Progression from ocular hypertension to visual field loss in the English hospital eye service.

BACKGROUND: There are more than one million National Health Service visits in England and Wales each year for patients with glaucoma or ocular hypertension (OHT). With the ageing population and an increase in optometric testing, the economic burden of glaucoma-related visits is predicted to increase. We examined the conversion rates of OHT to primary open-angle glaucoma (POAG) in England and assessed factors associated with risk of conversion. METHODS: Electronic medical records of 45 309 patients from five regionally different glaucoma clinics in England were retrospectively examined. Conversion to POAG from OHT was defined by deterioration in visual field (two consecutive tests classified as stage 1 or worse as per the glaucoma staging system 2). Cox proportional hazards models were used to examine factors (age, sex, treatment status and baseline intraocular pressure (IOP)) associated with conversion. RESULTS: The cumulative risk of conversion to POAG was 17.5% (95% CI 15.4% to 19.6%) at 5 years. Older age (HR 1.35 per decade, 95% CI 1.22 to 1.50, p<0.001) was associated with a higher risk of conversion. IOP-lowering therapy (HR 0.45, 95% CI 0.35 to 0.57, p<0.001) was associated with a lower risk of conversion. Predicted 5-year conversion rates for treated and untreated groups were 14.0% and 26.9%, respectively. CONCLUSION: Less than one-fifth of OHT patients managed in glaucoma clinics in the UK converted to POAG over a 5-year period, suggesting many patients may require less intensive follow-up. Our study provides real-world evidence for the efficacy of current management (including IOP-lowering treatment) at reducing risk of conversion.

Auditing service delivery in glaucoma clinics using visual field records: a feasibility study.

OBJECTIVE: This study aimed to demonstrate that large-scale visual field (VF) data can be extracted from electronic medical records (EMRs) and to assess the feasibility of calculating metrics from these data that could be used to audit aspects of service delivery of glaucoma care. METHOD AND ANALYSIS: Humphrey visual field analyser (HFA) data were extracted from Medisoft EMRs from five regionally different clinics in England in November 2015, resulting in 602 439 records from 73 994 people. Target patients were defined as people in glaucoma clinics with measurable and sustained VF loss in at least one eye (HFA mean deviation (MD) outside normal limits ≥2 VFs). Metrics for VF reliability, stage of VF loss at presentation, speed of MD loss, predicted loss of sight years (bilateral VF impairment) and frequency of VFs were calculated. RESULTS: One-third of people (34.8%) in the EMRs had measurable and repeatable VF loss and were subject to analyses (n=25 760 patients). Median (IQR) age and presenting MD in these patients were 71 (61, 78) years and -6 (-10, -4) dB, respectively. In 19 264 patients with >4 years follow-up, median (IQR) MD loss was -0.2 (-0.8, 0.3) dB/year and median (IQR) intervals between VF examinations was 11 (8, 16) months. Metrics predicting loss of sight years and reliability of examinations varied between centres (p<0.001). CONCLUSION: This study illustrates the feasibility of assessing aspects of health service delivery in glaucoma clinics through analysis of VF databases. Proposed metrics could be useful for blindness prevention from glaucoma in secondary care centres.

Evaluating the Impact of Uveitis on Visual Field Progression Using Large-Scale Real-World Data.

PURPOSE: To compare rates of visual field (VF) loss in uveitis patients with glaucoma against patients with primary open-angle glaucoma (POAG) and explore the association between intraocular pressure (IOP) and rate of VF loss. DESIGN: Retrospective cohort study. METHODS: Anonymized VFs and IOP measurements extracted from the electronic medical records of 5 regionally different glaucoma clinics in England. A total of 205 eyes with diagnosis of uveitis plus glaucoma were compared with 4600 eyes with POAG only. Minimum inclusion criteria were ≥4 visits within a 4-year window. Relative risk (RR) of being a "rapid progressor" (mean deviation [MD] loss ≥1.5 dB/year) was calculated. A mixed-effects model (MEM) and a pointwise VF progression analysis of pattern deviation were used to confirm differences between the groups. Longitudinal IOP mean, range, and variability were compared with rate of VF progression. RESULTS: Median (interquartile range) baseline MD in the uveitis and POAG groups was -3.8 (-8.7, -1.5) dB and -3.1 (-6.6, -1.2) dB, respectively. The uveitis and POAG groups had 23 of 205 (11%) and 331 of 4600 (7%) "rapidly progressing" eyes, respectively. Age-adjusted RR for "rapid progression" in uveitic vs POAG eyes was 1.9 (95% confidence interval: 1.8-2.0). The MEM confirmed that uveitic eyes (-0.49 dB/year) showed higher rates of VF progression than the POAG group (-0.37 dB/year; P < .01). IOP range and variability were higher in the "rapidly progressing" uveitic eyes. CONCLUSIONS: Our analysis suggests that VF loss occurs faster in glaucoma patients with uveitis than those without uveitis. The risk of progressing rapidly in glaucoma with uveitis is almost double than in those without uveitis. Early identification of "rapid progressors" may enable targeted intervention to preserve visual function in this high-risk group.

Does eye examination order for standard automated perimetry matter?

PURPOSE: In spite of faster examination procedures, visual field (VF) results are potentially influenced by fatigue. We use large-scale VF data collected from clinics to test the hypothesis that perimetric fatigue effects are greater in the eye examined second. METHODS: Series of six Humphrey Swedish Interactive Testing Algorithm (SITA) VFs from 6901 patients were retrospectively extracted from a VF database from four different glaucoma clinics. Mean deviation (MD) was compared between first and second tested eyes. A surrogate measure of longitudinal MD variability over time was estimated from errors using linear regression of MD against time then compared between first and second tested eye. RESULTS: Right eye VF was tested consistently first throughout in 6320 (91.6%) patients. Median (interquartile range; IQR) MD in the first tested (right) eye and second tested (left) eye was -2.57 (-6.15, -0.58) dB and -2.70 (-6.34, -0.80) dB respectively (median reduction VF sensitivity of 0.13 dB; p < 0.001). Median (IQR) increase in our surrogate measure of longitudinal MD variability in the second eye tested was 3% (-43%, 50%); this effect was not associated with patient age or rest time between examinations. CONCLUSION: Statistically significant perimetric fatigue effects manifest on average in the second eye tested in routine clinics using Humphrey Field Analyzer SITA examinations. However, the average effects were very small and there was enormous variation among patients. We recommend starting with a right eye examination so that any perimetric fatigue effects, if they exist in an individual, will be as constant as possible from visit to visit.

A Comparison between the Compass Fundus Perimeter and the Humphrey Field Analyzer.

PURPOSE: To evaluate relative diagnostic precision and test-retest variability of 2 devices, the Compass (CMP, CenterVue, Padova, Italy) fundus perimeter and the Humphrey Field Analyzer (HFA, Zeiss, Dublin, CA), in detecting glaucomatous optic neuropathy (GON). DESIGN: Multicenter, cross-sectional, case-control study. PARTICIPANTS: We sequentially enrolled 499 patients with glaucoma and 444 normal subjects to analyze relative precision. A separate group of 44 patients with glaucoma and 54 normal subjects was analyzed to assess test-retest variability. METHODS: One eye of recruited subjects was tested with the index tests: HFA (Swedish interactive thresholding algorithm [SITA] standard strategy) and CMP (Zippy Estimation by Sequential Testing [ZEST] strategy), 24-2 grid. The reference test for GON was specialist evaluation of fundus photographs or OCT, independent of the visual field (VF). For both devices, linear regression was used to calculate the sensitivity decrease with age in the normal group to compute pointwise total deviation (TD) values and mean deviation (MD). We derived 5% and 1% pointwise normative limits. The MD and the total number of TD values below 5% (TD 5%) or 1% (TD 1%) limits per field were used as classifiers. MAIN OUTCOME MEASURES: We used partial receiver operating characteristic (pROC) curves and partial area under the curve (pAUC) to compare the diagnostic precision of the devices. Pointwise mean absolute deviation and Bland-Altman plots for the mean sensitivity (MS) were computed to assess test-retest variability. RESULTS: Retinal sensitivity was generally lower with CMP, with an average mean difference of 1.85±0.06 decibels (dB) (mean ± standard error, P < 0.001) in healthy subjects and 1.46±0.05 dB (mean ± standard error, P < 0.001) in patients with glaucoma. Both devices showed similar discriminative power. The MD metric had marginally better discrimination with CMP (pAUC difference ± standard error, 0.019±0.009, P = 0.035). The 95% limits of agreement for the MS were reduced by 13% in CMP compared with HFA in participants with glaucoma and by 49% in normal participants. Mean absolute deviation was similar, with no significant differences. CONCLUSIONS: Relative diagnostic precision of the 2 devices is equivalent. Test-retest variability of MS for CMP was better than for HFA.

A New Graphical Tool for Assessing Visual Field Progression in Clinical Populations.

PURPOSE: We demonstrate a new approach for assessing and visualizing visual field (VF) progression in clinics. METHODS: Two summary measures for VF progression, Rate of Progression (RP) and Loss of Sight Years (LSY), are combined with a novel visualization (Hedgehog Plots). RP is calculated per eye using linear regression of mean deviation (MD) against time of follow-up. LSY is a novel parameter, linked to actuarial data, which estimates the number of years that a patient will have advanced bilateral VF loss in their predicted remaining lifetime. Every eye is given a rank within the sample based on RP and LSY allowing for "priority" patients to be identified. We illustrate differences between the parameters with an experiment comparing the cases flagged as "priority" by each method using data from 1263 VF records. RESULTS: RP for every eye in a "clinic" can be visualized and assessed using a Hedgehog Plot. Eyes are ranked against all other eyes by RP and LSY; these parameters provide different and complementary information on a patient's VF progression status. A purpose written interactive application demonstrating the techniques is available in the public domain at https://crabblab.shinyapps.io/hedgehog. CONCLUSION: Hedgehog Plots provide a tool for visualizing VF progression in groups of patients and can be used potentially to prioritize monitoring resources. TRANSLATIONAL RELEVANCE: This study illustrates a novel visualization technique and an interactive application that can be used to help determine VF progression in large groups of patients.

Example of monitoring measurements in a virtual eye clinic using 'big data'.

AIM: To assess the equivalence of measurement outcomes between patients attending a standard glaucoma care service, where patients see an ophthalmologist in a face-to-face setting, and a glaucoma monitoring service (GMS). METHODS: The average mean deviation (MD) measurement on the visual field (VF) test for 250 patients attending a GMS were compared with a 'big data' repository of patients attending a standard glaucoma care service (reference database). In addition, the speed of VF progression between GMS patients and reference database patients was compared. Reference database patients were used to create expected outcomes that GMS patients could be compared with. For GMS patients falling outside of the expected limits, further analysis was carried out on the clinical management decisions for these patients. RESULTS: The average MD of patients in the GMS ranged from +1.6dB to -18.9dB between two consecutive appointments at the clinic. In the first analysis, 12 (4.8%; 95% CI 2.5% to 8.2%) GMS patients scored outside the 90% expected values based on the reference database. In the second analysis, 1.9% (95% CI 0.4% to 5.4%) GMS patients had VF changes outside of the expected 90% limits. CONCLUSIONS: Using 'big data' collected in the standard glaucoma care service, we found that patients attending a GMS have equivalent outcomes on the VF test. Our findings provide support for the implementation of virtual healthcare delivery in the hospital eye service.

Gradually Then Suddenly? Decline in Vision-Related Quality of Life as Glaucoma Worsens.

Purpose. To evaluate the relationship between self-reported vision-related quality of life (VRQL) and visual field (VF) loss in people from glaucoma clinics. Methods. A postal survey using the National Eye Institute Visual Function Questionnaire (NEI VFQ-25) was administered to people with a range of VF loss identified from a UK hospital-based glaucoma service database. Trends were assessed in a composite score from NEI VFQ-25 against better-eye mean deviation (BEMD) using linear regression and a spline-fitting method that can highlight where a monotonic relationship may have different stages. Results. A total of 636 patients (median [interquartile range] BEMD -2.1 [-5.2, -0.4] dB, median age 70 [60, 77] years) were analysed. Analysis of trends in the data revealed an average patient loses approximately 2 units (out of 100) on NEI VFQ-25 for every loss of 1 dB (BEMD) as VF defects first become bilateral, up to BEMD -5 dB. NEI VFQ-25 deterioration then appears to slow before a more rapid phase of change (4-5 units per 1 dB loss) after BEMD worsens beyond -15 dB. Conclusions. Relationship between decline in VRQL and VF worsening in glaucoma is unlikely to be linear; it more likely has different phases, and these should be further explored in longitudinal studies.

Bayesian hierarchical modeling of longitudinal glaucomatous visual fields using a two-stage approach.

The Bayesian approach has become increasingly popular because it allows to fit quite complex models to data via Markov chain Monte Carlo sampling. However, it is also recognized nowadays that Markov chain Monte Carlo sampling can become computationally prohibitive when applied to a large data set. We encountered serious computational difficulties when fitting an hierarchical model to longitudinal glaucoma data of patients who participate in an ongoing Dutch study. To overcome this problem, we applied and extended a recently proposed two-stage approach to model these data. Glaucoma is one of the leading causes of blindness in the world. In order to detect deterioration at an early stage, a model for predicting visual fields (VFs) in time is needed. Hence, the true underlying VF progression can be determined, and treatment strategies can then be optimized to prevent further VF loss. Because we were unable to fit these data with the classical one-stage approach upon which the current popular Bayesian software is based, we made use of the two-stage Bayesian approach. The considered hierarchical longitudinal model involves estimating a large number of random effects and deals with censoring and high measurement variability. In addition, we extended the approach with tools for model evaluation. Copyright © 2017 John Wiley & Sons, Ltd.

Global Visit Effects in Point-Wise Longitudinal Modeling of Glaucomatous Visual Fields.

PURPOSE: One of the difficulties in modeling visual field (VF) data is the sometimes large and correlated measurement errors in the point-wise sensitivity estimates. As these errors affect all locations of the same VF, we propose to model them as global visit effects (GVE). We evaluate this model and show the effect it has on progression estimation and prediction. METHODS: Visual field series (24-2 Full Threshold; 15 biannual VFs per patient) of 125 patients with primary glaucoma were included in the analysis. The contribution of the GVE was evaluated by comparing the fitting and predictive ability of a conventional model, which does not contain GVE, to such a model that incorporates the GVE. Moreover, the GVE's effect on the estimated slopes was evaluated by determining the absolute difference between the slopes of the models. Finally, the magnitude of the GVE was compared with that of other measurement errors. RESULTS: The GVE model showed a significant improvement in both the model fit and predictive ability over the conventional model, especially when the number of VFs in a series is limited. The average absolute difference in slopes between the models was 0.13 dB/y. Lastly, the magnitude of the GVE was more than three times larger than the measureable factors combined. CONCLUSIONS: By incorporating the GVE in the longitudinal modeling of VF data, better estimates may be obtained of the rate of progression as well as of predicted future sensitivities.

Optimizing structure-function relationship by maximizing correspondence between glaucomatous visual fields and mathematical retinal nerve fiber models.

PURPOSE: To introduce a method to optimize structural retinal nerve fiber layer (RNFL) models based on glaucomatous visual field data and to show how such an optimized model can be used to reduce noise in visual fields while probably preserving clinically important features. METHODS: Correlation coefficients between age-adjusted deviation values of pairs of visual field test locations were calculated from 103 visual fields of eyes with moderate glaucomatous damage. Distances between those test locations were defined for various parameters of a mathematical RNFL model. Then, the correspondence between the structural and functional data was defined by the spread, or variance, of the correlation coefficients for all distances. The model parameters that minimized this spread constituted the optimized model. To reduce noise in visual fields, the optimized model was used to smooth visual field data according to the RNFL's structure. The resulting fields were compared with visual fields that were smoothed based on the regular testing grid. RESULTS: The optimal parameters for the RNFL model reduced the variance of the correlation coefficients by 78% and were well within the range of parameters previously determined from fundus photographs. Smoothing the visual fields based on the optimized RNFL model strongly reduced noise while keeping important features. CONCLUSIONS: Mathematic RNFL models can be optimized based on visual field data, resulting in a strong structure-function relationship. Taking the RNFL's shape, as defined by such an optimized model, into account when smoothing visual fields results in better noise reduction while preserving important details.

Robust and censored modeling and prediction of progression in glaucomatous visual fields.

PURPOSE: Classic regression is based on certain assumptions that conflict with visual field (VF) data. We investigate and evaluate different regression models and their assumptions in order to determine point-wise VF progression in glaucoma and to better predict future field loss for personalised clinical glaucoma management. METHODS: Standard automated visual fields of 130 patients with primary glaucoma with a minimum of 6 years of follow-up were included. Sensitivity estimates at each VF location were regressed on time with classical linear and exponential regression models, as well as different variants of these models that take into account censoring and allow for robust fits. These models were compared for the best fit and for their predictive ability. The prediction was evaluated at six measurements (approximately 3 years) ahead using varying numbers of measurements. RESULTS: For fitting the data, the classical uncensored linear regression model had the lowest root mean square error and 95th percentile of the absolute errors. These errors were reduced in all models when increasing the number of measurements used for the prediction of future measurements, with the classical uncensored linear regression model having the lowest values for these errors irrespective of how many measurements were included. CONCLUSIONS: All models performed similarly. Despite violation of its assumptions, the classical uncensored linear regression model appeared to provide the best fit for our data. In addition, this model appeared to perform the best when predicting future VFs. However, more advanced regression models exploring any temporal-spatial relationships of glaucomatous progression are needed to reduce prediction errors to clinically meaningful levels.