Detecting Change Using Standard Global Perimetric Indices in Glaucoma

Abstract

Purpose Various global indices are available to summarize results from standard automated perimetry. This study asks which index can detect significant deterioration earliest, for a fixed specificity. Design Comparison of prognostic indices. Methods Two cohorts were tested. A test-retest cohort contained 5 reliable visual fields, within a short interval, from 45 eyes of 23 participants with glaucoma and/or likelihood of developing glaucoma. A separate longitudinal cohort contained 508 eyes from 330 participants, tested on average 13 times. Three global indices were extracted; Mean Deviation (MD), Pattern Standard Deviation (PSD) and Visual Field Index (VFI). For each index we defined a critical p-value CritIndex, such that 5% of test-retest series showed significant deterioration with p<CritIndex, using artificial “test dates” in random order. Therefore these criteria have 95% specificity over series of 5 tests. The times to detect significant deterioration in the longitudinal cohort were compared using a survival analysis model. Results The median time to detect significant deterioration with MD was 7.3 years (95% confidence interval 6.8–7.9 years). For VFI, the median was 8.5 years (7.9–9.0); this comparison had p=0.088. For PSD, the median was 10.5 years (9.3–11.7); slower than MD with p<0.001. Within the first five years of a series, MD detected significant deterioration in 138 eyes, versus 104 for VFI (p=0.0013) and 107 for PSD (p=0.029). Conclusions MD detected significant deterioration sooner than VFI or PSD. In particular, MD detected more eyes in the first five years of their follow-up, which were presumably undergoing more rapid progression.

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

“…One of the challenges of evaluating new algorithms for detecting glaucomatous visual field progression is the difficulty in determining its true specificity due to the lack of a perfect independent reference standard that represents true stability. Several approaches have been used for this, such as collecting test-retest visual field data over short periods of time 10 – 16 (to ensure that no true progression is seen), although this approach is unlikely to truly represent long-term test-retest variability. Instead, the simulation model presented in our study provides a powerful method to estimate the specificity of new algorithms for detecting visual field progression, as results for eyes that are truly stable can be simulated to examine this.…”

“…Recent studies have used short-term test-retest data to evaluate specificity since progression can be assumed to be absent over such a time frame. 10 – 16 However, this approach may insufficiently capture the full extent of measurement variability present in real-world visual field tests over a long-term period, making it hard to accurately determine the true potential performance of these new methods in clinical practice.…”

Detecting glaucomatous progression with infrequent visual field testing

“…Our analyses were performed using a linear regression of the summary index MD, which has been shown to be more sensitive to change than similar regressions using the global indices Visual Field Index (VFI) or Pattern Standard Deviation . Furthermore, although regressions of the VFI are designed to be interpretively easy, irregularities in VFI‐based metrics can occur for both low rates of change and for visual fields of MD <−20 dB .…”

Detection of Functional Change Using Cluster Trend Analysis in Glaucoma

“…7 The first five visual field tests performed on participants in the test–retest cohort were assigned artificial “test dates” at 6-month intervals, to match the typical intertest interval in the longitudinal cohort. Then, the rate of change of MD was calculated by linear regression.…”