On the accuracy of measuring rates of visual field change in glaucoma

Jansonius, Nomdo M.

doi:10.1136/bjo.2009.164897

Cited by 68 publications

(75 citation statements)

References 6 publications

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“…[13][14][15] Precision of estimating the rate of MD loss (dB/year) using simple linear regression varies enormously by the length of follow-up. 16 We attempted to control for this by only calculating the rate within a fixed 4-year period (window) from the baseline test (see Figure 1). Each series had to have at least three examinations within this period.…”

Section: Methodsmentioning

confidence: 99%

Are rates of vision loss in patients in English glaucoma clinics slowing down over time? Trends from a decade of data

Boodhna

Saunders

Crabb

2015

Eye

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Purpose To examine changes in rates of visual field (VF) progression in patients attending a sample of glaucoma clinics in England between 1999 and 2012. Methods An archive of 473 252 Humphrey VFs recorded across the UK was retrospectively examined. Distribution of rates recorded in the first half of the decade was compared with the second. The relationship between age and severity of MD loss at baseline with rates of loss and frequency of testing was examined. Results VF series from 18 926 eyes were analysed. Median rate of MD loss for the period before and after 2003 was − 0.11 and − 0.06 dB/year, respectively, but the proportion of eyes with medium or fast rates of MD loss remained constant. Median rate of MD loss in older (470 years) eyes was faster than that observed in younger (o60 years) eyes (−0.21 compared with − 0.01 dB/ year). Median rate of loss did not vary with severity of MD loss at baseline. Frequency of testing, typically carried out annually, did not vary by age, rate of loss or disease severity. Conclusions VFs of eyes treated in the first half of the decade deteriorated more rapidly than those in the second half. Several factors might explain these differences but average effects were small and there was no reduction in the proportion of rapidly progressing eyes over the decade. Older age and, to a lesser extent, worse VF damage at diagnosis are indicators for faster VF loss in clinics, but frequency of VF testing was similar for all patients.

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Section: Methodsmentioning

confidence: 99%

Are rates of vision loss in patients in English glaucoma clinics slowing down over time? Trends from a decade of data

Boodhna

Saunders

Crabb

2015

Eye

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“…[7][8][9] Mitigation of the effects of variability, to accurately detect true disease deterioration, requires frequent VF testing and/or a long period of time. 10,11 The requirement for frequent VF tests over extended periods of time results in delayed identification of vision loss and is a burden to patients and the NHS. 12 The vision loss in glaucoma is a consequence of structural damage to the ONH.…”

Section: Trial Registrationmentioning

confidence: 99%

Combining optical coherence tomography with visual field data to rapidly detect disease progression in glaucoma: a diagnostic accuracy study

Garway-Heath

Zhu

Qian

et al. 2018

Health Technol Assess

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Background Progressive optic nerve damage in glaucoma results in vision loss, quantifiable with visual field (VF) testing. VF measurements are, however, highly variable, making identification of worsening vision (‘progression’) challenging. Glaucomatous optic nerve damage can also be measured with imaging techniques such as optical coherence tomography (OCT). Objective To compare statistical methods that combine VF and OCT data with VF-only methods to establish whether or not these allow (1) more rapid identification of glaucoma progression and (2) shorter or smaller clinical trials. Design Method ‘hit rate’ (related to sensitivity) was evaluated in subsets of the United Kingdom Glaucoma Treatment Study (UKGTS) and specificity was evaluated in 72 stable glaucoma patients who had 11 VF and OCT tests within 3 months (the RAPID data set). The reference progression detection method was based on Guided Progression Analysis™ (GPA) Software (Carl Zeiss Meditec Inc., Dublin, CA, USA). Index methods were based on previously described approaches [Analysis with Non-Stationary Weibull Error Regression and Spatial enhancement (ANSWERS), Permutation analyses Of Pointwise Linear Regression (PoPLR) and structure-guided ANSWERS (sANSWERS)] or newly developed methods based on Permutation Test (PERM), multivariate hierarchical models with multiple imputation for censored values (MaHMIC) and multivariate generalised estimating equations with multiple imputation for censored values (MaGIC). Setting Ten university and general ophthalmology units (UKGTS) and a single university ophthalmology unit (RAPID). Participants UKGTS participants were newly diagnosed glaucoma patients randomised to intraocular pressure-lowering drops or placebo. RAPID participants had glaucomatous VF loss, were on treatment and were clinically stable. Interventions 24-2 VF tests with the Humphrey Field Analyzer and optic nerve imaging with time-domain (TD) Stratus OCT™ (Carl Zeiss Meditec Inc., Dublin, CA, USA). Main outcome measures Criterion hit rate and specificity, time to progression, future VF prediction error, proportion progressing in UKGTS treatment groups, hazard ratios (HRs) and study sample size. Results Criterion specificity was 95% for all tests; the hit rate was 22.2% for GPA, 41.6% for PoPLR, 53.8% for ANSWERS and 61.3% for sANSWERS (all comparisons p ≤ 0.042). Mean survival time (weeks) was 93.6 for GPA, 82.5 for PoPLR, 72.0 for ANSWERS and 69.1 for sANSWERS. The median prediction errors (decibels) when the initial trend was used to predict the final VF were 3.8 (5th to 95th percentile 1.7 to 7.6) for PoPLR, 3.0 (5th to 95th percentile 1.5 to 5.7) for ANSWERS and 2.3 (5th to 95th percentile 1.3 to 4.5) for sANSWERS. HRs were 0.57 [95% confidence interval (CI) 0.34 to 0.90; p = 0.016] for GPA, 0.59 (95% CI 0.42 to 0.83; p = 0.002) for PoPLR, 0.76 (95% CI 0.56 to 1.02; p = 0.065) for ANSWERS and 0.70 (95% CI 0.53 to 0.93; p = 0.012) for sANSWERS. Sample size estimates were not reduced using methods including OCT data. PERM hit rates were between 8.3% and 17.4%. Treatment effects were non-significant in MaHMIC and MaGIC analyses; statistical significance was altered little by incorporating imaging. Limitations TD OCT is less precise than current imaging technology; current OCT technology would likely perform better. The size of the RAPID data set limited the precision of criterion specificity estimates. Conclusions The sANSWERS method combining VF and OCT data had a higher hit rate and identified progression more quickly than the reference and other VF-only methods, and produced more accurate estimates of the progression rate, but did not increase treatment effect statistical significance. Similar studies with current OCT technology need to be undertaken and the statistical methods need refinement. Trial registration Current Controlled Trials ISRCTN96423140. Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 22, No. 4. See the NIHR Journals Library website for further project information. Data analysed in the study were from the UKGTS. Funding for the UKGTS was provided through an unrestricted investigator-initiated research grant from Pfizer Inc. (New York, NY, USA), with supplementary funding from the NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK. Imaging equipment loans were made by Heidelberg Engineering, Carl Zeiss Meditec and Optovue (Fremont, CA, USA). Pfizer, Heidelberg Engineering, Carl Zeiss Meditec and Optovue had no input into the design, conduct, analysis or reporting of any of the UKGTS findings or this work. The sponsor for both the UKGTS and RAPID data collection was Moorfields Eye Hospital NHS Foundation Trust. David F Garway-Heath, Tuan-Anh Ho and Haogang Zhu are partly funded by the NIHR Biomedical Research Centre based at Moorfields Eye Hospital and UCL Institute of Ophthalmology. David F Garway-Heath’s chair at University College London (UCL) is supported by funding from the International Glaucoma Association.

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“…Появившаяся возможность рассчитывать скорость прогрессирования (Rate of Progression, ROP) сделала реальным не только возможность сравнивать эффективность тех или иных методов лечения глаукомы, но и оценивать значимость различных факторов риска, влияющих на развитие ГОН, включая уровень офталь-мотонуса. Уже первые эпидемиологические исследова-ния продемонстрировали, насколько ранее удается за-фиксировать признаки прогрессирования глаукомы с применением GPA по сравнению с использованием, так называемого Trend Analyses, который позволяет опреде-лить факт прогрессирования глаукомы на основании регрессионного анализа вероятности глаукомных изме-нений (Glaucoma Change Probability Analysis) [18,19]. Но самое главное состояло в том, что данную технологию удалось применить в первом серьезном эпидемиологи-ческом исследовании, направленном на решение вопро-са: помогает ли местное гипотензивное лечение [20].…”

Section: Introductionunclassified

Evolution the Concepts of Role of Intraocular Pressure in Glaucoma Progression (Review)

Курышева¹,

Shatalova²

2016

test

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On the accuracy of measuring rates of visual field change in glaucoma

Cited by 68 publications

References 6 publications

Are rates of vision loss in patients in English glaucoma clinics slowing down over time? Trends from a decade of data

Are rates of vision loss in patients in English glaucoma clinics slowing down over time? Trends from a decade of data

Combining optical coherence tomography with visual field data to rapidly detect disease progression in glaucoma: a diagnostic accuracy study

Evolution the Concepts of Role of Intraocular Pressure in Glaucoma Progression (Review)

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