The visualFields package: A tool for analysis and visualization of visual fields

Between-subject differences in the shape of the nasal visual field were assessed for 103 volunteers 21–85 years of age and free of visual disorder. Perimetry was conducted with a stimulus for which contrast sensitivity is minimally affected by peripheral defocus and decreased retinal illumination. One eye each was tested for 103 volunteers free of eye disease in a multi-center prospective longitudinal study. A peripheral deviation index was computed as the difference in log contrast sensitivity at outer (25°–29° nasal) and inner (8° from fixation) locations. Values for this index ranged from 0.01 (outer sensitivity slightly greater than inner sensitivity) to −0.7 log unit (outer sensitivity much lower than inner sensitivity). Mean sensitivity for the inner locations was independent of the deviation index (R2 < 1%), while mean sensitivity for the outer locations was not (R2 = 38%, p < 0.0005). Age was only modestly related to the index, with a decline by 0.017 log unit per decade (R2 = 10%). Test-retest data for 21 volunteers who completed 7–10 visits yielded standard deviations for the index from 0.04 to 0.17 log unit, with a mean of 0.09 log unit. Between-subject differences in peripheral deviation persisted over two years of longitudinal testing. Peripheral deviation indices were correlated with indices for three other perimetric stimuli used in a subset of 24 volunteers (R2 from 20% – 49%). Between-subject variability in shape of the visual field raises concerns about current clinical visual field indices, and further studies are needed to develop improved indices.

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“…These volunteers were experienced and reliable on perimetric testing (Marin-Franch and Swanson 2013, Swanson, Malinovsky et al 2014). …”

Section: Methodsmentioning

confidence: 99%

Individual differences in the shape of the nasal visual field

et al. 2017

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“…The novel GHr estimator is available from the free R package visualFields [25]. Since the GH estimator has been shown to underestimate the height of the hill of vision so that PD maps underestimate the severity of glaucomatous focal loss, improved methods are desirable [20].…”

Section: Discussionmentioning

confidence: 99%

“…This is because the mean GH for a normal population is 1.8 dB [23], and not zero (see vertical arrow for the 7th position in Figure A1). For this reason, we computed here the GH as 1.8 dB minus the 7th largest TD value from the measured visual field instead of just the 7th largest TD value, and GH is, hence, what needs to be subtracted to a measured visual field so that its height equals that of the age-corrected mean normal.…”

Section: Methodsmentioning

confidence: 99%

“…The R programming environment [24] and its contributed package visualFields [25] were used for all the computations in this paper. The visualFields package contains normative values obtained for static automated perimetry (SAP) with the Goldmann size III stimulus presented with the 24–2 pattern of locations using the SITA-Standard strategy.…”

Section: Methodsmentioning

confidence: 99%

“…The visualFields package contains normative values obtained for static automated perimetry (SAP) with the Goldmann size III stimulus presented with the 24–2 pattern of locations using the SITA-Standard strategy. Details about the control subjects with inclusion and exclusion criteria can be found in Marín-Franch and Swanson [25]. Normative values for PD and corresponding probability maps using the novel GHr estimator were obtained with the same dataset of control subjects.…”

Section: Methodsmentioning

confidence: 99%

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A novel strategy for the estimation of the general height of the visual field in patients with glaucoma

Marín-Franch

Swanson

Malinovsky

2014

Graefes Arch Clin Exp Ophthalmol

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Background More accurate estimation of the general height of the visual field may improve our ability to detect and monitor progression of diseases affecting visual function such as glaucoma. General height (GH) can be affected by factors such as cataracts, pupillary miosis, refractive error, and learning and fatigue effects. The conventional GH index, consisting of subtracting the 85th largest value from the total-deviation map, has been shown to overestimate the height in patients with moderate and advanced glaucoma. We aimed at developing an improved estimator for general height based on ranking of total-deviation values that are within normal limits (GHr). Methods Two datasets were used for the comparisons between GH and GHr estimates: one with 369 visual fields for 102 controls and another with 500 visual fields for 124 patients. For controls, we compared the distributions of mean of total deviation (MD) and of mean of pattern deviation (MPD) derived from both the GH and the GHr estimates. For patients, we assessed agreement between both estimates and between pairs of consecutive visits. We also compared linear fits in progression analyses. All data were collected with 24-2 SITA Standard. Results For control subjects and patients with MD above −5.5 dB, estimates with the GHr estimator were not significantly different than with the GH estimator. For patients with glaucoma with MD below −5.5 dB, as MD became more negative the GH estimates were increasingly greater than GHr estimates. For patients with glaucoma, test-retest variability was lower with the GHr estimator: between visits agreement was better for GHr estimates than for GH estimates (SD of 0.8 dB versus 1.5 dB; p < 0.0001). Linear progression analysis fitted better the data from the GHr estimator. Root mean square error for GHr was 0.4 dB; lower than the 0.8 dB for GH (p < 0.0001). Conclusions The novel GHr estimator is very different from the conventional GH estimator, has more solid foundations and better statistical properties. Nevertheless, it is not always better than the GH estimator, in particular, if no focal loss is present. Pattern-deviation maps obtained with GHr reduce systematic underestimation of glaucomatous damage.

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