The Assessment of Morphological and Functional Changes in the Detection of the Initial Stage of Primary Glaucoma

Purpose — to study morphological and functional changes in the detection of primary glaucoma progression.Patients and methods. 128 patients (128 eyes, among them — 64 eyes with primary open angle glaucoma (POAG) and 64 with primary angle closure glaucoma (PACG)) with the initial MD of –6.0 dB were examined at the Ophthalmology Center of the FMBA of Russia from May 2016 to November 2019. The values of corneal-compensated IOP were also considered: minimal (IOPmin), peak (IOPmax) and its fluctuations (IOPfluct). The progression was measured using standard automated perimetry (SAP) and spectral-domain OCT (SD-OCT). During the observation period, each patient received the average of 8.42 ± 2.08 SAP and SD-OCT. Progressive thinning of the retinal nerve fiber layer (RNFL) and its ganglion cell complex (GCC) were evaluated using SD-OCT. If RNFL and/or GCC had a trend of significant (p < 0.05) thinning, the eye was classified as having the SD-OCT progression. The correlation between the rate of progression detected by SAP (ROP1) using thinning of RNFL (ROP2) and GCC (ROP3) with other clinical parameters was analyzed.Results and discussion. Glaucoma progression was detected in 73 eyes. While the isolated use of SAP did not allow detecting progression, it was possible to detect it in 39 % cases by SD-OCT. The combination of both methods allowed detecting progression in 57 %. In both forms, ROP1 correlated with IOPmin: in PACG r = 0.41, p = 0.023 and in POAG r = 0.43, p = 0.016. In PACG, ROP2 and ROP3 correlated with the foveal choroid thickness: r = 0.46, p = 0.019 and r = 0.47, p = 0.009, respectively. At the same time, ROP3 was associated with peak IOP (r = –0.402, p = 0.025); the correlation of peak IOP with its fluctuations amounted to 0.7 (p < 0.001).Conclusion. SD-OCT is more informative than SAP in determining the progression of the initial primary glaucoma. The combination of these two methods 1.5 times increases the possibility of detecting progression in comparison with the isolated use of SD-OCT. The choroid thickness, associated with the IOP fluctuations, plays an important role in the progression of PACG.

Progression in pediatric glaucoma: Lessons learnt from 8 years’ follow-up

Background: Surgical procedures are used as 1 of the main treatment modalities for pediatric glaucoma, even though progression may occur. In this study, we aimed to investigate the risk factors affecting the progression of pediatric glaucoma.Methods: In this retrospective cohort study, we reviewed the medical records of patients diagnosed with pediatric glaucoma between April 2009 and March 2017. Pediatric glaucoma patients who underwent regular follow-up for at least 1 year were included. Demographics, intraocular pressure (IOP), central corneal thickness (CCT), axial length (AL), cup-to-disc ratio (C/D ratio), corneal diameter, type of glaucoma, age at time ofdiagnosis, and age at surgery were recorded. Progression was defined as an increase in AL > 2 mm, C/D ratio > 0.2, or corneal diameter > 1 mm during 1 year of follow-up.Results: Eighty-three eyes from 46 patients were included: 37 eyes (45%) with primary congenital glaucoma (PCG), 46 eyes (55%) with secondary glaucoma, and 27 of these 83 eyes (32.5%) showed progression. Progression was comparable between eyes with PCG and secondary glaucoma (PCG, 22%; secondary glaucoma, 41%; P = 0.152). Age at the time of diagnosis and age at the time of the first surgery were significantly lower in the eyes with progression (P = 0.046 and 0.012, respectively). The mean ± standard deviation of surgeries in progressed versus non-progressed eyes was 1.88 ± 1.1 versus 1 ± 0.8 (P = 0.015). The frequency of comorbid systemic disease was significantly higher in patients with glaucoma progression (P = 0.043). The progressed and non-progressed eyes were comparable in terms of other demographic characteristics and ocular parameters (allP > 0.05).Conclusions: Pediatric glaucoma patients who were younger at the time of diagnosis and the first glaucoma surgery and those with comorbid systemic disease are at higher risk of glaucoma progression. These findings are useful for clinicians when counseling parents of children with pediatric glaucoma about disease outcomes. However, future prospective studies with larger sample sizes and longer follow-up periods are needed to confirm our findings.

Monitoring for glaucoma progression with SAP, electroretinography (PERG and PhNR) and OCT

Purpose To investigate the value of pattern electroretinography (PERG) and photopic negative response (PhNR) in monitoring glaucoma compared to standard clinical tests (standard automated perimetry (SAP) and clinical optic disc assessment) and structural measurements using spectral-domain OCT. Methods A prospective study included 32 subjects (32 eyes) with ocular hypertension, suspect or early glaucoma monitored for progression with clinical examination, SAP, PERG, PhNR and OCT for at least 4 years. Progression was defined clinically by the documented change of the optic disc and/or significant visual field progression (EyeSuite™ trend analysis). One eye per patient was included in the analysis. Results During the follow-up, 13 eyes (40.6%) showed progression, whereas 19 remained stable. In the progressing group, all parameters showed significant worsening over time, except for the PhNR, whereas in the stable group only the OCT parameters showed a significant decrease at the last visit. The trend of change over time using linear regression was steepest for the OCT parameters. At baseline, only the ganglion cell complex (GCC) and peripapillary retinal nerve fibre (pRNFL) thicknesses significantly discriminated between the stable and progressing eyes with the area under the ROC curve of 0.72 and 0.71, respectively. The inter-session variability for the first two visits in the stable group was lower for OCT (% limits of agreement within ± 17.4% of the mean for pRNFL and ± 3.6% for the GCC thicknesses) than for ERG measures (within ± 35.9% of the mean for PERG N95 and ± 59.9% for PhNR). The coefficient of variation for repeated measurements in the stable group was 11.9% for PERG N95 and 23.6% for the PhNR, while it was considerably lower for all OCT measures (5.6% for pRNFL and 1.7% for GCC thicknesses). Conclusions Although PERG and PhNR are sensitive for early detection of glaucomatous damage, they have limited usefulness in monitoring glaucoma progression in clinical practice, mainly due to high inter-session variability. On the contrary, OCT measures show low inter-session variability and might have a predicting value for early discrimination of progressing cases.

Dense Optic Nerve Head Deformation Estimated using CNN as a Structural Biomarker of Glaucoma Progression

Purpose: To present a new structural biomarker for detecting glaucoma progression based on structural transformation of the optic nerve head (ONH) region. Methods: A dense ONH deformation was estimated using deep learning methods namely DDCNet-Multires, FlowNet2, and FlowNet-Correlation, and legacy computational methods namely the topographic change analysis (TCA) and proper orthogonal decomposition (POD) methods using longitudinal confocal scans of the ONH for each study eye. A candidate structural biomarker of glaucoma progression in a study eye was estimated as average magnitude of flow velocities within the ONH region. The biomarker was evaluated using longitudinal confocal scans of 12 laser-treated and 12 contralateral normal eyes of 12 primates from the LSU Experimental Glaucoma Study (LEGS); and 36 progressing eyes and 21 longitudinal normal eyes from the UCSD Diagnostic Innovations in Glaucoma Study (DIGS). Area under the ROC curves (AUC) was used to assess the diagnostic accuracy of the candidate biomarker. Results: AUROC (95\% CI) for LEGS were: 0.83 (0.79, 0.88) for DDCNet-Multires; 0.83 (0.78, 0.88) for FlowNet2; 0.83 (0.78, 0.88) for FlowNet-Correlation; 0.94 (0.91, 0.97) for POD; and 0.86 (0.82, 0.91) for TCA methods. For DIGS: 0.89 (0.80, 0.97) for DDCNet-Multires; 0.82 (0.71, 0.93) for FlowNet2; 0.93 (0.86, 0.99) for FlowNet-Correlation; 0.86 (0.76, 0.96) for POD; and 0.86 (0.77, 0.95) for TCA methods. Lower diagnostic accuracy of the learning-based methods for LEG study eyes were due to image alignment errors in confocal sequences. Conclusion: Deep learning methods trained to estimate generic deformation were able to detect ONH deformation from confocal images and provided a higher diagnostic accuracy when compared to the classical optical flow and legacy biomarkers of glaucoma progression. Because it is difficult to validate the estimates of dense ONH deformation in clinical population, our validation using ONH sequences under controlled experimental conditions confirms the diagnostic accuracy of the biomarkers observed in the clinical population. Performance of these deep learning methods can be further improved by fine-tuning these networks using longitudinal ONH sequences instead of training the network to be a general-purpose deformation estimator.

Inter-Eye Correlation Analysis of 24-hour IOPs and Glaucoma Progression

Purpose: To determine whether 24-hour IOP monitoring can be a predictor for glaucoma progression and to analyze the inter-eye relationship of IOP, perfusion and progression parameters. Methods: We extracted data from manually drawn IOP curves with HIOP-Reader, a software suite we developed. The relationship between measured IOPs and mean ocular perfusion pressures (MOPP) to retinal nerve fiber layer (RNFL) thickness was analyzed. We determined the ROC curves for peak IOP (Tmax), average IOP (Tavg), IOP variation (IOPvar) and historical IOP cut-off levels to detect glaucoma progression (rate of RNFL loss). Bivariate analysis was conducted to check for various inter-eye relationships. Results: 217 eyes were included. The average IOP was 14.8&plusmn;3.5 mmHg, with a 24-hour variation of 5.2&plusmn;2.9 mmHg. 52% of eyes with RNFL data showed disease progression. There was no significant difference in Tmax, Tavg and IOPvar between progressors and non-progressors (all p&gt;0.05). Except for Tavg and the temporal RNFL, there was no correlation between disease progression in any quadrant, Tmax, Tavg and IOPvar. 24-hour and outpatient IOP variables had poor sensitivities and specificities in detecting disease progression. The correlation of inter-eye parameters was moderate; correlation with disease progression was weak. Conclusion: In line with our previous study, IOP data obtained during a single visit (outpatient or inpatient monitoring) make for a poor diagnostic tool, no matter the method deployed. Glaucoma progression and perfusion pressure in left and right eyes correlated weakly to moderately with each other.

HIOP-Reader: Automated Data Extraction for the Analysis of Manually Recorded Nycthemeral IOPs and Glaucoma Progression

Purpose: Nycthemeral (24-hour) glaucoma inpatient intraocular pressure (IOP) monitoring has been used in Europe for more than 100 years to detect peaks missed during regular office hours. Data supporting this practice is lacking, partially because it is difficult to correlate manually drawn IOP curves to objective glaucoma progression. To address this, we deployed automated IOP data extraction tools and tested for a correlation to a progressive retinal nerve fiber layer loss on spectral-domain optical coherence tomography (SDOCT). Methods: We created and deployed a machine-learning image analysis software to extract IOP data from hand-drawn, nycthemeral IOP curves of 225 retrospectively identified glaucoma patients. The relationship between demographic parameters, IOP and mean ocular perfusion pressure (MOPP) data to SDOCT data was analyzed. Sensitivities and specificities for the historical cut-off values of 15 mmHg and 22 mmHg in detecting glaucoma progression were calculated. Results: IOP data could be extracted efficiently. The IOP average was 15.2&plusmn;4.0 mmHg, nycthemeral IOP variation was 6.9&plusmn;4.2 mmHg, and MOPP was 59.1&plusmn;8.9 mmHg. Peak IOP occurred at 10 AM and trough at 9 PM. Disease progression occurred mainly in the temporal-superior and -inferior SDOCT sectors. No correlation could be established between demographic, IOP, or MOPP parameters and SDOCT disease progression. The sensitivity and specificity of both cut-off points (15 and 22 mmHg) were insufficient to be clinically useful. Outpatient IOPs were non-inferior to nycthemeral IOPs. Conclusion: IOP data obtained during a single visit make for a poor diagnostic tool, no matter whether obtained using nycthemeral measurements or during outpatient hours.