Faculty Bibliography

PURPOSE/OBJECTIVE:To evaluate the ability of OCT optic nerve head (ONH) and macular parameters to detect disease progression in eyes with advanced structural glaucomatous damage of the circumpapillary retinal nerve fiber layer (cRNFL). DESIGN/METHODS:Longitudinal study. PARTICIPANTS/METHODS:Forty-four eyes from 37 patients with advanced average cRNFL damage (≤60 μm) followed up for an average of 4.0 years. METHODS:All patients were examined with spectral-domain OCT and visual field (VF) assessment during at least 4 visits. MAIN OUTCOME MEASUREMENTS/METHODS:Visual field mean deviation (MD) and VF index. OCT cRNFL (average, superior, and inferior quadrants), ganglion cell-inner plexiform layer (GCIPL) (average, superior, and inferior), rim area, cup volume, average cup-to-disc (C:D) ratio, and vertical C:D ratio. RESULTS:/year). CONCLUSIONS:Macula GCIPL and ONH parameters may be useful in tracking progression in patients with advanced glaucoma.

PURPOSE/OBJECTIVE:Previously, we introduced a state-based 2-dimensional continuous-time hidden Markov model (2D CT HMM) to model the pattern of detected glaucoma changes using structural and functional information simultaneously. The purpose of this study was to evaluate the detected glaucoma change prediction performance of the model in a real clinical setting using a retrospective longitudinal dataset. DESIGN/METHODS:Longitudinal, retrospective study. PARTICIPANTS/METHODS:One hundred thirty-four eyes from 134 participants diagnosed with glaucoma or as glaucoma suspects (average follow-up, 4.4±1.2 years; average number of visits, 7.1±1.8). METHODS:A 2D CT HMM model was trained using OCT (Cirrus HD-OCT; Zeiss, Dublin, CA) average circumpapillary retinal nerve fiber layer (cRNFL) thickness and visual field index (VFI) or mean deviation (MD; Humphrey Field Analyzer; Zeiss). The model was trained using a subset of the data (107 of 134 eyes [80%]) including all visits except for the last visit, which was used to test the prediction performance (training set). Additionally, the remaining 27 eyes were used for secondary performance testing as an independent group (validation set). The 2D CT HMM predicts 1 of 4 possible detected state changes based on 1 input state. MAIN OUTCOME MEASURES/METHODS:Prediction accuracy was assessed as the percentage of correct prediction against the patient's actual recorded state. In addition, deviations of the predicted long-term detected change paths from the actual detected change paths were measured. RESULTS:Baseline mean ± standard deviation age was 61.9±11.4 years, VFI was 90.7±17.4, MD was -3.50±6.04 dB, and cRNFL thickness was 74.9±12.2 μm. The accuracy of detected glaucoma change prediction using the training set was comparable with the validation set (57.0% and 68.0%, respectively). Prediction deviation from the actual detected change path showed stability throughout patient follow-up. CONCLUSIONS:The 2D CT HMM demonstrated promising prediction performance in detecting glaucoma change performance in a simulated clinical setting using an independent cohort. The 2D CT HMM allows information from just 1 visit to predict at least 5 subsequent visits with similar performance.

Glaucoma is the second leading cause of blindness world-wide. Despite active research efforts driven by the importance of diagnosis and treatment of the optic degenerative neuropathy, the relationship between structural and functional changes along the glaucomateous evolution are still not clearly understood. Dynamic changes of the lamina cribrosa (LC) in the presence of intraocular pressure (IOP) were suggested to play a significant role in optic nerve damage, which motivates the proposed research to explore the relationship of changes of the 3D structure of the LC collagen meshwork to clinical diagnosis. We introduce a framework to quantify 3D dynamic morphological changes of the LC under acute IOP changes in a series of swept-source optical coherence tomography (SS-OCT) scans taken under different pressure states. Analysis of SS-OCT images faces challenges due to low signal-to-noise ratio, anisotropic resolution, and observation variability caused by subject and ocular motions. We adapt unbiased diffeomorphic atlas building which serves multiple purposes critical for this analysis. Analysis of deformation fields yields desired global and local information on pressure-induced geometric changes. Deformation variability, estimated with repeated images of a healthy volunteer without IOP elevation, is found to be a magnitude smaller than pressure-induced changes and thus illustrates feasibility of the proposed framework. Results in a clinical study with healthy, glaucoma suspect, and glaucoma subjects demonstrate the potential of the proposed method for non-invasive in vivo analysis of LC dynamics, potentially leading to early prediction and diagnosis of glaucoma.

Purpose/UNASSIGNED:To determine if inner retinal layer reflectivity in eyes with acute central retinal vein occlusion (CRVO) correlates with visual acuity at 12 months. Methods/UNASSIGNED:Macular optical coherence tomography (OCT) scans were obtained from 22 eyes of 22 patients with acute CRVO. Optical intensity ratios (OIRs), defined as the mean OCT reflectivity of the inner retinal layers normalized to the mean reflectivity of the RPE, were measured from the presenting and 1-month OCT image by both manual measurements of grayscale B-scans and custom algorithmic measurement of raw OCT volume data. OIRs were assessed for association with final visual outcome. Cohort subgroup division for analysis was determined statistically. Results/UNASSIGNED:Eyes with poorer final visual acuity (≥20/70) at 1 year were more likely to have a higher ganglion cell layer OIR than eyes with better final visual acuity (<20/70) at 1 month (manually: 0.591 to 0.735, P = 0.006, algorithmically: 0.663 to 0.799, P = 0.014). At 1 month, eyes with a poorer final visual acuity demonstrated a higher variance of OIR measurements (algorithmically: 0.087 vs. 0.160, P = 0.002) per scan than eyes with better final visual acuity. Conclusions/UNASSIGNED:In acute CRVO, ganglion cell layer changes at 1 month, including increased reflectivity and increased heterogeneity of reflectivity signal as expressed as OIR and OIR variance, were associated with a poorer visual prognosis at 1 year. Technique calibration with larger sample sizes and automated integration into OCT platforms will be necessary to determine if OIR can be a clinically useful prognostic tool.

Purpose : Ganglion cell-inner plexiform layer (GCIPL) and retinal nerve fiber layer (RNFL) thicknesses measured by OCT have been shown to be useful for glaucoma diagnosis and progression detection. The purpose of this study was to determine the disease severity threshold (tipping point) at which the longitudinal rate of change for RNFL and GCIPL thickness slows down; allowing to determine the preferred location to follow structural damage along the spectrum of the disease severity. Methods : Subjects with comprehensive ophthalmic examination and >= 5 visits with qualified visual field (VF; Humphrey Field Analyzer; Zeiss, Dublin, CA) and OCT (Cirrus HDOCT; Zeiss) optic nerve head and macular scans were enrolled. Piece-wise linear mixed effects model was used to identify the tipping points of RNFL and GCIPL vs. VF mean deviation (MD), respectively, and model with quadratic term for RNFL vs. GCIPL. To account for the difference in dynamic ranges, RNFL and GCIPL rate of change were standardized (normal distribution, mean=0 sd=1). Results : 177 eyes (125 open angle glaucoma, 45 glaucoma suspect, and 7 healthy eyes) of 114 subjects were analyzed. Subjects' mean age: 70 +/- 11 years, median VF MD: -1.78 dB ([Q1, Q3]; -5.8, -0.2), baseline average RNFL and GCIPL thicknesses: 73.2 +/- 14.9mum and 68.3 +/- 10.7mum, respectively, average follow-up time: 3.4 +/- 1 years, mean visits per subject: 5.6, with a grand total of 1,010 follow-up visits. Tipping point for RNFL occur at earlier stage of the disease and the rate of change is faster than for GCIPL (Table). However, after standardization, the slopes for RNFL and GCIPL were similar. When comparing the rate of change for RNFL and GCIPL, no tipping point was detected, but as RNFL and GCIPL decreased, the rate of change in RNFL became significantly smaller. Conclusions : Rate of RNFL thinning slows down at an earlier stage of functional damage than GCIPL. Different dynamic ranges give the impression that RNFL decreases faster, but accounting for the larger dynamic range, a similar rate of change to GCIPL is observed across the entire disease severity spectrum. Our results do not indicate whether RNFL or GCIPL is better for detecting progression except for very advanced stages of the disease where RNFL progression rate stalls

Purpose : We previously described the two-dimensional continuous time hidden Markov model (2D CT-HMM) to model glaucoma progression using structural and functional measurements simultaneously. The purpose of this study was to validate the glaucoma progression prediction performance of a previously trained model on data collected from a different cohort. Methods : A 2D CT-HMM was trained using optical coherence tomography (OCT; Cirrus HD-OCT, Zeiss, Dublin, CA) mean circumpapillary retinal nerve fiber layer (cRNFL) thickness and visual field index (VFI; Humphrey Field Analyzer, Zeiss) obtained from 107 eyes of 107 subjects, including glaucoma and glaucoma suspect. Average observation period was 4.2 years (7.1 visits). Approximately 1 year of longitudinal data were collected from a separate cohort. 78 eyes of 39 subjects, glaucoma and glaucoma suspect, with an average of 2.2 +/- 0.4 visits were included. After matching the distribution of OCT and VF data on the training cohort, 19 eyes from 14 subjects were selected. The previously trained model was tested on these cases. One visit was used as an input to the model to predict the state at the next visit at least 6 months later, with 4 possible state changes (stable, OCT, VF, or OCT+VF progression). The percentage of correct prediction against the actual recorded state was reported as the prediction accuracy. Results : Baseline age of the test cohort was 58.4 +/- 13.9 years, VFI 93.6 +/- 8.3, mean cRNFL thickness 74.0 +/- 10.9mum. Figure 1 shows the trained model. The size of the circle (state) shows the number of subjects passing through the state. The grayscale of the state indicates the length of time spent there, increasing white to black. Lines indicate state changes, with the blue line being the most likely. This information is also shown in numerical form. The inset shows an example of model use. The calculated prediction accuracy of this pre-trained 2D CT-HMM on test data was 52.6%. Conclusions : Although the glaucoma progression prediction performance of the trained 2D CT-HMM was slightly lower than that previously reported, it is acceptable given the training and testing cohorts were different, and it exceeds the random chance of making a correct prediction, 25%. Furthermore, unlike conventional methods, this model requires only one visit as an input, which makes it a potentially useful tool in the clinical prediction of glaucoma progression. (Figure Presented)

Purpose: When assessing treatment effects in observational studies, the propensity score (PS) method is commonly used to reduce the selection bias of treatments. Weighting subjects by the inverse probability of treatment using the PS mimics treatment s ran e domization e Our y c o ntin as to g apply PS t r website, you are ag ation o g glaucoma treatment c cmeepdtication with rates of structural changes in a longitudinal cohort of glaucoma subjects.
Method(s): Glaucoma subjects treated with prostaglandin, beta blockers, and/or carbonic anhydrase inhibitors (CAIs) with > 2 visits with qualified OCT (Cirrus HD-OCT; Zeiss) were included. Subjects were on medication for at least 3 months prior to each OCT visit. Multinomial PS for baseline medication selection was estimated by baseline age, visual field (VF) mean deviation (MD), intraocular pressure and ethnicity. Rates of change for OCT's average circumpapillary retinal nerve fiber layer (RNFL) and macular ganglion cell-inner plexiform layer (GCIPL) thicknesses were calculated per eye using linear regression. Their associations with baseline RNFL, GCIPL, baseline medication, post-baseline medication and medication duration were tested using linear regression with and without PS weighting.
Result(s): 207 eyes (117 subjects) were qualified with average age of 62.2+/-12.7 years and median MD of -3.6 dB (IQR -9.0, -1.4) at baseline, and a mean follow-up of 3.2+/-1.8 years. The average duration of treatment range from 1.3+/-1.8 to 2.4+/-2.5 years for CAIs and prostaglandin, respectively. At baseline, average RNFL and GCIPL were 71.5+/-14.4 mum and 65.9+/-13.5 mum. Throughout follow-up, mean rate of change for RNFL and GCIPL were -0.30+/-2.60 mum/year and 0.27+/-7.72 mum/year. Without PS weighting, no medication effect was shown to be associated with either rate of change. With PS weighting, however, the rate of change for RNFL was significantly associated with taking CAIs (-1.26 mum/year, p=0.029) and prostaglandin (-0.98 mum/year, p=0.044) and baseline RNFL (-0.05 mum/year, p=0.017). Longer use of the medications slowed RNFL decrease, although the effects were not statistically significant. No association was detected between treatment and rate of change for GCIPL.
Conclusion(s): PS can be useful to reduce treatment selection bias and facilitate more rigorous estimation of medication effects in observational glaucoma research