Parafoveal and peripapillary PCD measurements obtained by the Canon OCT-HS100 within the same session and from the same subject are in excellent agreement when considering the ICC values; upon retest their absolute values may differ up to 2.7% and 1.7%, respectively. NFI measurements are in good agreement; upon retest they may differ up to 13.1 and 6.9, respectively. Reliability from average measures is higher than reliability from single measures among all metrics in both regions. The angiographic metrics are weakly to moderately correlated with the corresponding retinal layer thicknesses.
A number of other studies have evaluated the intrasession repeatability of OCT-A metric PCD, albeit with different devices. Regarding the parafoveal region, Alnawaiseh et al.
11 found the CoR and ICC (2,1) for the PCD to be 3.4% and 0.72, respectively, to be compared with 2.7% (95% confidence interval [CI]: 1.8%–3.6%) and 0.76 (0.56–0.88) in our study. Fang et al.
23 reported a CoR of 3.2% and an ICC of 0.86, while the same repeatability variables reported by Coscas et al.
24 were 3.3% and 0.78 and by Al-Sheikh et al.
9 3.4% and 0.90. The latter three studies did not clarify which ICC was presented. An ICC of 0.89 without further specification was also reported by Lei et al.
10 A larger CoR of 4.9% was reported by Chen et al.
25 because they included multiplication by the square root of 2 in the calculation; the corrected value is 3.4%. Venugopal et al.
26 presented a CoR of 4.4% together with an ICC of 0.87 for the parafoveal region, as well as the corresponding values of 4.1% and 0.86 for the peripapillary region. Hence, the CoR for PCD that we report here as assessed by the angiographic module of Canon OCT HS-100 with customized software is at least as good as the ones reported in most of the aforementioned studies for different devices. Venugopal et al.,
26 using a different device in their study, found the measurements to be significantly less repeatable than ours both in the parafoveal and the peripapillary region. This could be attributed to improved results of our customized software, to different inclusion criteria, or to differences between devices in terms of scanning and segmentation.
Chen et al.
8 evaluated the repeatability of NFI but with a different statistical approach. They found a coefficient of variation (CV) of 3.3% and 4.2% for the parafoveal and peripapillary region, respectively. For the sake of comparison, we also calculated the CV (SD of differences divided by the mean), which was 2.3% (CI: 1.7%–2.9%) for the parafoveal and 1.2% (CI: 0.9%–1.5%) for the peripapillary region. This might suggest improved repeatability of our methods; however, using exclusively the CV might spuriously suggest that repeatability is worse (higher CV) in ocular diseases where the mean angiographic metrics decrease (e.g., in glaucoma) or in devices with lower average signal intensity. Indeed, a recent study found some differences between glaucoma and healthy in the CV of the inferotemporal region of the peripapillary scan, but not in the CoR.
26
Variation among consecutive scans could in general be a consequence of signal strength (even within the high quality scans), floaters, or measurement noise. Importantly, since OCT-A is only able to image capillaries that are perfused and visible given the resolution of the system, it is possible that a portion of the variation between consecutive scans could be attributed to physiological reasons such as small changes in perfusion pressure due to, for example, cardiac cycle related variability in IOP.
27
To our knowledge, this is the first study that addressed the PCD and NFI intrasession repeatability with the Canon OCT-HS100, and the first report of CoR and ICC for OCT-A metric NFI. Importantly, the Canon OCT-HS100 is currently the only device using a full-spectrum amplitude decorrelation algorithm; therefore, a separate evaluation was also deemed necessary.
28 A strength of this study is the fact that the calculation of the quantification parameters and reasoning are described in detail. This generic approach avoids obscurities involved in metrics belonging to proprietary algorithms and thus allows for harmonization. For example, it is unclear if other algorithms include the larger vessels originating from the ONH in their calculations. A limitation of this study is the restriction to the superficial capillary plexus. Additionally, other metrics such as fractal dimension and foveal avascular zone were not considered in this analysis. Lastly, it is possible that the small sample size of the study affects the weak correlations of PCD and NFI with the GCC. A larger sample size could, for example, result in measured differences regressing toward the mean. However, our results are in agreement with Yu et al.
29 who reported stronger correlations in the peripapillary than the parafoveal sector.
The results suggest that it is possible to quantify the retinal microvasculature through OCT-A with a satisfactory degree of accuracy. The additional information provided by OCT-A metrics can be helpful in differentiating between healthy and diseased eyes within the clinical setting, should the effect size be sufficiently large. However, this study shows that one single OCT-A image, which is most frequently obtained within the clinical setting, is not enough to guarantee a reliable absolute value estimation. Consequently, the use of these measurements for the evaluation and follow-up on an individual basis is not recommended. Instead, averaging of consecutive images or measurements might be a more informative approach,
30 and this is also suggested in our study when comparing average measures versus single measures ICC values.
In conclusion, by applying a generic quantification algorithm to the images obtained with a commercially available OCT-A ,we were able to quantify perfusion and estimate its intrasession repeatability. Small changes in perfusion fall within the test-retest variability; changes surpassing the variability in healthy subjects should be easily detectable in a clinical setting. This is important, since it provides insight on how the output of the specific device can be interpreted and handled in the clinic. Metrics with improved test-retest variability and diagnostic accuracy together with quantified blood velocity could potentially not only serve as additional clinical markers but also help unravel underlying pathophysiological mechanisms.