Investigative ophthalmology & visual science · 2013
Diagnostic performance of visual field test using subsets of the 24-2 test pattern for early glaucomatous field loss.
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Wang Yanfang, Henson David B
Аннотация
PURPOSE: To explore the diagnostic performance of threshold visual field tests using subsets of the standard 24-2 test pattern in detecting early/moderate glaucomatous field loss.
METHODS: Normal (Brusini stage 0, n = 2344) and defective eyes (Brusini stage 2-3, n = 2222) from a database of visual field tests (6696 eyes/3586 patients, SITA standard 24-2 algorithm) were selected and resampled using a bootstrap method. The positive predictive values (PPVs) of each test location were calculated for the resampled datasets with a fail criteria of a single missed stimulus at a pattern deviation probability level of less than 0.01. Optimized test patterns started with the most frequent location of the maximum PPV in datasets. Eyes missing the location were removed and the PPV values of residual sample recalculated. The process was repeated until all defective eyes were detected. Receiver operating characteristic (ROC) curves were established for the PPV-optimized and five randomized patterns. Characteristics of visual field defects detected with subsets of optimized test pattern were established.
RESULTS: With the PPV-optimized pattern, 95% of the field defects were detected with 30 locations and all with 43 locations. Areas under the ROC curve were greatest for the optimized pattern. With each increment in the number of test locations, the Mean Deviation of additionally detected eyes became more positive while Pattern Standard Deviation became less positive (P < 0.001).
CONCLUSIONS: Good diagnostic performance can be obtained with optimized subsets of the standard 24-2 test pattern that can provide substantial savings in test times.
METHODS: Normal (Brusini stage 0, n = 2344) and defective eyes (Brusini stage 2-3, n = 2222) from a database of visual field tests (6696 eyes/3586 patients, SITA standard 24-2 algorithm) were selected and resampled using a bootstrap method. The positive predictive values (PPVs) of each test location were calculated for the resampled datasets with a fail criteria of a single missed stimulus at a pattern deviation probability level of less than 0.01. Optimized test patterns started with the most frequent location of the maximum PPV in datasets. Eyes missing the location were removed and the PPV values of residual sample recalculated. The process was repeated until all defective eyes were detected. Receiver operating characteristic (ROC) curves were established for the PPV-optimized and five randomized patterns. Characteristics of visual field defects detected with subsets of optimized test pattern were established.
RESULTS: With the PPV-optimized pattern, 95% of the field defects were detected with 30 locations and all with 43 locations. Areas under the ROC curve were greatest for the optimized pattern. With each increment in the number of test locations, the Mean Deviation of additionally detected eyes became more positive while Pattern Standard Deviation became less positive (P < 0.001).
CONCLUSIONS: Good diagnostic performance can be obtained with optimized subsets of the standard 24-2 test pattern that can provide substantial savings in test times.