Fig 1.
Algorithm for the classification of single eyes into ‘no glaucoma suspect’, ‘possible’ and ‘probable’ glaucoma suspect.
Glaucoma suspect is defined in the text, for the glaucoma score definition see Table 1.
Table 1.
Calculation of a glaucoma score for the classification into possible or probable glaucoma.
The score was defined as the sum of the points. Calculation was performed for each eye separately.
Table 2.
Cross tabulation for diagnosing individuals on categorization of left and right eyes.
Numbers are defined in the marginals and final diagnoses of the individuals are given in cells.
Table 3.
Age and sex distribution.
Table 4.
Cross-classified table for all FDT results (n = 4167).
Table 5.
Distribution of elevated IOP and pathological FDT, based on both eyes.
Table 6.
Comparison of different screening models with respect to the different statistical parameters, glaucoma suspects.
The assessment of the ophthalmologist was defined as “gold standard”. He appraised 4056 (97.34%) as “no glaucoma suspects” and 111 (2.66%) as “glaucoma suspects”.
Table 7.
Comparison of different screening models with respect to the different statistical parameters, probable glaucoma.
The assessment of the ophthalmologist was defined as “gold standard”. He appraised 4154 (99.69%) as “not probable glaucoma” and 13 (0.31%) as “probable glaucoma”.
Table 8.
Subjects treated with glaucoma medication and relation to the algorithm’s and to expert’s decision on the prevalence of glaucoma suspects.
Table 9.
Subjects treated with glaucoma medication and relation to the algorithm’s and to expert’s decision on the prevalence of probable glaucoma suspects.
Table 10.
Number needed to screen.
Table 11.
Cost-calculation of the whole study.
Table 12.
Varying criteria in a diagnosis of glaucoma (according to [32]), complemented by the Tajimi study [33] and [34] and the definition of Foster et al. [35].