The aim of this study is to compare the diagnostic performance of peripapillary and macular structural and perfusion parameters obtained by swept-source optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) in high myopia with open-angle glaucoma (OAG), and to develop a parsimonious combined diagnostic model.

A total of 239 patients (239 eyes) with HM who attended the Department of Ophthalmology, Peking University Third Hospital, from June 2022 to December 2024 and completed the relevant examinations were enrolled in the study. Among them, 97 were male (97 eyes) and 142 were female (142 eyes) with a mean of (53.1±8.6) years (ranging from 32 to 68 years). According to the presence or absence of OAG, the participants were divided into the HM group and the HM combined with OAG group (HMG group). Age, gender, spherical equivalent, axial length (AL), and visual field indices were recorded. OCT and OCTA were used to obtain parameters including peripapillary retinal nerve fiber layer (RNFL) thickness and radial peripapillary capillary (RPC) density, macular ganglion cell complex (GCC) thickness and loss volume indices, superficial capillary plexus (SCP) vessel density, and deep capillary plexus (DCP) vessel density. After Shapiro-Wilk normality testing, data were expressed as ±s, and comparisons between the two groups were performed using the independent-samples t test. The Benjamini-Hochberg method was used for false discovery rate correction of sectoral structural and blood flow parameters. Gender was expressed as number of cases and percentage and compared using the χ² test. Receiver operating characteristic curve analysis was used to calculate the area under the curve (AUC) and 95% confidence interval (CI), and differences in AUC were compared using the DeLong test. The indicators with significant statistical significance of AUC were selected to enter the multivariate Logistic regression model, and the simplified joint diagnosis model was constructed by multivariate Logistic regression analysis. Multicollinearity was evaluated by variance expansion factor, and variance expansion factor<5 was acceptable.

There were no statistically significant differences between the HM group 117 patients (117 eyes) and the HMG group 122 patients(122 eyes) in age, AL, spherical equivalent refraction, or central corneal thickness (t=-1.63, -1.50, 0.97, 0.8; P>0.05). However, the differences in mean deviation and pattern standard deviation of the visual field between the HMG and HM groups were statistically significant (t=15.3, -11.9; P<0.05). The global peripapillary RNFL thickness in the HMG group was (83.5±11.6) μm, which was lower than that in the HM group (94.2±10.2) μm, and the difference between groups was statistically significant (t=7.58, adjusted P<0.05), with an AUC of 0.76 (95%CI: 0.70 to 0.82). Quadrant analysis showed that the differences in superior and inferior RNFL thickness in the HMG group were both statistically significant (t=7.61 and 12.85; adjusted P<0.05). The global peripapillary RPC density in the HMG group was (47.1±5.1) %, which was lower than that in the HM group (49.2±3.4)%, and the difference between groups was statistically significant (t=3.76, adjusted P<0.05), with an AUC of 0.63 (95%CI: 0.56 to 0.70). Among these, inferior quadrant RPC density had the highest AUC (AUC=0.79, 95%CI: 0.73 to 0.84). The temporal quadrant RPC density in the HMG group was (44.8±4.5) %, which was close to that in the HM group (45.2±3.8)%, with no statistically significant difference (t=0.74, adjusted P>0.05). Among macular structural parameters, the global average GCC thickness in the HMG group was (85.1±10.5) μm, lower than that in the HM group (92.4±8.2) μm, and the difference between groups was statistically significant (t=6.00, adjusted P<0.05), with an AUC of 0.71 (95%CI: 0.64 to 0.77). The GLV in the HMG group was (14.2±6.8) %, higher than that in the HM group (4.2±3.2) %, and the difference between groups was statistically significant (t=-14.65, adjusted P<0.05), with an AUC of 0.88 (95%CI: 0.85 to 0.91). Analysis of macular blood flow parameters showed that, in the SCP, the inferior inner-ring vessel density in the HMG group was (44.5±5.2)%, lower than that in the HM group (47.8±3.9)%, and the difference between groups was statistically significant (t=5.57, adjusted P<0.05); its discriminatory performance was the best among all SCP sectors, with an AUC of 0.69 (95%CI: 0.62 to 0.76). At the DCP level, there were no statistically significant differences between the two groups in any inner-ring or outer-ring sector (t=1.11, 1.93, 0.69, 0.99, 1.73, 2.20, 0.64, 0.93; adjusted P>0.05). Based on the screening results of single indicators, stepwise multivariable logistic regression models were constructed. Model 1 included inferior peripapillary RNFL thickness and had an AUC of 0.850 (95%CI: 0.810 to 0.890). Compared with Model 1, Model 2 additionally included GLV, and the AUC increased to 0.928 (95%CI: 0.895 to 0.961), with a statistically significant difference compared with Model 1 (DeLong test P<0.05). Compared with Model 2, Model 3 additionally included inner inferior macular SCP capillary density, and the AUC increased to 0.946 (95%CI: 0.918 to 0.974), with a statistically significant difference (DeLong test P<0.05).

Inferior peripapillary RNFL thickness, macular GLV, and inferior inner-ring macular SCP vessel density all have good value for glaucoma discrimination in HM patients. The combination of multimodal structural and blood flow parameters can significantly improve diagnostic performance and provide a basis for the auxiliary diagnosis of HM combined with OAG.