Biomedical subjects

S K Gardiner

Publications and source records attributed to S K Gardiner.

2 recordsLinked to original sources

PubMed2006

Modeling the sensitivity to variability relationship in perimetry.

PURPOSE: Studies in glaucoma patients show that standard automated perimetry results increase in variability as sensitivity decreases. However, the reasons for this change are unclear. This study presents the principle of Divergent Dysfunction as a possible explanation for this change in variability, and incorporates it into a model that can be used to simulate perimetry. METHODS: A computer program was written to simulate visual field test results based on the model, using a Full Threshold testing strategy. The validity of the simulation was tested by comparing it with normal sensitivity values, and with test-retest data from 63 participants evaluated five times each over the course of 1 month. The effect on the simulated data of varying parameters of the model was investigated, such as changing the magnitude of variability and the percentages of false positive and negative responses. RESULTS: The correlation between subject and simulated test-retest data was 0.987. Several factors were found to affect the sensitivity-variability relationship for the simulated data, most notably the rate of sensitivity decline, the percentage of false positives, and the starting luminance of the test procedure. CONCLUSIONS: The principle of Divergent Dysfunction presented here provides a plausible explanation for the sensitivity-variability relationship for standard automated perimetry in glaucomatous eyes. The model and resultant simulation program aim to provide an intuitive demonstration of the principle, which can also be used to examine the effectiveness of different testing strategies. These findings have great implications for future clinical research.

Computer Simulation↗

PubMed2002

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.

Computer Simulation↗