Publications by authors named "Tobias Elze"

48 Publications

Predicting eyes at risk for rapid glaucoma progression based on an initial visual field test using machine learning.

PLoS One 2021 16;16(4):e0249856. Epub 2021 Apr 16.

Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America.

Objective: To assess whether machine learning algorithms (MLA) can predict eyes that will undergo rapid glaucoma progression based on an initial visual field (VF) test.

Design: Retrospective analysis of longitudinal data.

Subjects: 175,786 VFs (22,925 initial VFs) from 14,217 patients who completed ≥5 reliable VFs at academic glaucoma centers were included.

Methods: Summary measures and reliability metrics from the initial VF and age were used to train MLA designed to predict the likelihood of rapid progression. Additionally, the neural network model was trained with point-wise threshold data in addition to summary measures, reliability metrics and age. 80% of eyes were used for a training set and 20% were used as a test set. MLA test set performance was assessed using the area under the receiver operating curve (AUC). Performance of models trained on initial VF data alone was compared to performance of models trained on data from the first two VFs.

Main Outcome Measures: Accuracy in predicting future rapid progression defined as MD worsening more than 1 dB/year.

Results: 1,968 eyes (8.6%) underwent rapid progression. The support vector machine model (AUC 0.72 [95% CI 0.70-0.75]) most accurately predicted rapid progression when trained on initial VF data. Artificial neural network, random forest, logistic regression and naïve Bayes classifiers produced AUC of 0.72, 0.70, 0.69, 0.68 respectively. Models trained on data from the first two VFs performed no better than top models trained on the initial VF alone. Based on the odds ratio (OR) from logistic regression and variable importance plots from the random forest model, older age (OR: 1.41 per 10 year increment [95% CI: 1.34 to 1.08]) and higher pattern standard deviation (OR: 1.31 per 5-dB increment [95% CI: 1.18 to 1.46]) were the variables in the initial VF most strongly associated with rapid progression.

Conclusions: MLA can be used to predict eyes at risk for rapid progression with modest accuracy based on an initial VF test. Incorporating additional clinical data to the current model may offer opportunities to predict patients most likely to rapidly progress with even greater accuracy.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0249856PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8051770PMC
April 2021

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

Ophthalmol Glaucoma 2021 Apr 10. Epub 2021 Apr 10.

Bernard and Shirlee Brown Glaucoma Research Laboratory, Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York, NY. Electronic address:

Purpose: To compare the variability and ability to detect visual field progression of 24-2, central 12 locations of the 24-2 and 10-2 visual field (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 7,307 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 MD (Mean Deviation) change (slopes) while their residuals were used to assess variability across the entire MD range. Computer simulations (n=10,000) based upon 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 dB range and consistently lower with the 10-2 compared to 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 to 24-2 and a decrease of 22.9% to 26.5% when compared to Central 24-2.

Conclusion: 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 glaucoma patients and in clinical trial design.
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http://dx.doi.org/10.1016/j.ogla.2021.04.004DOI Listing
April 2021

Trends and Usage Patterns of Minimally Invasive Glaucoma Surgery (MIGS) in the US: IRIS Registry Analysis 2013-2018.

Ophthalmol Glaucoma 2021 Apr 5. Epub 2021 Apr 5.

Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA; Glaucoma Service, Massachusetts Eye and Ear, Boston, MA; Harvard Medical School, Department of Ophthalmology, Boston, MA.

Objective: Understanding trends and patterns in the use of minimally invasive glaucoma surgery (MIGS) and patient profiles undergoing each procedure is important given their relative expense and unknown long-term safety and effectiveness.

Design: Retrospective analysis SUBJECTS: MIGS and standard glaucoma surgeries recorded in the American Academy of Ophthalmology (AAO) Intelligent Research in Sight (IRIS®) Registry.

Methods: We used the data from IRIS® Registry between 2013-2018 (inclusive) to measure annual number of MIGS and standard surgical techniques (trabeculectomy or glaucoma drainage device (GDD)) performed in the US, stratified by demographic characteristics. Secondary analyses of concurrent surgeries and of subsequent surgeries for MIGS and standard surgical technique were also conducted.

Main Outcome Measures: Trends and sociodemographic characteristics of MIGS usage in the US.

Results: 203,332 eyes and 232,537 unique procedures had associated, documented International Statistical Classification of Diseases and Related Health Problems (ICD) 9/10 codes for glaucoma and were included in final analyses. Among eyes with documented glaucoma diagnoses, there was a substantial increase in annual MIGS procedures over the study period (from 7,586 in 2013 to 39,677), and a smaller decrease in standard glaucoma procedures (from 16,215 to 13,701). The proportion of iStent procedures almost tripled during the study period (from 14% to 40%), and by 2017 accounted for almost half (43.7%) of all glaucoma surgeries in the US. 21,025 (10.3%) of all eyes received multiple procedures; 7,638 (36.3%) on the same day and 13,387 (63.7%) on subsequent days. ECP and iStent were the most common concurrent procedures (55.4% of all concurrent procedures). Trabeculectomy and GDD were most commonly followed by another standard glaucoma surgery, but when followed by sequential MIGS, ECP and goniotomy were the most common procedures performed (33.0%, 21.9%, respectively).

Conclusions: There was a significant increase in MIGS use over the recent six-year period despite limited evidence of their long-term safety or effectiveness, highlighting the need for trials comparing safety and outcomes of novel MIGS vs traditional surgical treatments for glaucoma.
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http://dx.doi.org/10.1016/j.ogla.2021.03.012DOI Listing
April 2021

Chemical and thermal ocular burns in the United States: An IRIS registry analysis.

Ocul Surf 2021 Mar 31. Epub 2021 Mar 31.

Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA. Electronic address:

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http://dx.doi.org/10.1016/j.jtos.2021.03.008DOI Listing
March 2021

Characteristics of Gln368Ter Myocilin Variant and Influence of Polygenic Risk on Glaucoma Penetrance in the UK Biobank.

Ophthalmology 2021 Mar 10. Epub 2021 Mar 10.

Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts; Ocular Genomics Institute, Harvard Medical School, Boston, Massachusetts.

Purpose: MYOC (myocilin) mutations account for 3% to 5% of primary open-angle glaucoma (POAG) cases. We aimed to understand the true population-wide penetrance and characteristics of glaucoma among individuals with the most common MYOC variant (p.Gln368Ter) and the impact of a POAG polygenic risk score (PRS) in this population.

Design: Cross-sectional population-based study.

Participants: Individuals with the p.Gln368Ter variant among 77 959 UK Biobank participants with fundus photographs (FPs).

Methods: A genome-wide POAG PRS was computed, and 2 masked graders reviewed FPs for disc-defined glaucoma (DDG).

Main Outcome Measures: Penetrance of glaucoma.

Results: Two hundred individuals carried the p.Gln368Ter heterozygous genotype, and 177 had gradable FPs. One hundred thirty-two showed no evidence of glaucoma, 45 (25.4%) had probable/definite glaucoma in at least 1 eye, and 19 (10.7%) had bilateral glaucoma. No differences were found in age, race/ethnicity, or gender among groups (P > 0.05). Of those with DDG, 31% self-reported or had International Classification of Diseases codes for glaucoma, whereas 69% were undiagnosed. Those with DDG had higher medication-adjusted cornea-corrected intraocular pressure (IOPcc) (P < 0.001) vs. those without glaucoma. This difference in IOPcc was larger in those with DDG with a prior glaucoma diagnosis versus those not diagnosed (P < 0.001). Most p.Gln368Ter carriers showed IOP in the normal range (≤21 mmHg), although this proportion was lower in those with DDG (P < 0.02) and those with prior glaucoma diagnosis (P < 0.03). Prevalence of DDG increased with each decile of POAG PRS. Individuals with DDG demonstrated significantly higher PRS compared with those without glaucoma (0.37 ± 0.97 vs. 0.01 ± 0.90; P = 0.03). Of those with DDG, individuals with a prior diagnosis of glaucoma had higher PRS compared with undiagnosed individuals (1.31 ± 0.64 vs. 0.00 ± 0.81; P < 0.001) and 27.5 times (95% confidence interval, 2.5-306.6) adjusted odds of being in the top decile of PRS for POAG.

Conclusions: One in 4 individuals with the MYOC p.Gln368Ter mutation demonstrated evidence of glaucoma, a substantially higher penetrance than previously estimated, with 69% of cases undetected. A large portion of p.Gln368Ter carriers, including those with DDG, have IOP in the normal range, despite similar age. Polygenic risk score increases disease penetrance and severity, supporting the usefulness of PRS in risk stratification among MYOC p.Gln368Ter carriers.
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http://dx.doi.org/10.1016/j.ophtha.2021.03.007DOI Listing
March 2021

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

Ophthalmol Glaucoma 2020 Dec 11. Epub 2020 Dec 11.

Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts. Electronic address:

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 R = 0.45), adding TD values improved prediction accuracy of the global variability (adjusted R = 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 R 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.
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http://dx.doi.org/10.1016/j.ogla.2020.12.001DOI Listing
December 2020

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

Transl Vis Sci Technol 2020 11 17;9(12):22. Epub 2020 Nov 17.

Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.

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.
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http://dx.doi.org/10.1167/tvst.9.12.22DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7683854PMC
November 2020

Three-dimensional Neuroretinal Rim Thickness and Visual Fields in Glaucoma: A Broken-stick Model.

J Glaucoma 2020 10;29(10):952-963

Department of Ophthalmology, Massachusetts Eye and Ear.

Precis: In open-angle glaucoma, when neuroretinal rim tissue measured by volumetric optical coherence tomography (OCT) scans is below a third of the normal value, visual field (VF) damage becomes detectable.

Purpose: To determine the amount of neuroretinal rim tissue thickness below which VF damage becomes detectable.

Methods: In a retrospective cross-sectional study, 1 eye per subject (of 57 healthy and 100 open-angle glaucoma patients) at an academic institution had eye examinations, VF testing, spectral-domain OCT retinal nerve fiber layer (RNFL) thickness measurements, and optic nerve volumetric scans. Using custom algorithms, the minimum distance band (MDB) neuroretinal rim thickness was calculated from optic nerve scans. "Broken-stick" regression was performed for estimating both the MDB and RNFL thickness tipping-point thresholds, below which were associated with initial VF defects in the decibel scale. The slopes for the structure-function relationship above and below the thresholds were computed. Smoothing curves of the MDB and RNFL thickness covariates were evaluated to examine the consistency of the independently identified tipping-point pairs.

Results: Plots of VF total deviation against MDB thickness revealed plateaus of VF total deviation unrelated to MDB thickness. Below the thresholds, VF total deviation decreased with MDB thickness, with the associated slopes significantly greater than those above the thresholds (P<0.014). Below 31% of global MDB thickness, and 36.8% and 43.6% of superior and inferior MDB thickness, VF damage becomes detectable. The MDB and RNFL tipping points were in good accordance with the correlation of the MDB and RNFL thickness covariates.

Conclusions: When neuroretinal rim tissue, characterized by MDB thickness in OCT, is below a third of the normal value, VF damage in the decibel scale becomes detectable.
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http://dx.doi.org/10.1097/IJG.0000000000001604DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7541591PMC
October 2020

An Artificial Intelligence Approach to Assess Spatial Patterns of Retinal Nerve Fiber Layer Thickness Maps in Glaucoma.

Transl Vis Sci Technol 2020 08 27;9(9):41. Epub 2020 Aug 27.

Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.

Purpose: The purpose of this study was to classify the spatial patterns of retinal nerve fiber layer thickness (RNFLT) and assess their associations with visual field (VF) loss in glaucoma.

Methods: We used paired reliable 24-2 VFs and optical coherence tomography scans of 691 eyes from 691 patients. The RNFLT maps were used to determine the RNFLT patterns (RPs) by non-negative matrix factorization (NMF). The RPs were correlated with mean deviation (MD), spherical equivalent (SE), and major blood vessel locations. The RPs were further used to predict the 52 total deviation (TD) values by linear regression compared with models using 24 15-degree sectors. Last, we associated the RPs with average TDs of the central upper two locations (C2-TD). Stepwise regression was applied to remove redundant features.

Results: NMF highlighted 16 distinct RPs. Twelve RPs had arcuate-like informative zones (iZones): six with superior iZones, five with inferior iZones, and one RP with a bi-hemifield iZone, and four with non-arcuate-like temporal or nasal iZones. Twelve, nine, nine, and nine RPs were significantly ( < 0.05) correlated to MD, SE, and superior and inferior artery locations, respectively. Using RPs significantly ( < 0.05) improved the prediction of 52 TDs compared with using 24 15-degree sectors. Using RPs significantly ( < 0.001) improved the C2-TD prediction related to thinning in the inferior vulnerability zone compared with using the 24 sectoral RNFLTs.

Conclusions: Using RPs improved the VF prediction compared with using sectoral RNFLTs.

Translational Relevance: The RPs characterizing both pathological and anatomical variations can potentially assist clinicians better assess RNFLT loss.
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http://dx.doi.org/10.1167/tvst.9.9.41DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7453051PMC
August 2020

Norms of Interocular Circumpapillary Retinal Nerve Fiber Layer Thickness Differences at 768 Retinal Locations.

Transl Vis Sci Technol 2020 08 12;9(9):23. Epub 2020 Aug 12.

Leipzig Research Centre for Civilization Diseases (LIFE), Leipzig University, Leipzig, Germany.

Purpose: The onset and progression of optic neuropathies like glaucoma often occurs asymmetrically between the two eyes of a patient. Interocular circumpapillary retinal nerve fiber layer thickness (cpRNFLT) differences could detect disease earlier. To apply such differences diagnostically, detailed location specific norms are necessary.

Methods: Spectral-domain optical coherence tomography cpRNFLT circle scans from the population-based Leipzig Research Centre for Civilization Diseases-Adult study were selected. At each of the 768 radial scanning locations, normative interocular cpRNFLT difference distributions were calculated based on age and interocular radius difference.

Results: A total of 8966 cpRNFLT scans of healthy eyes (4483 patients; 55% female; age range, 20-79 years) were selected. Global cpRNFLT average was 1.53 µm thicker in right eyes ( < 2.2 × 10). On 96% of the 768 locations, left minus right eye differences were significant ( < 0.05), varying between +11.6 µm (superonasal location) and -11.8 µm (nasal location). Increased age and difference in interocular scanning radii were associated with an increased mean and variance of interocular cpRNFLT difference at most retinal locations, apart from the area temporal to the inferior RNF bundle where cpRNFLT becomes more similar between eyes with age.

Conclusions: We provide pointwise normative distributions of interocular cpRNFLT differences at an unprecedentedly high spatial resolution of 768 A-scans and reveal considerable location specific asymmetries as well as their associations with age and scanning radius differences between eyes.

Translational Relevance: To facilitate clinical application, we implement these age- and radius-specific norms across all 768 locations in an open-source software to generate patient-specific normative color plots.
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http://dx.doi.org/10.1167/tvst.9.9.23DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442876PMC
August 2020

Genome-wide association meta-analysis for early age-related macular degeneration highlights novel loci and insights for advanced disease.

BMC Med Genomics 2020 08 26;13(1):120. Epub 2020 Aug 26.

Institute of Human Genetics, University of Regensburg, Regensburg, Germany.

Background: Advanced age-related macular degeneration (AMD) is a leading cause of blindness. While around half of the genetic contribution to advanced AMD has been uncovered, little is known about the genetic architecture of early AMD.

Methods: To identify genetic factors for early AMD, we conducted a genome-wide association study (GWAS) meta-analysis (14,034 cases, 91,214 controls, 11 sources of data including the International AMD Genomics Consortium, IAMDGC, and UK Biobank, UKBB). We ascertained early AMD via color fundus photographs by manual grading for 10 sources and via an automated machine learning approach for > 170,000 photographs from UKBB. We searched for early AMD loci via GWAS and via a candidate approach based on 14 previously suggested early AMD variants.

Results: Altogether, we identified 10 independent loci with statistical significance for early AMD: (i) 8 from our GWAS with genome-wide significance (P < 5 × 10), (ii) one previously suggested locus with experiment-wise significance (P < 0.05/14) in our non-overlapping data and with genome-wide significance when combining the reported and our non-overlapping data (together 17,539 cases, 105,395 controls), and (iii) one further previously suggested locus with experiment-wise significance in our non-overlapping data. Of these 10 identified loci, 8 were novel and 2 known for early AMD. Most of the 10 loci overlapped with known advanced AMD loci (near ARMS2/HTRA1, CFH, C2, C3, CETP, TNFRSF10A, VEGFA, APOE), except two that have not yet been identified with statistical significance for any AMD. Among the 17 genes within these two loci, in-silico functional annotation suggested CD46 and TYR as the most likely responsible genes. Presence or absence of an early AMD effect distinguished the known pathways of advanced AMD genetics (complement/lipid pathways versus extracellular matrix metabolism).

Conclusions: Our GWAS on early AMD identified novel loci, highlighted shared and distinct genetics between early and advanced AMD and provides insights into AMD etiology. Our data provide a resource comparable in size to the existing IAMDGC data on advanced AMD genetics enabling a joint view. The biological relevance of this joint view is underscored by the ability of early AMD effects to differentiate the major pathways for advanced AMD.
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http://dx.doi.org/10.1186/s12920-020-00760-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7449002PMC
August 2020

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

Ophthalmology 2020 09 10;127(9):1170-1178. Epub 2020 Mar 10.

Wilmer Eye Institute and Division of Health Sciences Informatics, Johns Hopkins University, Baltimore, Maryland.

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.
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http://dx.doi.org/10.1016/j.ophtha.2020.03.008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7483368PMC
September 2020

Artificial Intelligence Classification of Central Visual Field Patterns in Glaucoma.

Ophthalmology 2020 06 12;127(6):731-738. Epub 2019 Dec 12.

Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts; Complex Structures in Biology and Cognition, Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany. Electronic address:

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.
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http://dx.doi.org/10.1016/j.ophtha.2019.12.004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7246163PMC
June 2020

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

JAMA Ophthalmol 2020 02;138(2):190-198

Schepens Eye Research Institute, Massachusetts Eye and Ear, Harvard Medical School, Boston.

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.
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http://dx.doi.org/10.1001/jamaophthalmol.2019.5413DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6990977PMC
February 2020

Sex-Specific Differences in Circumpapillary Retinal Nerve Fiber Layer Thickness.

Ophthalmology 2020 03 25;127(3):357-368. Epub 2019 Sep 25.

Leipzig Research Centre for Civilization Diseases, Leipzig University, Leipzig, Germany; Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts.

Purpose: To investigate the role of sex on retinal nerve fiber layer (RNFL) thickness at 768 circumpapillary locations based on OCT findings.

Design: Population-based cross-sectional study.

Participants: We investigated 5646 eyes of 5646 healthy participants from the Leipzig Research Centre for Civilization Diseases (LIFE)-Adult Study of a predominantly white population.

Methods: All participants underwent standardized systemic assessments and ocular imaging. Circumpapillary RNFL (cRNFL) thickness was measured at 768 points equidistant from the optic nerve head using spectral-domain OCT (Spectralis; Heidelberg Engineering, Heidelberg, Germany). To control ocular magnification effects, the true scanning radius was estimated by scanning focus. Student t test was used to evaluate sex differences in cRNFL thickness globally and at each of the 768 locations. Multivariable linear regression and analysis of variance were used to evaluate individual contributions of various factors to cRNFL thickness variance.

Main Outcome Measures: Difference in cRNFL thickness between males and females.

Results: Our population consisted of 54.8% females. The global cRNFL thickness was 1 μm thicker in females (P < 0.001). However, detailed analysis at each of the 768 locations revealed substantial location specificity of the sex effects, with RNFL thickness difference ranging from -9.98 to +8.00 μm. Females showed significantly thicker RNFLs in the temporal, superotemporal, nasal, inferonasal, and inferotemporal regions (43.6% of 768 locations), whereas males showed significantly thicker RNFLs in the superior region (13.2%). The results were similar after adjusting for age, body height, and scanning radius. The superotemporal and inferotemporal RNFL peaks shifted temporally in females by 2.4° and 1.9°, respectively. On regions with significant sex effects, sex explained more RNFL thickness variance than age, whereas the major peak locations and interpeak angle explained most of the RNFL thickness variance unexplained by sex.

Conclusions: Substantial sex effects on cRNFL thickness were found at 56.8% of all 768 circumpapillary locations, with specific patterns for different sectors. Over large regions, sex was at least as important in explaining the cRNFL thickness variance as was age, which is well established to have a substantial impact on cRNFL thickness. Including sex in the cRNFL thickness norm could therefore improve glaucoma diagnosis and monitoring.
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http://dx.doi.org/10.1016/j.ophtha.2019.09.019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7039768PMC
March 2020

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

J Glaucoma 2020 01;29(1):31-38

Bernard and Shirlee Brown Glaucoma Research Laboratory, Edward S. Harkness Eye Institute, Columbia University Medical Center.

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 (β=-0.0079 dB/y; P<0.001) and baseline MD (β=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 (β=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.
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http://dx.doi.org/10.1097/IJG.0000000000001401DOI Listing
January 2020

Reply.

Ophthalmology 2019 10;126(10):e78-e79

Wilmer Eye Institute, Johns Hopkins University, Baltimore, Maryland.

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http://dx.doi.org/10.1016/j.ophtha.2019.04.036DOI Listing
October 2019

The impact of artificial intelligence in the diagnosis and management of glaucoma.

Eye (Lond) 2020 01 20;34(1):1-11. Epub 2019 Sep 20.

Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Deep learning (DL) is a subset of artificial intelligence (AI), which uses multilayer neural networks modelled after the mammalian visual cortex capable of synthesizing images in ways that will transform the field of glaucoma. Autonomous DL algorithms are capable of maximizing information embedded in digital fundus photographs and ocular coherence tomographs to outperform ophthalmologists in disease detection. Other unsupervised algorithms such as principal component analysis (axis learning) and archetypal analysis (corner learning) facilitate visual field interpretation and show great promise to detect functional glaucoma progression and differentiate it from non-glaucomatous changes when compared with conventional software packages. Forecasting tools such as the Kalman filter may revolutionize glaucoma management by accounting for a host of factors to set target intraocular pressure goals that preserve vision. Activation maps generated from DL algorithms that process glaucoma data have the potential to efficiently direct our attention to critical data elements embedded in high throughput data and enhance our understanding of the glaucomatous process. It is hoped that AI will realize more accurate assessment of the copious data encountered in glaucoma management, improving our understanding of the disease, preserving vision, and serving to enhance the deep bonds that patients develop with their treating physicians.
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http://dx.doi.org/10.1038/s41433-019-0577-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7002653PMC
January 2020

Machine Learning in the Detection of the Glaucomatous Disc and Visual Field.

Semin Ophthalmol 2019 27;34(4):232-242. Epub 2019 May 27.

d Department of Ophthalmology , Icahn School of Medicine at Mount Sinai , New York , NY , USA.

Glaucoma is the leading cause of irreversible blindness worldwide. Early detection is of utmost importance as there is abundant evidence that early treatment prevents disease progression, preserves vision, and improves patients' long-term quality of life. The structure and function thresholds that alert to the diagnosis of glaucoma can be obtained entirely via digital means, and as such, screening is well suited to benefit from artificial intelligence and specifically machine learning. This paper reviews the concepts and current literature on the use of machine learning for detection of the glaucomatous disc and visual field.
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http://dx.doi.org/10.1080/08820538.2019.1620801DOI Listing
July 2019

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

Ophthalmology 2019 06 4;126(6):822-828. Epub 2019 Feb 4.

Wilmer Eye Institute, Johns Hopkins University, Baltimore, Maryland.

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.
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http://dx.doi.org/10.1016/j.ophtha.2019.01.029DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7260059PMC
June 2019

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

Invest Ophthalmol Vis Sci 2019 01;60(1):365-375

Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts, United States.

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.
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http://dx.doi.org/10.1167/iovs.18-25568DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6348996PMC
January 2019

Predicting Refractive Outcome of Small Incision Lenticule Extraction for Myopia Using Corneal Properties.

Transl Vis Sci Technol 2018 Sep 26;7(5):11. Epub 2018 Sep 26.

Tianjin Medical University, Tianjin, China.

Purpose: To investigate whether preoperative corneal topographic and biomechanical parameters (CTBPs) predict postoperative residual refractive error (RRE).

Methods: We retrospectively included 151 eyes from 151 patients of small-incision lenticule extraction (SMILE) with target RRE of plano and 3-month measurements of refractive error from Tianjin Eye Hospital. Multivariate linear/logistic regressions were performed to associate age, gender, preoperative refractive error, lenticule thickness, and CTBPs with postoperative RRE/the occurrence of myopic RRE ≤ -0.25 diopter (D). Stepwise regression was used for feature selection. Leave-one-cross-validation was used for model evaluation by the area under the receiver operating characteristic curve (AUC).

Results: From linear regression, more myopic RRE was associated with higher preoperative myopia, intraocular pressure (IOP), flattest curvature of anterior cornea (AC), and highest concavity deformation (HCD), and was associated with lower anterior elevation, anterior asphericity, steepest curvature of AC, and second applanation velocity. The occurrence of ≤ -0.25 D RRE was associated with higher myopia, IOP, posterior elevation and asphericity, flattest curvature of AC, first applanation velocity and HCD, and was associated with lower first applanation stiffness parameter, central corneal thickness, anterior elevation and asphericity, steepest curvature of AC, and second applanation velocity as well as thinner lenticule thickness. Compared to the baseline model using age, gender, and preoperative refractive error, adding CTBPs significantly ( < 0.001) improved the AUC performance to 0.771 from 0.615.

Conclusions: Postoperative outcomes of SMILE can be predicted by individual CTBPs.

Translational Relevance: Our findings could be used to customize a refractive nomogram based on individual corneal properties improving outcomes and patient satisfaction.
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http://dx.doi.org/10.1167/tvst.7.5.11DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6159733PMC
September 2018

Reply.

Ophthalmology 2018 09 21;125(9):e66-e67. Epub 2018 Aug 21.

Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts; Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany. Electronic address:

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http://dx.doi.org/10.1016/j.ophtha.2018.03.045DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6706862PMC
September 2018

Systemic and Ocular Determinants of Peripapillary Retinal Nerve Fiber Layer Thickness Measurements in the European Eye Epidemiology (E3) Population.

Ophthalmology 2018 10 30;125(10):1526-1536. Epub 2018 Apr 30.

Department of Ophthalmology, University of Bonn, Bonn, Germany. Electronic address:

Purpose: To investigate systemic and ocular determinants of peripapillary retinal nerve fiber layer thickness (pRNFLT) in the European population.

Design: Cross-sectional meta-analysis.

Participants: A total of 16 084 European adults from 8 cohort studies (mean age range, 56.9±12.3-82.1±4.2 years) of the European Eye Epidemiology (E3) consortium.

Methods: We examined associations with pRNFLT measured by spectral-domain OCT in each study using multivariable linear regression and pooled results using random effects meta-analysis.

Main Outcome Measures: Determinants of pRNFLT.

Results: Mean pRNFLT ranged from 86.8±21.4 μm in the Rotterdam Study I to 104.7±12.5 μm in the Rotterdam Study III. We found the following factors to be associated with reduced pRNFLT: Older age (β = -0.38 μm/year; 95% confidence interval [CI], -0.57 to -0.18), higher intraocular pressure (IOP) (β = -0.36 μm/mmHg; 95% CI, -0.56 to -0.15), visual impairment (β = -5.50 μm; 95% CI, -9.37 to -1.64), and history of systemic hypertension (β = -0.54 μm; 95% CI, -1.01 to -0.07) and stroke (β = -1.94 μm; 95% CI, -3.17 to -0.72). A suggestive, albeit nonsignificant, association was observed for dementia (β = -3.11 μm; 95% CI, -6.22 to 0.01). Higher pRNFLT was associated with more hyperopic spherical equivalent (β = 1.39 μm/diopter; 95% CI, 1.19-1.59) and smoking (β = 1.53 μm; 95% CI, 1.00-2.06 for current smokers compared with never-smokers).

Conclusions: In addition to previously described determinants such as age and refraction, we found that systemic vascular and neurovascular diseases were associated with reduced pRNFLT. These may be of clinical relevance, especially in glaucoma monitoring of patients with newly occurring vascular comorbidities.
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http://dx.doi.org/10.1016/j.ophtha.2018.03.026DOI Listing
October 2018

Quantifying positional variation of retinal blood vessels in glaucoma.

PLoS One 2018 15;13(3):e0193555. Epub 2018 Mar 15.

Schepens Eye Research Institute, Harvard Medical School, Boston, MA, United States of America.

We studied the relationship between major retinal blood vessel (BV) positions and glaucoma parameters based on pairs of Cirrus optical coherence tomography scans and Humphrey visual fields of 445 eyes from 445 glaucoma patients in our cross-sectional study. A trained observer marked the major superior and inferior temporal BV (artery and vein) positions on four concentric circles around the optic disc. Analysis of variance was performed to analyze the group differences of BV positions related to the factors of radius, BV type, myopia status and glaucoma stage. Subsequent t-tests were implemented to further study the effect of glaucoma stage on BV positions. The radial variations of BV positions were correlated to mean deviation and circumpapillary retinal nerve fiber layer thickness (cpRNFLT). We found significant main effects of BV type, radius and myopia status for superior and inferior BV positions and of glaucoma stage for superior BV positions (all p≤0.006) with significant superior artery nasalization in advanced compared to mild glaucoma on the two smallest circles (subsequent t-tests, p<0.05). In addition, MD (r = -0.10, p = 0.04) and cpRNFLT (r = -0.12, p = 0.02) were significantly correlated to the angle difference of superior arteries between the innermost and outermost circles. In conclusion, we demonstrated that peripapillary superior artery positions are significantly nasalized for advanced glaucoma.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0193555PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5854294PMC
June 2018

The Interrelationship between Refractive Error, Blood Vessel Anatomy, and Glaucomatous Visual Field Loss.

Transl Vis Sci Technol 2018 Jan 18;7(1). Epub 2018 Jan 18.

Schepens Eye Research Institute, Harvard Medical School, Boston, MA, USA.

Purpose: We quantified the interrelationship between retinal blood vessel (BV) anatomical variation, spherical equivalent (SE) of refractive error, and functional diagnostic parameters in glaucoma to identify optimal parameters for the improvement of optical coherence tomography (OCT) retinal nerve fiber layer thickness (RNFLT) norms.

Methods: A trained observer marked the intersections of the main superior/inferior temporal arteries and veins with concentric circles around the optic nerve head (ONH) center on fundus images. The interrelationship of BV, SE, and visual field global parameters was analyzed by multivariate regression and model comparison.

Results: A total of 445 eyes of 445 patients in a large glaucoma practice were selected. Of all investigated BV parameters, interartery angles (IAA) between superior and inferior arteries at a radius of 1.73 mm around the ONH center demonstrated the strongest relationship to SE (Bayesian information criterion difference to null model, 11.9). SE and BV parameters are unrelated to functional parameters, including mean deviation (MD), pattern standard deviation, and glaucoma hemifield test results.

Conclusions: BV locations outside the ONH are sufficiently stable over glaucoma severity to represent individual eye anatomy, and the IAA at 1.73 mm eccentricity is the optimal parameter to be considered for novel OCT RNFLT norms.

Translational Relevance: Among a large set of BV location parameters, considering IAA may improve RNFLT norms optimally and thereby increase the accuracy of clinical glaucoma diagnosis.
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http://dx.doi.org/10.1167/tvst.7.1.4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5772833PMC
January 2018

Age, ocular magnification, and circumpapillary retinal nerve fiber layer thickness.

J Biomed Opt 2017 12;22(12):1-19

Leipzig University, Leipzig Research Centre for Civilization Diseases, Leipzig, Germany.

Optical coherence tomography (OCT) manufacturers graphically present circumpapillary retinal nerve fiber layer thickness (cpRNFLT) together with normative limits to support clinicians in diagnosing ophthalmic diseases. The impact of age on cpRNFLT is typically implemented by linear models. cpRNFLT is strongly location-specific, whereas previously published norms are typically restricted to coarse sectors and based on small populations. Furthermore, OCT devices neglect impacts of lens or eye size on the diameter of the cpRNFLT scan circle so that the diameter substantially varies over different eyes. We investigate the impact of age and scan diameter reported by Spectralis spectral-domain OCT on cpRNFLT in 5646 subjects with healthy eyes. We provide cpRNFLT by age and diameter at 768 angular locations. Age/diameter were significantly related to cpRNFLT on 89%/92% of the circle, respectively (pointwise linear regression), and to shifts in cpRNFLT peak locations. For subjects from age 42.1 onward but not below, increasing age significantly decreased scan diameter (r=-0.28, p<0.001), which suggests that pathological cpRNFLT thinning over time may be underestimated in elderly compared to younger subjects, as scan diameter decrease correlated with cpRNFLT increase. Our detailed numerical results may help to generate various correction models to improve diagnosing and monitoring optic neuropathies.
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http://dx.doi.org/10.1117/1.JBO.22.12.121718DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5996149PMC
December 2017

Ametropia, retinal anatomy, and OCT abnormality patterns in glaucoma. 2. Impacts of optic nerve head parameters.

J Biomed Opt 2017 Dec;22(12):1-9

Harvard Medical School, Schepens Eye Research Institute, Department of Ophthalmology, Boston, Massac, United States.

Clinicians use retinal nerve fiber layer thickness (RNFLT) measured by optical coherence tomography (OCT) as an adjunct to glaucoma diagnosis. Ametropia is accompanied by changes to the optic nerve head (ONH), which may affect how OCT machines mark RNFLT measurements as abnormal. These changes in abnormality patterns may bias glaucoma diagnosis. Here, we investigate the relationship between OCT abnormality patterns and the following ONH-related and ametropia-associated parameters on 421 eyes of glaucoma patients: optic disc tilt and torsion, central retinal vessel trunk location (CRVTL), and nasal and temporal retinal curvature adjacent to ONH, quantified as nasal/temporal slopes of the inner limiting membrane. We applied multivariate logistic regression with abnormality marks as regressands to 40,401 locations of the peripapillary region and generated spatial maps of locations of false positive/negative abnormality marks independent of glaucoma severity. Effects of torsion and temporal slope were negligible. The effect of tilt could be explained by covariation with ametropia. For CRVTL/nasal slope, abnormality pattern shifts at 7.2%/23.5% of the peripapillary region were detected, respectively, independent of glaucoma severity and ametropia. Therefore, CRVTL and nasal curvature should be included in OCT RNFLT norms. Our spatial location maps may aid clinicians to improve diagnostic accuracy.
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http://dx.doi.org/10.1117/1.JBO.22.12.121714DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5745646PMC
December 2017

Ametropia, retinal anatomy, and OCT abnormality patterns in glaucoma. 1. Impacts of refractive error and interartery angle.

J Biomed Opt 2017 Dec;22(12):1-11

Schepens Eye Research Institute, Department of Ophthalmology, Harvard Medical School, Boston, Massac, United States.

Retinal nerve fiber layer thickness (RNFLT) measured by optical coherence tomography (OCT) is widely used in clinical practice to support glaucoma diagnosis. Clinicians frequently interpret peripapillary RNFLT areas marked as abnormal by OCT machines. However, presently, clinical OCT machines do not take individual retinal anatomy variation into account, and according diagnostic biases have been shown particularly for patients with ametropia. The angle between the two major temporal retinal arteries (interartery angle, IAA) is considered a fundamental retinal ametropia marker. Here, we analyze peripapillary spectral domain OCT RNFLT scans of 691 glaucoma patients and apply multivariate logistic regression to quantitatively compare the diagnostic bias of spherical equivalent (SE) of refractive error and IAA and to identify the precise retinal locations of false-positive/negative abnormality marks. Independent of glaucoma severity (visual field mean deviation), IAA/SE variations biased abnormality marks on OCT RNFLT printouts at 36.7%/22.9% of the peripapillary area, respectively. 17.2% of the biases due to SE are not explained by IAA variation, particularly in inferonasal areas. To conclude, the inclusion of SE and IAA in OCT RNFLT norms would help to increase diagnostic accuracy. Our detailed location maps may help clinicians to reduce diagnostic bias while interpreting retinal OCT scans.
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http://dx.doi.org/10.1117/1.JBO.22.12.121713DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5730847PMC
December 2017

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

Ophthalmology 2018 03 2;125(3):352-360. Epub 2017 Nov 2.

Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts; Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany. Electronic address:

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.
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http://dx.doi.org/10.1016/j.ophtha.2017.09.021DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6706864PMC
March 2018