Development of a Visual Field Simulation Model of Longitudinal Point-Wise Sensitivity Changes From a Clinical Glaucoma Cohort

Overview

Journal Transl Vis Sci Technol

Specialties Biomedical Engineering
Ophthalmology

Date 2018 Jun 28

PMID 29946496

Citations 16

Authors

Zhichao Wu

Felipe A Medeiros

Affiliations

Soon will be listed here.

Abstract

Purpose: To develop a new visual field simulation model that can recreate real-world longitudinal results at a point-wise level from a clinical glaucoma cohort.

Methods: A cohort of 367 glaucoma eyes from 265 participants seen over 10.1 ± 2.5 years were included to obtain estimates of "true" longitudinal visual field point-wise sensitivity and estimates of measurement variability. These two components were then combined to reconstruct visual field results in a manner that accounted for correlated measurement error. To determine how accurately the simulated results reflected the clinical cohort, longitudinal variability estimates of mean deviation (MD) were determined by calculating the SD of the residuals from linear regression models fitted to the MD values over time for each eye in the simulated and clinical cohorts. The new model was compared to a previous model that does not account for spatially correlated errors.

Results: The SD of all the residuals for the clinical and simulated cohorts was 1.1 dB (95% confidence interval [CI]: 1.1-1.2 dB) and 1.1 dB (95% CI: 1.1-1.1 dB), respectively, whereas it was 0.4 dB (95% CI: 0.4-0.4 dB) using the previous simulation model that did not account for correlated errors.

Conclusions: A new simulation model accounting for correlated measurement errors between visual field locations performed better than a previous model in estimating visual field variability in glaucoma.

Translational Relevance: This model can provide a powerful framework to better understand use of visual field testing in clinical practice and trials and to evaluate new methods for detecting progression.

Citing Articles

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References

Zhu H, Russell R, Saunders L, Ceccon S, Garway-Heath D, Crabb D . Detecting changes in retinal function: Analysis with Non-Stationary Weibull Error Regression and Spatial enhancement (ANSWERS). PLoS One. 2014; 9(1):e85654. PMC: 3894992. DOI: 10.1371/journal.pone.0085654. View

Zhu H, Crabb D, Ho T, Garway-Heath D . More Accurate Modeling of Visual Field Progression in Glaucoma: ANSWERS. Invest Ophthalmol Vis Sci. 2015; 56(10):6077-83. DOI: 10.1167/iovs.15-16957. View

McKendrick A, Turpin A . Combining perimetric suprathreshold and threshold procedures to reduce measurement variability in areas of visual field loss. Optom Vis Sci. 2005; 82(1):43-51. View

Medeiros F, Vizzeri G, Zangwill L, Alencar L, Sample P, Weinreb R . Comparison of retinal nerve fiber layer and optic disc imaging for diagnosing glaucoma in patients suspected of having the disease. Ophthalmology. 2008; 115(8):1340-6. PMC: 2832850. DOI: 10.1016/j.ophtha.2007.11.008. View

Yousefi S, Balasubramanian M, Goldbaum M, Medeiros F, Zangwill L, Weinreb R . Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields. Transl Vis Sci Technol. 2016; 5(3):2. PMC: 4855479. DOI: 10.1167/tvst.5.3.2. View

De Moraes C, Liebmann J, Levin L . Detection and measurement of clinically meaningful visual field progression in clinical trials for glaucoma. Prog Retin Eye Res. 2016; 56:107-147. PMC: 5313392. DOI: 10.1016/j.preteyeres.2016.10.001. View

Quigley H . Clinical trials for glaucoma neuroprotection are not impossible. Curr Opin Ophthalmol. 2012; 23(2):144-54. DOI: 10.1097/ICU.0b013e32834ff490. View

Gardiner S, Demirel S . Detecting Change Using Standard Global Perimetric Indices in Glaucoma. Am J Ophthalmol. 2017; 176:148-156. PMC: 5376527. DOI: 10.1016/j.ajo.2017.01.013. View

Artes P, OLeary N, Hutchison D, Heckler L, Sharpe G, Nicolela M . Properties of the statpac visual field index. Invest Ophthalmol Vis Sci. 2011; 52(7):4030-8. DOI: 10.1167/iovs.10-6905. View

10.

Turpin A, McKendrick A, Johnson C, Vingrys A . Development of efficient threshold strategies for frequency doubling technology perimetry using computer simulation. Invest Ophthalmol Vis Sci. 2002; 43(2):322-31. View

11.

Rubinstein N, McKendrick A, Turpin A . Incorporating Spatial Models in Visual Field Test Procedures. Transl Vis Sci Technol. 2016; 5(2):7. PMC: 4790418. DOI: 10.1167/tvst.5.2.7. View

12.

Medeiros F . Biomarkers and Surrogate Endpoints: Lessons Learned From Glaucoma. Invest Ophthalmol Vis Sci. 2017; 58(6):BIO20-BIO26. PMC: 5455347. DOI: 10.1167/iovs.17-21987. View

13.

Saunders L, Medeiros F, Weinreb R, Zangwill L . What rates of glaucoma progression are clinically significant?. Expert Rev Ophthalmol. 2018; 11(3):227-234. PMC: 5898440. DOI: 10.1080/17469899.2016.1180246. View

14.

Ballae Ganeshrao S, McKendrick A, Denniss J, Turpin A . A perimetric test procedure that uses structural information. Optom Vis Sci. 2014; 92(1):70-82. DOI: 10.1097/OPX.0000000000000447. View

15.

Bengtsson B, Olsson J, Heijl A, Rootzen H . A new generation of algorithms for computerized threshold perimetry, SITA. Acta Ophthalmol Scand. 1997; 75(4):368-75. DOI: 10.1111/j.1600-0420.1997.tb00392.x. View

16.

Wu Z, Saunders L, Daga F, Diniz-Filho A, Medeiros F . Frequency of Testing to Detect Visual Field Progression Derived Using a Longitudinal Cohort of Glaucoma Patients. Ophthalmology. 2017; 124(6):786-792. DOI: 10.1016/j.ophtha.2017.01.027. View

17.

Odberg T, Jakobsen J, Hultgren S, Halseide R . The impact of glaucoma on the quality of life of patients in Norway. I. Results from a self-administered questionnaire. Acta Ophthalmol Scand. 2001; 79(2):116-20. DOI: 10.1034/j.1600-0420.2001.079002116.x. View

18.

Turpin A, McKendrick A, Johnson C, Vingrys A . Properties of perimetric threshold estimates from full threshold, ZEST, and SITA-like strategies, as determined by computer simulation. Invest Ophthalmol Vis Sci. 2003; 44(11):4787-95. DOI: 10.1167/iovs.03-0023. View

19.

King-Smith P, Grigsby S, Vingrys A, Benes S, Supowit A . Efficient and unbiased modifications of the QUEST threshold method: theory, simulations, experimental evaluation and practical implementation. Vision Res. 1994; 34(7):885-912. DOI: 10.1016/0042-6989(94)90039-6. View

20.

Denniss J, McKendrick A, Turpin A . Towards Patient-Tailored Perimetry: Automated Perimetry Can Be Improved by Seeding Procedures With Patient-Specific Structural Information. Transl Vis Sci Technol. 2013; 2(4):3. PMC: 3763896. DOI: 10.1167/tvst.2.4.3. View