Table 1.
Baseline characteristics of the study subjects.
Fig 1.
Method for determining the disappearance angle of the RPCs.
The image processing and analysis software ImageJ was used to analyze OCT angiography images of the optic disc. At the edge of the optic disc, two sites where the RPCs disappeared and the RNFL defect was present were identified (white circles), and the center of the optic disc was connected to each site with straight lines (yellow lines). The angle generated by the two lines on OCT angiography images was defined as the disappearance angle of the RPCs (Fig 1A), and that on the en face images was defined as the angle of the RNFL defect (Fig 1B).
Fig 2.
Method for dividing the peripapillary area into segments on OCT angiography and 3D-OCT images.
Using the Angio disc mode of OCT angiography, the optic disc was divided into six areas (blue dotted lines), which were further grouped into four segments: a superior segment consisting of two superior areas (S), an inferior segment consisting of two inferior areas (I), a temporal segment (T), and a nasal segment (N) (Fig 2A). The four segments on OCT angiography images were matched with four segments of the optic disc that were automatically defined by the 3D-OCT optic disc map mode (Fig 2B, lower panel on the right).
Table 2.
Reproducibility of the disappearance angle of the RPC and the RNFL defect.
Intraclass correlation coefficients determined with SPSS.
Fig 3.
(a) Pearson correlation coefficient matrix for flow density, structural variables, visual field, and clinical background in subjects with glaucoma. (Total patients) (b) Pearson correlation coefficient matrix for flow density, structural variables, visual field, and clinical background in subjects with glaucoma. (POAG group) (c) Pearson correlation coefficient matrix for flow density, structural variables, visual field, and clinical background in subjects with glaucoma. (NTG group). Fig 3a-c shows the correlations between FD, the disappearance angle of the RPCs, and other parameters. Fig3a shows the correlations in the overall population, Fig 3b shows the correlations in the POAG patients, and Fig 3c shows the correlations in the NTG patients. Abbreviations: OCT, optical coherence tomography; HFA, Humphrey field analyzer; RPC, radial peripapillary capillary; NFLD, retinal nerve fiber layer defect; PSD, pattern standard deviation; MD, mean deviation; cpRNFLT, circumpapillary retinal nerve fiber layer thickness.
Fig 4.
Scatterplots illustrating the linear associations between flow density (FD) on OCT angiography, the disappearance angle of the RPCs (DA), and other factors in glaucoma.
The correlation between FD, DA, and other factors was analyzed, and measurement data are shown in Fig 3A–3C. A scatterplot of FD and DA (y-axis) and the retinal nerve fiber layer defect (NFLD) angle (NFLD-A), circumpapillary RNFL thickness (cpRNFT), pattern standard deviation (PSD), mean deviation (MD), and threshold of sensitivity (TS) (x-axis) was generated to calculate a correlation coefficient. Only data with significant correlations are shown. r = correlation coefficient from the fitted linear regression model.
Fig 5.
Correlation between flow density on OCT angiography and cpRNFL thickness in different peripapillary segments.
The correlation between FD and cpRNFL thickness was analyzed in the four segments generated in Fig 2, and the actual measurement data are shown in Table 3. A scatter plot of FD (y-axis) and cpRNFL thickness (x-axis) was generated to calculate a correlation coefficient. r = correlation coefficient from the fitted linear regression model.
Table 3.
The correlation between flow density on OCT angiography and cpRNFL thickness varied by peripapillary segment.