Abstract
PURPOSE: Test-retest variability in perimetry consists of short-term and long-term components, both of which impede assessment of progression. By minimizing and quantifying the algorithm-dependent short-term variability, we can quantify the algorithm-independent long-term variability that reflects true fluctuations in sensitivity between visits. We do this at locations with sensitivity both < 28 dB (when the stimulus is smaller than Ricco's area and complete spatial summation can be assumed) and > 28 dB (when partial summation occurs). METHODS: Frequency-of-seeing curves were measured at four locations of 35 participants with glaucoma. The standard deviation of cumulative Gaussian fits to those curves was modeled for a given sensitivity and used to simulate the expected short-term variability of a 30-presentation algorithm. A separate group of 137 participants was tested twice with that algorithm, 6 months apart. Long-term variance at different sensitivities was calculated as the LOESS fit of observed test-retest variance minus the LOESS fit of simulated short-term variance. RESULTS: Below 28 dB, short-term variability increased approximately linearly with increasing loss. Long-term variability also increased with damage below this point, attaining a maximum standard deviation of 2.4 dB at sensitivity 21 dB, before decreasing due to the floor effect of the algorithm. Above 30 dB, the observed test-retest variance was slightly smaller than the simulated short-term variance. CONCLUSIONS: Long-term and short-term variability both increase with damage for perimetric stimuli smaller than Ricco's area. Above 28 dB, long-term variability constitutes a negligible proportion of test-retest variability. TRANSLATIONAL RELEVANCE: Fluctuations in true sensitivity increase in glaucoma, even after accounting for increased short-term variability. This long-term variability cannot be reduced by altering testing algorithms alone.