The pseudotemporal bootstrap for predicting glaucoma from cross-sectional visual field data

Progressive loss of the field of vision is characteristic of a number of eye diseases such as glaucoma, a leading cause of irreversible blindness in the world. Recently, there has been an explosion in the amount of data being stored on patients who suffer from visual deterioration, including visual field (VF) test, retinal image, and frequent intraocular pressure measurements. Like the progression of many biological and medical processes, VF progression is inherently temporal in nature. However, many datasets associated with the study of such processes are often cross sectional and the time dimension is not measured due to the expensive nature of such studies. In this paper, we address this issue by developing a method to build artificial time series, which we call pseudo time series from cross-sectional data. This involves building trajectories through all of the data that can then, in turn, be used to build temporal models for forecasting (which would otherwise be impossible without longitudinal data). Glaucoma, like many diseases, is a family of conditions and it is, therefore, likely that there will be a number of key trajectories that are important in understanding the disease. In order to deal with such situations, we extend the idea of pseudo time series by using resampling techniques to build multiple sequences prior to model building. This approach naturally handles outliers and multiple possible disease trajectories. We demonstrate some key properties of our approach on synthetic data and present very promising results on VF data for predicting glaucoma

Correlation of structural and functional measurements in primary open angle glaucoma (optic disc morphology and psychophysics)

Background: Primary open angle glaucoma (POAG) is the term given to a progressive optic neuropathy for which the major risk factors are raised intraocular pressure and older age. The presence of glaucoma is defined by functional (visual field) defects that are associated with loss of retinal ganglion cells and neuroretinal tissue at the optic nerve head (ONH). The relationship between the functional and structural changes is, therefore, of great importance to the understanding of the disease process, and to the clinician's interpretation of the state of the disease. This thesis sets out to define the relationship between retinal function, as measured by conventional white-on-white perimetry, and optic nerve head structure, as measured by scanning laser ophthalmoscopy. Plan of research: The investigations are divided into four parts. Firstly, the ONH structural measurements that best distinguish glaucomatous from normal eyes are determined. This includes an analysis of the relationship between the optical components of the eye and image magnification. Secondly, an analysis of the physiological relationship between ganglion cell numbers and retinal function. Thirdly, the establishment of the anatomical relationship between visual field locations and the ONH (a map relating the visual field to the ONH). And fourthly, the investigation of the correlation between structural and functional measurements in POAG. Results: Neuroretinal rim area in relation to optic disc size is the best parameter to distinguish glaucomatous from normal eyes. The physiological relationship of ganglion cell numbers to decibel light sensitivity (10*log[1/light intensity]) is curvilinear and to light sensitivity (1/light intensity) is linear. The visual field/ONH map allows a correlation of sectoral ONH and regional visual field sensitivity. Analyses demonstrate that the relationship of neuroretinal rim area to decibel light sensitivity is curvilinear in glaucoma. Clinical significance: The curvilinear relationship between decibel light sensitivity and neuroretinal rim area indicates that staging of glaucoma by decibel summary indices may underestimate the amount of structural damage in early disease. In addition, the analysis of disease progression by linear modelling of decibel light sensitivity over time may need re-evaluation

Deriving visual field loss based upon OCT of inner retinal thicknesses of the macula

Using a multiple linear regression method, a derived visual field (VF) was obtained from retinal ganglion cell and retinal nerve fiber layer (RNFL) thicknesses measured with frequency-domain, optical coherence tomography (OCT) macular scans. 138 eyes from 92 glaucoma patients or suspects and 58 healthy eyes were included. The derived VF was compared to the VF measured with standard automated perimetry (SAP). The median agreement between the derived and observed VFs was 90%. As the derived and observed VFs should be independent, they can be combined to potentially increase the sensitivity/specificity of a test for glaucoma

A Spatio-Temporal Bayesian Network Classifier for Understanding Visual Field Deterioration

Progressive loss of the field of vision is characteristic of a number of eye diseases such as glaucoma which is a leading cause of irreversible blindness in the world. Recently, there has been an explosion in the amount of data being stored on patients who suffer from visual deterioration including field test data, retinal image data and patient demographic data. However, there has been relatively little work in modelling the spatial and temporal relationships common to such data. In this paper we introduce a novel method for classifying Visual Field (VF) data that explicitly models these spatial and temporal relationships. We carry out an analysis of this method and compare it to a number of classifiers from the machine learning and statistical communities. Results are very encouraging showing that our classifiers are comparable to existing statistical models whilst also facilitating the understanding of underlying spatial and temporal relationships within VF data. The results reveal the potential of using such models for knowledge discovery within ophthalmic databases, such as networks reflecting the ‘nasal step’, an early indicator of the onset of glaucoma. The results outlined in this paper pave the way for a substantial program of study involving many other spatial and temporal datasets, including retinal image and clinical data

Intervals between visual field tests when monitoring the glaucomatous patient: wait-and-see approach

Purpose.: Published recommendations suggest three visual field (VF) tests per year are required to identify rapid progression in a newly diagnosed glaucomatous patient over 2 years. This report aims to determine if identification of progression would be improved by clustering tests at the beginning and end of the 2-year period. Methods.: Computer-simulated “patients” were given a rapid VF (mean deviation [MD]) loss of −2 dB/year with added MD measurement variability. Linear regression of MD against time was used to estimate progression. One group of “patients” was measured every 6 months, another every 4 months, whereas the wait-and-see group were measured either 2 or 3 times at both baseline and at the end of a 2-year period. Stable “patients” (0 dB/year) were generated to examine the effect of the follow-up patterns on false-positive (FP) progression identification. Results.: By 2 years, 58% and 82% of rapidly progressing patients were correctly detected using evenly spaced 6- and 4-month VFs, respectively. This power of detection significantly improved to 62% and 95% with the wait-and-see approach (P < 0.001). When compared with evenly spaced VFs, the rate of MD loss was better estimated by the wait-and-see approach, but average detection time was slightly slower. Evenly spaced testing incurred a significantly higher FP rate: up to 5.9% compared with only 0.4% in wait-and-see (P < 0.001). Conclusions.: Compared with an evenly spaced follow-up, wait-and-see identifies more “patients” with rapid VF progression with fewer FPs, making it particularly applicable to clinical trials. Modeling experiments, as reported here, are useful for investigating and optimizing follow-up schemes

Improving statistical power of glaucoma clinical trials using an ensemble of cyclical generative adversarial networks

Albeit spectral-domain OCT (SDOCT) is now in clinical use for glaucoma management, published clinical trials relied on time-domain OCT (TDOCT) which is characterized by low signal-to-noise ratio, leading to low statistical power. For this reason, such trials require large numbers of patients observed over long intervals and become more costly. We propose a probabilistic ensemble model and a cycle-consistent perceptual loss for improving the statistical power of trials utilizing TDOCT. TDOCT are converted to synthesized SDOCT and segmented via Bayesian fusion of an ensemble of GANs. The final retinal nerve fibre layer segmentation is obtained automatically on an averaged synthesized image using label fusion. We benchmark different networks using i) GAN, ii) Wasserstein GAN (WGAN) (iii) GAN + perceptual loss and iv) WGAN + perceptual loss. For training and validation, an independent dataset is used, while testing is performed on the UK Glaucoma Treatment Study (UKGTS), i.e. a TDOCT-based trial. We quantify the statistical power of the measurements obtained with our method, as compared with those derived from the original TDOCT. The results provide new insights into the UKGTS, showing a significantly better separation between treatment arms, while improving the statistical power of TDOCT on par with visual field measurements

Are Patient Self-Reported Outcome Measures Sensitive Enough to Be Used as End Points in Clinical Trials? Evidence from the United Kingdom Glaucoma Treatment Study

PURPOSE: The United Kingdom Glaucoma Treatment Study (UKGTS) demonstrated the effectiveness of an intraocular pressure-lowering drug in patients with glaucoma using visual field progression as a primary outcome. The present study tested the hypothesis that responses on patient-reported outcome measures (PROMs; secondary outcome measure) differ between patients receiving a topical prostaglandin analog (latanoprost) or placebo eye drops in UKGTS. DESIGN: Multicenter, randomized, triple-masked, placebo-controlled trial. PARTICIPANTS: Newly diagnosed glaucoma patients in the UKGTS with baseline and exit PROMs (n = 182 and n = 168 patients from the treatment and placebo groups, respectively). METHODS: In the UKGTS (trial registration number, ISRCTN96423140), patients with open-angle glaucoma were allocated to receive latanoprost (treatment) or placebo; the observation period was 24 months. Patients completed general health PROMs (European Quality of Life in 5 Dimensions [EQ-5D] and 36-item Short Form [SF-36]) and PROMs specific to glaucoma (15-item Glaucoma Quality of Life [GQL-15] and 9-item Glaucoma Activity Limitation [GAL-9]) at baseline and exit from the trial. Percentage changes between measurement on PROMs were calculated for each patient and compared between treatment arms. In addition, differences between stable patients (n = 272) and those with glaucomatous progression (n = 78), as determined by visual field change (primary outcome), were assessed. MAIN OUTCOME MEASURE: PROMs on health-related and vision-related quality of life. RESULTS: Average percentage change on PROMs was similar for patients in both arms of the trial, with no statistically significant differences between treatment and placebo groups (EQ-5D, P = 0.98; EQ-5D visual analog scale, P = 0.88; SF-36, P = 0.94, GQL-15, P = 0.66; GAL-9, P = 0.87). There were statistically significant differences between stable and progressing patients on glaucoma-specific PROMs (GQL-15, P = 0.02; GAL-9, P = 0.02), but not on general health PROMs (EQ-5D, P = 0.62; EQ-5D visual analog scale, P = 0.23; SF-36, P = 0.65). CONCLUSIONS: Average change in PROMs on health-related and vision-related quality of life was similar for the treatment and placebo groups in the UKGTS. The PROMs used may not be sensitive enough to function as primary end points in clinical trials when participants have newly diagnosed early-stage glaucoma

Spatiotemporal Summation of Perimetric Stimuli in Early Glaucoma

PURPOSE: To investigate achromatic temporal summation under the conditions of standard automated perimetry (SAP), using a Goldmann III (GIII) stimulus and a stimulus scaled to the local area of complete spatial summation (Ricco's area) in open-angle glaucoma (OAG) patients and healthy age-similar control participants. METHODS: Twenty patients with OAG (mean age, 63 years; mean MD, -3.3 dB) and 15 healthy controls (mean age, 64 years) were recruited. Contrast thresholds were measured for seven stimulus durations (1-24 frames, 1.8-191.9 ms) using a near-GIII stimulus (0.48° diameter) and stimuli scaled to the local Ricco's area, in four oblique meridians at 8.8° eccentricity in the visual field. The upper limit of complete temporal summation (critical duration) was estimated using iterative two-phase regression analysis. RESULTS: Median critical duration values were significantly longer (P < 0.05) in the OAG group for the near-GIII (107.2 ms; interquartile range [IQR], 38.0-190.5) and Ricco's area-scaled (83.2 ms, 41.7-151.4) stimuli, compared to those in healthy subjects (near-GIII, 34.7 ms; 18.2-47.9; Ricco's area-scaled, 49.0 ms; 25.1-64.6). The greatest difference in contrast thresholds between healthy and OAG subjects (i.e., disease signal) was found when stimuli were scaled to Ricco's area and shorter than or equal to the critical duration in healthy observers. CONCLUSIONS: Temporal summation is altered in glaucoma. The stimulus duration and area of conventional SAP may be suboptimal for identifying early functional damage. Simultaneously modulating stimulus duration, area, and luminance during the examination may improve the diagnostic capability of SAP and expand the dynamic range of current instruments

Improving the Accuracy and Speed of Visual Field Testing in Glaucoma With Structural Information and Deep Learning

Purpose: To assess the performance of a perimetric strategy using structure–function predictions from a deep learning (DL) model. Methods: Visual field test–retest data from 146 eyes (75 patients) with glaucoma with (median [5th–95th percentile]) 10 [7, 10] tests per eye were used. Structure–function predictions were generated with a previously described DL model using cicumpapillary optical coherence tomography (OCT) scans. Structurally informed prior distributions were built grouping the observed measured sensitivities for each predicted value and recalculated for each subject with a leave-one-out approach. A zippy estimation by sequential testing (ZEST) strategy was used for the simulations (1000 per eye). Groundtruth sensitivities for each eye were the medians of the test–retest values. Two variations of ZEST were compared in terms of speed (average total number of presentations [NP] per eye) and accuracy (average mean absolute error [MAE] per eye), using either a combination of normal and abnormal thresholds (ZEST) or the calculated structural distributions (S-ZEST) as prior information. Two additional versions of these strategies employing spatial correlations were tested. Results: S-ZEST was significantly faster, with a mean average NP of 213.87 (SD = 28.18), than ZEST, with a mean average NP of 255.65 (SD = 50.27) (P < 0.001). The average MAE was smaller for S-ZEST (1.98; SD = 2.37) than ZEST (2.43; SD = 2.69) (P < 0.001). Spatial correlations further improved both strategies (P < 0.001), but the differences between ZEST and S-ZEST remained significant (P < 0.001). Conclusions: DL structure–function predictions can significantly improve perimetric tests. Translational Relevance: DL structure–function predictions from clinically available OCT scans can improve perimetry in glaucoma patients