Biomedical subjects

Paul G D Spry

Publications and source records attributed to Paul G D Spry.

5 recordsLinked to original sources

PubMed2002

Spatial and temporal processing of threshold data for detection of progressive glaucomatous visual field loss.

OBJECTIVE: To evaluate the effect of spatial and temporal filtering of threshold visual field data on the ability of pointwise linear regression (PLR) to detect progressive glaucomatous visual field loss. METHODS: Longitudinal visual field data (Full-Threshold Program 30-2 test point pattern) were simulated using a computer model of glaucomatous visual field progression. This approach permitted construction of a "gold standard" because matching visual field data without variability could be generated and analyzed. Four clustered progressive defects were produced, consisting of 2, 3, 9, and 18 locations, respectively, each with progression rates of -1 and -2.5 dB/y. Pointwise linear regression was used to identify progressive test locations (criterion for progression of statistically significant slope of < or =-1 dB/y, P<.05). Each visual field series was analyzed after the following 3 procedures: (1) no filtering (unprocessed data), (2) Gaussian spatial possessing (3 x 3 grid), and (3) temporal processing (2 field moving average). The effect of spatial and temporal processing on PLR discriminatory power for progression detection was quantified by comparison with the gold standard. RESULTS: Spatial processing reduced PLR sensitivity to levels below that achieved for analysis of unprocessed data for small progressive defects (< or =9 locations) or at the low true progression rate (-1 dB/y). Under these conditions, spatial processing caused small PLR specificity improvement. Spatial processing only improved PLR sensitivity above unprocessed levels when progressive defects were large and changing rapidly (progression rate of -2.5 dB/y). Temporal processing gave consistent PLR improvement in sensitivity for all defect sizes and true progression rates. Pointwise linear regression sensitivity gain provided by temporal processing allowed progression to be detected 2 to 3 visual fields earlier than for analysis of raw data. Specificity dropped slightly as a result of temporal processing but remained at 89% or above for all conditions studied. CONCLUSIONS: Gaussian spatial processing reduces PLR discriminatory power with low true progression rates or small progressive defect sizes and, therefore, is of limited use for detection of progressive visual field loss. Temporal processing improves the sensitivity of PLR and reduces the number of tests required to detect progressive loss with minimal loss of specificity. CLINICAL RELEVANCE: Image processing techniques can be applied to threshold visual field data to enhance sensitivity or specificity of PLR for the determination of progressive change. This investigation demonstrates that temporal processing may assist with the detection of significant progressive visual field loss with fewer test results than unprocessed data.

Computer Simulation↗

PubMed2002

The effect of ocular dominance on visual field testing.

PURPOSE: During standard automated perimetry (SAP), some patients experience visual disturbances in the tested eye while the other eye is covered with an opaque occluder. It is possible that a binocular interaction producing an inhibitory response in the nonoccluded eye, such as rivalry or Ganzfeld blankout, may be the causative factor, particularly when the dominant eye is occluded. The objective of this experiment was to determine whether subjective visual disturbances occurring during conventional perimetric test conditions were related to ocular dominance and to investigate the effect of these disturbances on measurements made during threshold visual field analysis. METHOD: Ocular dominance was determined by questioning and objective testing on 55 normal subjects. Each subject underwent program 24-2 Full Threshold SAP on a Humphrey Field Analyzer, and an opaque black patch was used to occlude the nontested eye. After testing, patients were asked to report symptoms of visual disturbance characteristic of rivalry or blankout, and the relationship between ocular dominance and visual disturbances was investigated. To determine whether symptoms of rivalry or blankout had affected visual field quantification, comparisons of short-term fluctuation, mean deviation, and false-negative errors were performed between eyes with and without visual disturbances. RESULTS: A total of 24 of 55 subjects reported visual disturbances consistent with rivalry or blankout (44%). Sixteen subjects complained of the phenomenon in one eye, and eight complained of the phenomenon in both eyes. Of the 16 experiencing disturbances in one eye only, nine cases occurred during occlusion of the dominant eye. The association between ocular dominance and visual disturbances was not found to be significant (p > 0.10). No significant differences in short-term fluctuation (p = 0.78), mean deviation (p = 0.64), or false-negative errors (p = 0.10) were found between eyes with and without visual disturbances. CONCLUSIONS: Patients undergoing standard automated perimetry with opaque patch occlusion of the nontested eye often experience visual disturbances consistent with rivalry or blankout, although these disturbances do not cause increased within-test variability or reduced sensitivity as quantified by visual field global indices. In terms of summary visual field indices, ocular dominance does not appear to affect visual field test results.

Adult↗

PubMed2002

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

PURPOSE: The authors sought to verify computer simulation of visual fields by comparing thresholds of real and corresponding simulated visual fields. METHODS: Four patients with stable glaucomatous visual fields and three patients with progressing glaucomatous visual fields were chosen for the study. Visual fields had been recorded at 6-month intervals for 5 to 7.5 years. A previously described computer simulation program was used to generate a corresponding simulated visual field for each of the real fields. Twenty different levels of response variability and long-term variability were used in the simulations. Pointwise sensitivity differences between real and simulated fields were calculated. The average difference and 95% interval of the differences were analyzed for the different simulation conditions, for the pointwise sensitivities in the real patient fields, and to determine whether the field was stable or progressing. RESULTS: In almost all simulation conditions, the average pointwise sensitivity differences ranged from -1 to 1 dB and were not significantly different among different simulation conditions. The 95% interval of the average difference increased significantly with response variability, whereas long-term variability failed to show any apparent effect. Average pointwise differences and the 95% intervals were greatest in locations where the real-patient field had reduced sensitivity of 14 dB or worse. CONCLUSION: The simulation program provided good estimates of visual field sensitivities. Increasing amounts of response, but not long-term variability, produced a linear increase in the variability of threshold sensitivities. This finding implies that short-term rather than long-term fluctuation is the most important factor determining the variability of thresholds.

Aged↗

PubMed2002

Within-test variability of frequency-doubling perimetry using a 24-2 test pattern.

PURPOSE: To evaluate patient-response (within-test) variability for targets of the smaller frequency-doubling technology perimetry test that employs a 24-2 stimulus-presentation pattern. METHODS: Patient-response variability was examined using the method of constant stimuli for standard (10 degrees ) and small (4 degrees ) customized frequency-doubling technology perimetry stimuli presented on a CRT screen. Small stimuli were designed for use in a 24-2 test pattern. Matched test locations were examined in 24 subjects (8 normal, 8 in whom glaucoma was suspected, and 8 glaucoma patients). Threshold sensitivity (in decibels for the 50% detection level) and variability (interquartile range in decibels) were obtained from frequency-of-seeing curves derived from data fitting with cumulative Gaussian functions. Groups were compared using a two-way ANOVA. RESULTS: Thresholds obtained using standard and small stimuli were highly correlated (R = 0.94, P < 0.001, Pearson correlation), although smaller targets systematically estimated sensitivity to be 2.0 dB (95% CI, 1.7-2.4 dB) lower than standard targets. No significant difference in patient-response variability was observed between standard and small targets (P = 0.067), although both target sizes demonstrated small but significant increases in variability with reduced sensitivity. Mean (SD) patient-response variability for the normal, suspect, and glaucoma groups was 1.0 (0.6), 0.9 (0.4), and 1.8 (1.4) dB for standard-sized stimuli and 1.1 (0.8), 1.5 (1.2), and 2.0 (0.9) dB for small stimuli. CONCLUSIONS: Small (4 degrees ) frequency-doubling technology perimetry targets have variability characteristics that are not statistically significantly different from those observed for standard-sized (10 degrees ) stimuli. Reduction in frequency-doubling technology perimetry stimulus size necessary to produce 24-2 test resolution is unlikely to affect test repeatability. Smaller, more numerous stimuli may offer clinical advantages both in terms of detecting small defects and identifying progressive loss.

Aged↗

PubMedDate not supplied

Identification of progressive glaucomatous visual field loss.

In normal individuals, visual field measures are not perfectly repeatable and individual test locations exhibit both short- and long-term sensitivity variations. This physiologic variability is greatly increased in glaucoma and confounds detection of real progressive loss in visual function. Distinguishing progressive glaucomatous visual field loss from test variability therefore represents a complex task. Procedures used for detection of glaucomatous visual field progression may be broadly grouped into four categories: 1) clinical judgment, which consists of simple subjective observation of sequential visual field test results; 2) defect classification systems, whereby specific criteria are used to stratify field loss by discrete score and define progression as score change over time, such as the Advanced Glaucoma Intervention Study scoring system; 3) trend analyses, which follow test parameters sequentially over time to determine the magnitude and significance of patterns within the data, for example linear regression; and 4) event analyses, which identify single events of significant change relative to a reference examination. All of these methods demonstrate distinct benefits and drawbacks, making each useful in specific circumstances, although no single method appears universally ideal. At the present time the best method of detection of progression may be to rely upon confirmation of change at successive examinations and also by correlation of visual field changes with other clinical observations. Alternative analysis methods may become available in the near future to help identify cases of progressive loss.

Confounding Factors, Epidemiologic↗