title: What reduction in standard automated perimetry variability would improve the detection of visual field progression?
abstract: PURPOSE. The test-retest variability of standard automated perimetry (SAP) severely limits its ability to detect sensitivity decline. Numerous improvements in procedures have been proposed, but assessment of their benefits requires quantification of how much variability reduction results in meaningful benefit. This article determines how much reduction in SAP procedure variability is necessary to permit earlier detection of visual field deterioration. METHOD. Computer simulation and statistical analysis were used. Gaussian distributions were fit to the probability of observing any sensitivity measurement obtained with SAP and the Full Threshold algorithm to model current variability. The standard deviation of these Gaussians was systematically reduced to model a reduction of SAP variability. Progression detection ability was assessed by using pointwise linear regression on decreases of -1 and -2 dB/year from 20 and 30 dB, with a custom criteria that fixed detection specificity at 95%. Test visits occurring twice and thrice per annum are modeled, and analysis was performed on single locations and whole fields. RESULTS. A 30% to 60% reduction in SAP variability was required to detect pointwise deterioration 1 year earlier than current methods, depending on progression rate and visit frequency. A reduction of 20% in variability generally allowed progression to be detected one visit earlier. CONCLUSIONS. On average, the variability of SAP procedures must be reduced by approximately 20% for a clinically appreciable improvement in detection of visual field change. Analysis similar to that demonstrated can measure the improvement required of new procedures, assisting in cost-benefit assessment for the adoption of new techniques, before lengthy and expensive clinical trials.
authors.affiliation: Departments of Computer Science and Software Engineering, The University of Melbourne, Victoria, Australia. aturpin@unimelb.edu.au
authors.forename: Andrew
authors.initials: A
authors.lastname: Turpin
authors.forename: Allison M
authors.initials: AM
authors.lastname: McKendrick
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D018450:Disease Progression; D006801:Humans; D011336:Probability; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013997:Time Factors; D014786:Vision Disorders / Q000175:diagnosis* / Q000503:physiopathology; D058609:Visual Field Tests / Q000379:methods*; D014794:Visual Fields / Q000502:physiology*