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In this study, Tan et al. examined the variability profile of the newest addition to the SITA strategy—SITA Faster. They performed two SITA-Faster tests per eye in a cohort of 1426 eyes of 787 patients with a mean age of 64.9 ± 12.0 years: 540 eyes with glaucoma, 753 eyes classified as glaucoma suspect and 133 eyes classified as healthy. The mean baseline MD was -1.71 ± 3.2 dB (-3.55 ± 4.0 dB in the glaucoma cohort). The mean global and pointwise variability was reported to be 2.17 ± 1.2 dB and 2.17 ± 2.9 dB respectively.
Most of their results were in line with expectations from previous literature: the pointwise variability was larger for lower sensitives and for more peripheral locations. The variability profile of SITA-Faster was noted to be similar to findings by Heijl et al.1 who compared SITA-Faster, SITA-Fast and SITA-Standard in glaucoma and glaucoma-suspect patients.
Perhaps the most intriguing finding in study is the association (or rather, lack thereof) between higher false-positive (FP) error rates and a more positive test-retest difference in the MD. This resurfaces the age-old question: are reliability indices reliable? Most evidence, including this study, suggests, at best, a weak association between reliability indices and actual reliability of perimetric tests.2,3 Yet, these indices continue to befuddle clinicians and harm patients, whose tests are disregarded as 'unreliable' based on flawed metrics.
This issue is only going to be exacerbated as the tests become quicker. SITA algorithms quantify FPs by using responses captured during time gaps between presentations.4 However, shorter tests mean less time to 'listen' for false responses, leading to inaccurate ‒ and often inflated ‒ estimates of FPs.5-7
Shorter tests mean less time to “listen” for false responses, leading to inaccurate—and often inflated—estimates of FPs
What good are faster, more frequent tests if their results are rendered unusable by arbitrary thresholds unsupported by evidence?
In summary, algorithms like SITA-Faster are proving increasingly useful in saving time and resources for individual tests, with little compromise on reliability compared to older algorithms. Our assessment of such reliability needs to evolve alongside our perimetric strategies.