Topcon 3D Optical Coherence Tomography (OCT)-1 Maestro to capture OCT images and fundus photos in patients presenting to the ED with urgent ophthalmic concerns. Findings were communicated to the supervising ophthalmologist. Retrospective chart review was conducted to obtain patient characteristics and final ophthalmologist diagnosis. Residents rated ease of use, technical reliability, and educational value through a survey.Results:From December 1, 2019, to December 1, 2020, the device was used in 109 patient encounters, capturing 887 images (average 8.1 images per encounter). Patients on whom the device was used were on average 48.5 years old (±17.2, range 17-90) and 59.6% were female. The imaging device was utilized most commonly for evaluating papilledema (n = 21, 18.6%), new-onset visual acuity/visual field defects (n = 12, 10.6%), retinal detachment/tear (n = 8, 7.1%), and ophthalmic trauma workup (n = 8, 7.1%). Eight residents completed the survey and most (n = 7) agreed or strongly agreed that the device helped them diagnose patients more accurately. Technical issues such as machine malfunction, image artifacts, and problems syncing with the electronic health record and computer were noted by survey respondents.Conclusions:The most common use of teleophthalmology in the ED setting was evaluation of papilledema; the majority of residents perceived an educational benefit from this tool. Efforts should be made to address the technical challenges to increase the utility of this device.

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

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

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

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

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.

Incorporating a virtual curriculum into ophthalmology education in the coronavirus disease-2019 era.

PURPOSE OF REVIEW: The purpose of this review is to describe the transition of ophthalmology education to a virtual curriculum during the COVID-19 pandemic. We highlight innovative solutions ophthalmic educators implemented for medical students, residents, and fellows. RECENT FINDINGS: Clinical and surgical ophthalmology volume abruptly decreased in March 2020 as a result of the COVID-19 public health crisis. All didactics, grand rounds, and journal clubs shifted to an online format. Ophthalmic educators collaborated with other institutions in the United States and abroad on shared virtual curricula and seminars, and simulation-based training tools were better emphasized for surgical skill acquisition. We share the latest literature written by ophthalmic educators on successful adaptation of these new instructional strategies. SUMMARY: The COVID-19 pandemic transformed ophthalmology education overnight and accelerated the adoption of innovative e-virtual learning resources for trainees. These changes will undoubtedly remain part of the new paradigm of medical education in the post-COVID era.

Medicare Incentive Payments to United States Ophthalmologists for Use of Electronic Health Records: 2011-2016.

PURPOSE: To investigate ophthalmologists' rate of attestation to meaningful use (MU) of their electronic health record (EHR) systems in the Medicare EHR Incentive Program and their continuity and success in receiving payments in comparison with other specialties. DESIGN: Administrative database study. PARTICIPANTS: Eligible professionals participating in the Medicare EHR Incentive Program. METHODS: Based on publicly available data sources, subsets of payment and attestation data were created for ophthalmologists and for other specialties. The number of eligible professionals attesting was determined using the attestation data for each year and stage of the program. The proportion of attestations by EHR vendor was calculated using all attestations for each vendor. MAIN OUTCOME MEASURES: Numbers of ophthalmologists attesting by year and stage of the Medicare EHR Incentive Program, incentive payments, and number of attestations by EHR vendor. RESULTS: In the peak year of participation, 51.6% of ophthalmologists successfully attested to MU, compared with 37.1% of optometrists, 50.2% of dermatologists, 54.5% of otolaryngologists, and 64.4% of urologists. Across the 6 years of the program, ophthalmologists received an average of $17 942 in incentive payments compared with $11 105 for optometrists, $16 617 for dermatologists, $20 203 for otolaryngologists, and $23 821 for urologists. Epic and Nextgen were the most frequently used EHRs for attestation by ophthalmologists. CONCLUSIONS: Ophthalmology as a specialty performed better than optometry and dermatology, but worse than otolaryngology and urology, in terms of the proportion of eligible professionals attesting to MU of EHRs. Ophthalmologists were more likely to remain in the program after their initial year of attestation compared with all eligible providers. The top 4 EHR vendors accounted for 50% of attestations by ophthalmologists.

Greater Physical Activity Is Associated with Slower Visual Field Loss in Glaucoma.

PURPOSE: To determine the association between physical activity levels and the rate of visual field (VF) loss in glaucoma. DESIGN: Longitudinal, observational study. PARTICIPANTS: Older adults with suspect or manifest glaucoma. METHODS: Participants wore accelerometers for 1 week to define average steps per day, minutes of moderate-to-vigorous activity, and minutes of nonsedentary activity. All available VF measurements before and after physical activity assessment were retrospectively analyzed to measure rates of VF loss. MAIN OUTCOME MEASURES: Pointwise changes in VF sensitivity associated with physical activity measures. RESULTS: A total of 141 participants (mean age, 64.9±5.8 years) were enrolled. Eye mean deviation (MD) at the time of physical activity assessment was -6.6 decibels (dB), and average steps per day were 5613±3158. The unadjusted average rate of VF loss as measured by pointwise VF sensitivity was 0.36 dB/year (95% confidence interval, -0.37 to -0.35). In multivariable models, slower VF loss was observed for patients demonstrating more steps (+0.007 dB/year/1000 daily steps, P < 0.001), more moderate-to-vigorous activity (+0.003 dB/year/10 more minutes of moderate-to-vigorous activity per day, P < 0.001), and more nonsedentary activity (+0.007 dB/year/30 more minutes of nonsedentary time per day, P = 0.005). Factors associated with a faster rate of VF loss included older age, non-white race, glaucoma surgery, cataract surgery, and moderate baseline VF damage (-6 dB ≥ MD >-12 dB) as opposed to mild VF damage (MD >-6 dB). Similar associations between baseline accelerometer-measured physical activity and rates of VF loss were observed over other time periods (e.g., within 1, 3, and 5 years of activity assessment). CONCLUSIONS: Increased walking, greater time spent doing moderate-to-vigorous physical activity, and more time spent in nonsedentary activity were associated with slower rates of VF loss in a treated population of patients with glaucoma, with an additional 5000 daily steps or 2.6 hours of nonsedentary physical activity decreasing the average rate of VF loss by approximately 10%. Future prospective studies are needed to determine if physical activity can slow VF loss in glaucoma or if progressive VF loss results in activity restriction. If the former is confirmed, this would mark physical activity as a novel modifiable risk factor for preventing glaucoma damage.

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.

Artificial intelligence in glaucoma.

PURPOSE OF REVIEW: The use of computers has become increasingly relevant to medical decision-making, and artificial intelligence methods have recently demonstrated significant advances in medicine. We therefore provide an overview of current artificial intelligence methods and their applications, to help the practicing ophthalmologist understand their potential impact on glaucoma care. RECENT FINDINGS: Techniques used in artificial intelligence can successfully analyze and categorize data from visual fields, optic nerve structure [e.g., optical coherence tomography (OCT) and fundus photography], ocular biomechanical properties, and a combination thereof to identify disease severity, determine disease progression, and/or recommend referral for specialized care. Algorithms have become increasingly complex in recent years, utilizing both supervised and unsupervised methods of artificial intelligence. Impressive performance of these algorithms on previously unseen data has been reported, often outperforming standard global indices and expert observers. However, there remains no clearly defined gold standard for determining the presence and severity of glaucoma, which undermines the training of these algorithms. To improve upon existing methodologies, future work must employ more robust definitions of disease, optimize data inputs for artificial intelligence analysis, and improve methods of extracting knowledge from learned results. SUMMARY: Artificial intelligence has the potential to revolutionize the screening, diagnosis, and classification of glaucoma, both through the automated processing of large data sets, and by earlier detection of new disease patterns. In addition, artificial intelligence holds promise for fundamentally changing research aimed at understanding the development, progression, and treatment of glaucoma, by identifying novel risk factors and by evaluating the importance of existing ones.

Supervision and autonomy of ophthalmology residents in the outpatient clinic in the United States II: a survey of senior residents.

BACKGROUND: A balance between autonomy and supervision can be difficult to obtain in medical education. In this study, we sought to determine whether the presence and level of supervision of ophthalmology resident outpatient clinic correlates with metrics of resident success, professionalism and stress. METHODS: A survey was emailed to all US ophthalmology program directors requesting it be forwarded to PGY4 residents. Questions included whether their program provided a resident-hosted outpatient clinic, and if so, whether residents were mandated to discuss every patient with faculty. Residents were assigned to three categories based on this question (0: no clinic, 1: mandated faculty input, 2: discretionary faculty input). Success metrics included numbers of manuscripts submitted, OKAP scores and success in obtaining fellowships. Professionalism metrics included rating comfort obtaining informed consent, breaking bad news, managing time in clinic, and confidence in providing care in various settings. Residents affirming participation in a continuity clinic also provided perceptions of the level of supervision and how the clinic affected stress. RESULTS: Category 1 residents perceived somewhat too much supervision, while category 2 residents felt that they had somewhat insufficient supervision. The majority of residents in either category did not feel that the continuity clinic affected their overall stress, although those who reported a change in stress usually indicated that the presence of the clinic increased stress. There were no other statistically significant differences between the responses from any category. CONCLUSIONS: The presence of a resident-hosted continuity clinic neither adds nor detracts from the success or sense of professionalism of ophthalmology residents. However, when such a clinic is present, the degree of supervision appears to correlate inversely with resident perception of autonomy. These results suggest that the decision of a training program to offer a clinic hosted by residents offering comprehensive continuity care can be informed primarily by faculty and trainee philosophy and personal preferences without comprising education quality, clinical efficiency, residents' perception of stress or their success in fellowship matching.

Resident and program characteristics that impact performance on the Ophthalmic Knowledge Assessment Program (OKAP).

BACKGROUND: To determine which resident and program characteristics correlate with ophthalmic knowledge, as assessed by resident Ophthalmic Knowledge Assessment Program (OKAP) performance. METHODS: An online survey was sent in June 2017 to all US ophthalmology residents who took the OKAP in April 2017. RESULTS: The survey response rate was 13.8% (192/1387 residents). The mean respondent age was 30.4 years, and 57.3% were male. The mean [SD] self-reported 2017 OKAP percentile was 61.9 [26.7]. OKAP performance was found to have a significant positive correlation with greater number of hours spent/week studying for the OKAPs (p = 0.007), with use of online question banks (p < 0.001), with review sessions and/or lectures arranged by residency programs (p < 0.001), and with OKAP-specific didactics (p = 0.002). On multivariable analysis, factors most predictive of residents scoring ≥75th percentile were, higher step 1 scores (OR = 2.48, [95% CI: 1.68-3.64, p < 0.001]), presence of incentives (OR = 2.75, [95% CI: 1.16-6.56, p = 0.022]), greater number of hours/week spent studying (OR = 1.09, [95% CI:1.01-1.17, p = 0.026]) and fewer hours spent in research 3 months prior to examination (OR = 1.08, [95% CI: 1.01-1.15, p = 0.020]. Lastly, residents less likely to depend on group study sessions as a learning method tended to score higher (OR = 3.40, [95% CI: 1.16-9.94, p = 0.026]). CONCLUSIONS: Programs wishing to improve resident OKAP scores might consider offering incentives, providing effective access to learning content e.g. online question banks, and adjusting the curriculum to highlight OKAP material. Step 1 scores may help educators identify residents who might be at risk of not performing as well on the OKAP.

Reversal of Glaucoma Hemifield Test Results and Visual Field Features in Glaucoma.

PURPOSE: To develop a visual field (VF) feature model to predict the reversal of glaucoma hemifield test (GHT) results to within normal limits (WNL) after 2 consecutive outside normal limits (ONL) results. DESIGN: Retrospective cohort study. PARTICIPANTS: Visual fields of 44 503 eyes from 26 130 participants. METHODS: Eyes with 3 or more consecutive reliable VFs measured with the Humphrey Field Analyzer (Swedish interactive threshold algorithm standard 24-2) were included. Eyes with ONL GHT results for the 2 baseline VFs were selected. We extracted 3 categories of VF features from the baseline tests: (1) VF global indices (mean deviation [MD] and pattern standard deviation), (2) mismatch between baseline VFs, and (3) VF loss patterns (archetypes). Logistic regression was applied to predict the GHT results reversal. Cross-validation was applied to evaluate the model on testing data by the area under the receiver operating characteristic curve (AUC). We ascertained clinical glaucoma status on a patient subset (n = 97) to determine the usefulness of our model. MAIN OUTCOME MEASURES: Predictive models for GHT results reversal using VF features. RESULTS: For the 16 604 eyes with 2 initial ONL results, the prevalence of a subsequent WNL result increased from 0.1% for MD < -12 dB to 13.8% for MD ≥-3 dB. Compared with models with VF global indices, the AUC of predictive models increased from 0.669 (MD ≥-3 dB) and 0.697 (-6 dB ≤ MD < -3 dB) to 0.770 and 0.820, respectively, by adding VF mismatch features and computationally derived VF archetypes (P < 0.001 for both). The GHT results reversal was associated with a large mismatch between baseline VFs. Moreover, the GHT results reversal was associated more with VF archetypes of nonglaucomatous loss, severe widespread loss, and lens rim artifacts. For a subset of 97 eyes, using our model to predict absence of glaucoma based on clinical evidence after 2 ONL results yielded significantly better prediction accuracy (87.7%; P < 0.001) than predicting GHT results reversal (68.8%) with a prescribed specificity 67.7%. CONCLUSIONS: Using VF features may predict the GHT results reversal to WNL after 2 consecutive ONL results.

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

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