A total of 183 participants (328 eyes) with available RTVue GCC and Stratus scans were identified from the AIG central database. A total of 849 GCC scans were screened, 48 were excluded for low signal and 41 were excluded due to segmentation error. The average SSI of the accepted GCC scans was 39.8. A total of 622 Stratus scans were screened, 12 scans were excluded due to low signal strength. The average SS of the accepted Stratus scans was 8.3. Stratus scans that had segmentation error were rejected and retaken by the photographer so a failure rate was not available. Finally, 18 eyes were excluded due to lack of valid FD-OCT or TD-OCT data. The remaining 310 eligible eyes from 178 participants were analyzed. The demographic and clinical information for each group were summarized in . Pre-perimetric glaucoma and PG participants were both significantly older than N participants by approximately 7 years. The potential overestimation of AROC due to the age imbalance was removed using a logistic regression model as stated in the methods section. There were more Caucasians in the N group compared to PG group. However, there was no significant difference between the racial groups in terms of the means of diagnostic parameters in the N group. The PG group had significantly thinner central corneal thickness than the N group and the PPG group had significantly higher IOP than the N group. Both PG and PPG groups had significantly worse VF parameters than the N group. All participants had open angles by gonioscopy except one participant with narrow angles in the PPG group.
Characteristics of the Study Population
According GSS2 system, the 109 PG eyes were classified into stage 0 (18 eyes), borderline (14 eyes), stage 1 (20 eyes), stage 2 (17 eyes), stage 3 (20 eyes), stage 4 (16 eyes) and stage 5 (4 eyes). Forty one eyes had localized defects, 24 eyes have mixed defects and 12 eyes have generalized defects. Seventy nine eyes (70.5%) had MD ≥−6.0 dB, 25 eyes (22.3%) had MD between −6.01 to −12.0 dB, and 8 eyes (7.1%) MD < −12dB.
summarized the distribution statistics of each diagnostic parameter by group. All parameters were significantly worse in the PPG and PG groups compared to the N group (P < 0.001). Because SID, PCV, FLV and GLV had non-normal distributions (Gamma distributions), these parameters were compared using the Wald test as described in the methods section.
The Distribution of Diagnostic Parameters by Group
Repeatability was assessed by three measures: ICC, pooled SD, and CV of repeated measures (, available at http://aaojournal.org
) during the same session. The repeatability in the PPG and PG groups is important because it provides an indication of how well a parameter can track progression through stages of the disease. Because glaucoma primarily affects the GCC (inner 3 retinal layers), it should affect GCC thickness and MR thickness in parallel fashion, matching µm by µm. Thus their relative precision in tracking glaucoma can be gauged by the pooled SD. The pooled SDs of FD-OCT GCC were significantly smaller than TD-OCT MR in the N (p = .005) and PG (p = 0.02) groups. The pooled SDs of FD-OCT MR were also significantly smaller than TD-OCT MR in the N (p = 0.002) and PG (p = 0.048) groups. In PPG group, the p-values were close to statistical significance (0.08 for GCC and 0.07 for MR). We cannot assume that glaucoma would affect GCC and NFL the same µm by µm, but their loss might be roughly proportional. Therefore CV could provide an approximate comparison between the repeatability of GCC and NFL. The CV’s of FD-OCT GCC were significantly smaller than TD-OCT NFL in the N (p = 0.0002) and PG (p < 0.001) groups but not in the PPG group (p=0.11). To compare the repeatability of all FD-OCT parameters, it is best to use ICC, because the pattern parameters (FLV, GLV, PCV, SID) have mean values close to zero (therefore CV’s are not meaningful) and have different units (therefore SD’s are not comparable). The FD-OCT GCC-AVG, GCC-GLV, and MR-AVG all have the highest ICC values of 0.99 in the PG and PPG groups and therefore may be the best parameters to watch for the tracking of glaucoma progression.
Repeatability of Diagnostic Parameters
The AROC () provided a summary measure of the accuracy of diagnosing glaucoma against the normal reference group. The MR average measured by FD-OCT and TD-OCT had equivalent AROC values. By isolating the inner retina, GCC-AVG significantly improved the diagnosis of PG (AROC = 0.90) compared to MR (p = 0.021). The pattern parameter GCC-GLV (p = 0.01) performed even better in diagnosing PG than GCC. The macular parameters GCC-AVG, GCC-FLV, GCC-GLV had statistically equal (p > 0.1) diagnostic power compared to NFL-AVG. For the diagnosis of PPG (versus N), we found no significant difference between GCC parameters and MR. Diagnostic accuracy is also shown in the form of diagnostic sensitivities at 1st and 5th percentile cutoff thresholds ().
Diagnostic Accuracy of Diagnostic Parameters
Sensitivity and Specificity of Diagnostic Parameters
The odds ratio (95% confidence interval) of having glaucoma for every 10 µm loss of tissue was 7.45 (4.14, 13.40) for GCC-AVG, 5.06 (2.58, 9.92) for NFL and 2.69 (1.96, 3.67) and 2.53 (1.84, 3.47) for FD-OCT and TD-OCT MR, respectively. We note that for each 10-µm of tissue loss, GCC predicts a significantly larger (3-fold) increase in the odds ratio than the loss of MR.
We chose a PG case with a very asymmetric VF to show how the locations of VF and disc rim defects correlated with GCC loss (). The predominantly inferior GCC loss correlated with the inferior disc rim loss and superior VF defect.
The pattern of GCC loss averaged over the PG group () showed sparing of the maculopapillar bundle, which was tilted in accordance with the fact that the fovea is below the level of the disc. The severest fractional loss occurred at the superior and inferior edges of the map, corresponding to the locations of the superior and inferior arcuate NFL bundles.
The average ganglion cell complex (GCC) fractional deviation map of the perimetric glaucoma (PG) group.
We chose a PPG case (, available at http://aaojournal.org
) where the average GCC was within normal and the pattern parameters were abnormal, to investigate how such a situation could arise. In this case, the GCC loss was localized primarily to an area above the fovea, and the abnormality was easily picked up by the pattern-based parameters. But the GCC was actually thicker than average in the maculopapillar area, which contributed toward an average GCC thickness that was still within the normal range. The focal loss of GCC in the superior macula was as much as 30%, corresponding to a mild thinning of the superotemporal disc rim, while the VF was essentially normal.
Figure 6 A pre-perimetric glaucoma (PPG) case example. (A) Ganglion cell complex (GCC) fraction deviation (FD) map. Some of the GCC parameters were abnormal (average = 82.5 µm, p > 5%; focal loss volume (FLV) = 4.9%, P<0.5%, global loss (more ...)
To investigate whether GCC can help detect glaucomatous abnormalities not picked up by NFL, we constructed Venn diagrams of GCC and NFL abnormalities in both the PG and PPG groups (). We combined the 3 best GCC parameters – GCC abnormality was defined as GCC-AVG, FLV or GLV below fifth percentile of the normal reference. Abnormal NFL was defined by NFL-AVG below fifth percentile. GCC detected additional 9% of PG cases and 11% of PPG cases that were not detected by NFL.
Venn diagrams showing the overlap between abnormal nerve fiber layer (NFL) and ganglion cell complex (GCC) thicknesses in both perimetric glaucoma (PG) and pre-perimetric glaucoma (PPG) groups. Abnormalities were detected at the 5 percentile level.