David P Crabb

Integrated visual fields: a new approach to measuring the binocular field of view and visual disability

BackgroundWe have developed a method of quantifying the central binocular visual field by merging results from monocular fields (Integrated visual field). This study aims to compare the new measure with the binocular Esterman visual field test in identifying patients with self-reported visual disability.MethodsForty-eight patients with glaucoma each recorded Humphrey 24-2 fields for both eyes and an Esterman on the same day, and each completed a binary forced-choice questionnaire relating to perceived visual disability. Computer software merged sensitivity values from monocular fields to generate an integrated visual field and a related score of the number of defects at the <10xa0dB and <20xa0dB level. Receiver operating characteristic (ROC) analysis was used to compare the integrated visual field score and the Esterman disability score with individual responses to the questions on perceived difficulty with visual tasks.ResultsComparison of areas under ROC curves revealed that a score based on the integrated visual field was generally better (median area: 0.79) than Esterman scores (median area: 0.70) in classifying patients with or without a self-reported perceived difficulty with visual tasks.ConclusionsThe integrated visual field offers a rapid assessment of a glaucoma patient’s binocular visual field without extra perimetric testing. As compared to an actual binocular field test (Esterman), the integrated visual field provides a better prediction of a glaucoma patient’s perceived inability to perform certain visual tasks.

Frequency of testing for detecting visual field progression

Aims: To investigate the effect of frequency of testing on the determination of visual field progression using pointwise linear regression (PLR). Methods: A “virtual eye” was developed to simulate series of sensitivities over time at a given point in the eye. The user can input the actual behaviour of the point (for example, stable or deteriorating steadily), and then a configurable amount of noise is added to produce a realistic series over time. The advantage of this over using patient data is that the actual status of the eye is known. Series were generated using different frequencies of testing, and the diagnosis that would have been made from each series was compared with the true status of the eye. A point was diagnosed as progressing if the regression line for the series showed a deterioration of at least 1 dB per year, significant at the 1% level. From these results, graphs were produced showing the number of points correctly or incorrectly diagnosed as progressing. Results: With the virtual eye deteriorating at a rate of 2 dB/year, it was found that the point was determined to be progressing quicker when more tests were carried out each year. With a stable virtual eye, it was found that increasing the frequency of testing increased the number of series that were falsely labelled as progressing during the first 3 years of testing. Conclusions: As the frequency of testing increases, the sensitivity of PLR increases. However, the specificity decreases; possibly meaning more unnecessary changes in treatment. Three tests per year provide a good compromise between sensitivity and 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.