Long-term fluctuation of standard automatic perimetry, pulsar perimetry and frequency-doubling technology in early glaucoma diagnosis.

PURPOSE: Analyze the stability and accuracy of 3 perimetric techniques. METHODS: A total of 104 stable eyes (65 subjects) with ocular hypertension and early glaucoma [group G, mean defect = 1.08 dB, SD = 2.0, in standard TOP automatic perimetry (SAP)] were examined 5 times during 18 months using: (a) SAP; (b) Pulsar temporal modulation perimetry (T30W), and (c) frequency-doubling technology (FDT N30). Ninety eyes from 90 normal controls were compared with the first set of examinations of group G. RESULTS: The learning effect was minimal in the 3 techniques but higher in Pulsar (1.0 src, p < 0.05) than in SAP and FDT (0.4 dB). Long-term fluctuation (F) was significantly higher in FDT (3.1 dB, SD = 1.4, p < 0.0001) than in SAP (2.3 dB, SD = 1.1) and in Pulsar (1.9 src, SD = 0.7). Pulsar and FDT reduce F when increasing the number of examinations. F seems equivalent in SAP and FDT and lower in Pulsar, considering small-scale differences of the 3 perimeters. CONCLUSIONS: A slight learning effect would be expected on FDT and SAP in patients with previous experience with SAP. The stability and sensitivity of Pulsar is greater than on the other 2 systems. For early diagnosis of glaucoma it is essential to prove the reproducibility and coincidence of perimetric results.

Structure-function relationship depends on glaucoma severity.

BACKGROUND/AIMS: To determine the structure-function relationship throughout the different stages of glaucoma. SUBJECTS: 228 controls and 1007 suspected, early, moderate, or advanced glaucomas. In controls, Pearson coefficient of variation (PCV) of standard automated perimetry mean sensitivity (SAP-MS) and global mean retinal nerve fibre layer thickness (RNFL, Heidelberg Retina Tomograph) were calculated. Piecewise linear regression was used to evaluate if the data were related better to two separate regression lines in all population. RESULTS: PCV for SAP-MS and RNFL was 6.19% and 29.27%, respectively. For SAP-MS >22.42 dB (piecewise linear regression-obtained breakpoint, p<0.05), the logarithmic and linear relationships between SAP-MS and mean RNFL thickness were not different (r = 0.182, r = 0.185 respectively; p = 0.950). For SAP-MS <22.42 dB, the logarithmic and linear relationships between SAP-MS and mean RNFL thickness were not different (r = 0.353, r = 0.344 respectively; p = 0.884). CONCLUSION: The logarithmic and linear relationships between SAP-MS and mean RNFL thickness did not differ when individuals were stratified using the piecewise linear regression-obtained cut-off point. The curvilinear relationship between the morphological and perimetric results may be due to the wide variability in normal morphology and limitations in the dynamic range of the morphological tests in cases with moderate and severe defects.