Sensitivity differences between real-patient and computer-stimulated visual fields

Comparison of Snellen and Early Treatment Diabetic Retinopathy Study charts using a computer simulation

AIM

To compare accuracy, reproducibility and test duration for the Snellen and the Early Treatment Diabetic Retinopathy Study (ETDRS) charts, two main tools used to measure visual acuity (VA).

METHODS

A computer simulation was programmed to run multiple virtual patients, each with a unique set of assigned parameters, including VA, false-positive and false-negative error values. For each virtual patient, assigned VA was randomly chosen along a continuous scale spanning the range between 1.0 to 0.0 logMAR units (equivalent to 20/200 to 20/20). Each of 30 000 virtual patients were run ten times on each of the two VA charts.

RESULTS

Average test duration (expressed as the total number of characters presented during the test ±SD) was 12.6±11.1 and 31.2±14.7 characters, for the Snellen and ETDRS, respectively. Accuracy, defined as the absolute difference (± SD) between the assigned VA and the measured VA, expressed in logMAR units, was superior in the ETDRS charts: 0.12±0.14 and 0.08±0.08, for the Snellen and ETDRS charts, respectively. Reproducibility, expressed as test-retest variability, was superior in the ETDRS charts: 0.23±0.17 and 0.11±0.09 logMAR units, for the Snellen and ETDRS charts, respectively.

CONCLUSION

A comparison of true (assigned) VA to measured VA, demonstrated, on average, better accuracy and reproducibility of the ETDRS chart, but at the penalty of significantly longer test duration. These differences were most pronounced in the low VA range. The reproducibility using a simulation approach is in line with reproducibility values found in several clinical studies.

The effect of test variability on the structure-function relationship in early glaucoma

PURPOSE

To determine whether the weakness of the structure-function relationship could be produced by test variability alone, without implying underlying dissociation between the true rates of structural and functional change.

METHODS

Perimetric mean deviation (MD), and rim area (RA) and cup volume (CV) from confocal scanning laser ophthalmoscopy, over six visits, were taken from 166 eyes of 92 participants with high-risk ocular hypertension or suspected/early glaucoma in the Portland Progression Project. Models were created of each measure's variability. A further model predicted the rate of functional change from the rate of structural change. These were used to generate realistic simulated sequences of both functional and structural data with different standard deviations σ between the underlying rates of change. 'Observed' structure-function relationships were calculated. An empirical p-value was derived, equaling the proportion of simulated series for which the 'observed' structure-function dissociation was greater than that seen in patient data.

RESULTS

The correlation between the rates of structural (RA) and functional (MD) change was 0.171, consistent with σ < 0.02 dB/yr. Using CV, the correlation was -0.091, consistent with σ < 0.01 dB/yr. By comparison, the models predicted that the standard deviation of the rate of functional change for a healthy eye due to test variability would be 0.18 dB/yr.

CONCLUSION

Test variability is sufficiently large that realistic patient data can be simulated without requiring a large variability between the underlying rates of structural and functional change. This absence of implied dissociation is a necessary condition for it to be valid to combine structural and functional measures to improve estimates of functional change and/or to reduce perimetric variability.

A two-stage neural spiking model of visual contrast detection in perimetry

Perimetry is a commonly used clinical test for visual function, limited by high variability. The sources of this variability need to be better understood. In this paper, we investigate whether noise intrinsic to neural firing could explain the variability in normal subjects. We present the most physiologically accurate model to date for stimulus detection in perimetry combining knowledge of the physiology of components of the visual system with signal detection theory, and show that it requires that detection be mediated by multiple cortical cells in order to give predictions consistent with psychometric functions measured in human observers.

A comparison of algorithms for calculating glaucoma change probability confidence intervals

PURPOSE

To evaluate the ability to detect change in standard automated perimetry data using 4 different methods for calculating the glaucoma change probability (GCP).

METHODS

A database of stable visual fields, collected within 1 week from 35 glaucoma patients and within 6 months from 15 normal patients, was used to determine confidence intervals for GCP using 4 different methods. The methods classified visual field locations on the basis of either defect or mean threshold, and used test-retest data or baseline-less-follow-up data to determine values for the confidence intervals. The specificity of the 4 methods was measured using 3700 locations artificially generated to simulate stable visual field data. The sensitivity of the methods was measured using 3330 artificially generated locations that decreased in either a linear, curvilinear, or bi-linear fashion by 2, 3, or 4 dB per year on average.

RESULTS

Using GCP with confidence intervals built using the methods described in the literature (on the basis of defect and test-retest differences) resulted in a higher specificity than techniques based on mean threshold. However, the mean-based methods were more sensitive at detecting a decrease in a location. Building confidence intervals using the difference between a baseline and the current measurement (baseline-less-follow-up), rather than test-retest differences, also improved the detection of visual field progression.

CONCLUSIONS

Stratifying baseline visual field measurements based on defect and eccentricity as described in the literature results in an unusually high specificity: 98% accuracy in classifying the same stable data that generated the 95% confidence intervals, rather than the expected 95% accuracy. By stratifying measurements based on mean threshold, and using baseline-less-follow-up rather than test-retest differences to build 95% confidence intervals, sensitivity is increased by 14.1%. This increase in sensitivity comes with a corresponding 2.2% decrease in specificity.

Reducing noise in suspected glaucomatous visual fields by using a new spatial filter

Visual field testing with automated perimetry is hampered by the amount of noise present in the readings. Here, we derive a physiologically accurate spatial filter to be applied to the data after patient examination. The filter was tested by a Virtual Eye computer simulation. By simulating series of stable fields it was shown that specificity of determining visual field changes was improved; while simulating progressing fields (based on a map of the optic nerve head) it was shown that sensitivity was also improved. The filter appears to reduce the noise in glaucomatous visual field data and may be clinically useful.