Outcomes of 45 µm gelatin stent surgery over 24-month follow-up.

BACKGROUND: The main objectives of this study were to determine whether known risk factors for trabeculectomy failure similarly influence gelatin stent outcomes and to identify surgical factors which may optimise success. METHODS: A retrospective, observational study was conducted at a single centre in Perth, Western Australia over 24 months. Two-hundred and sixty-two eyes of 207 patients underwent XEN-45 stent surgery with various forms of glaucoma. Surgical and postoperative data on subjects undergoing XEN-45 stent surgery was collated. Intraocular pressure (IOP) reduction success was determined using three criteria: 1; IOP <18 mm Hg, 2: IOP <15 mm Hg and 3: >25% IOP reduction from baseline. Kaplan-Meier, mixed effects Cox Proportional hazard model and Chi-Square test were used to measure survival of functioning stents. RESULTS: The success rates at a maximum of 2 years after surgery by criteria 1, 2 and 3 were 61.3%, 26.2% and 28.9% in primary open angle glaucoma (n = 243), 18.8%, 16.9%, 21.4% in angle closure glaucoma (n = 11), 0%, 0%, 66.7% in congenital glaucoma (n = 5) and 0% in uveitic glaucoma (n = 3). No significant reduction in success was found in those eyes that had prior ocular surgery (all p > 0.07). CONCLUSIONS: Prior cataract or trabeculectomy surgery does not appear to adversely affect gelatin stent outcomes over 2 years follow up. Gelatin stent surgery appears to have less IOP reduction effect compared to trabeculectomy at 2 years.

Probability density and information entropy of machine learning derived intracranial pressure predictions.

Even with the powerful statistical parameters derived from the Extreme Gradient Boost (XGB) algorithm, it would be advantageous to define the predicted accuracy to the level of a specific case, particularly when the model output is used to guide clinical decision-making. The probability density function (PDF) of the derived intracranial pressure predictions enables the computation of a definite integral around a point estimate, representing the event's probability within a range of values. Seven hold-out test cases used for the external validation of an XGB model underwent retinal vascular pulse and intracranial pressure measurement using modified photoplethysmography and lumbar puncture, respectively. The definite integral ±1 cm water from the median (DIICP) demonstrated a negative and highly significant correlation (-0.5213±0.17, p< 0.004) with the absolute difference between the measured and predicted median intracranial pressure (DiffICPmd). The concordance between the arterial and venous probability density functions was estimated using the two-sample Kolmogorov-Smirnov statistic, extending the distribution agreement across all data points. This parameter showed a statistically significant and positive correlation (0.4942±0.18, p< 0.001) with DiffICPmd. Two cautionary subset cases (Case 8 and Case 9), where disagreement was observed between measured and predicted intracranial pressure, were compared to the seven hold-out test cases. Arterial predictions from both cautionary subset cases converged on a uniform distribution in contrast to all other cases where distributions converged on either log-normal or closely related skewed distributions (gamma, logistic, beta). The mean±standard error of the arterial DIICP from cases 8 and 9 (3.83±0.56%) was lower compared to that of the hold-out test cases (14.14±1.07%) the between group difference was statistically significant (p<0.03). Although the sample size in this analysis was limited, these results support a dual and complementary analysis approach from independently derived retinal arterial and venous non-invasive intracranial pressure predictions. Results suggest that plotting the PDF and calculating the lower order moments, arterial DIICP, and the two sample Kolmogorov-Smirnov statistic may provide individualized predictive accuracy parameters.

Angiographic biomarkers are significant predictors of treatment response to intravitreal aflibercept in diabetic macular edema.

This prospective single-center study aims to identify biomarkers that predict improvement in best-corrected visual acuity (BCVA) and central retinal thickness (CRT) at 6 months, in 76 eyes with diabetic macular edema (DME) treated monthly with intravitreal aflibercept. At baseline, all patients underwent standardized imaging with color photography, optical coherence tomography (OCT), fluorescein angiography (FA) and OCT angiography (OCTA). Glycosylated hemoglobin, renal function, dyslipidemia, hypertension, cardiovascular disease and smoking were recorded. Retinal images were graded in a masked fashion. Baseline imaging, systemic and demographic variables were investigated to detect associations to BCVA and CRT change post aflibercept. Predictors of BCVA improvement included greater macular vessel density quantified using OCTA (p = 0.001) and low-density lipoprotein (LDL) ≥ 2.6 mmol/L (p = 0.017). Lower macular vessel density eyes showed a significant reduction in CRT but no BCVA improvement. Predictors of CRT reduction included peripheral non-perfusion seen on ultrawide-field FA (p = 0.005) and LDL ≥ 2.6 mmol/L (p < 0.001). Retinal angiographic biomarkers derived from OCTA and ultrawide-field FA may help predict functional and anatomic response to anti-vascular endothelial growth factor (VEGF) therapy in patients with DME. Elevated LDL is associated with treatment response in DME. These results may be used to better-select patients who will benefit from intravitreal aflibercept for treatment of DME.

Empirical retinal venous pulse wave velocity using modified photoplethysmography.

OBJECTIVE: Using the novel imaging method of high-speed modified photoplethysmography we measured the retinal venous pulse wave velocity in a single case. RESULTS: A healthy 30-year-old subject underwent high-speed modified photoplethysmography (120 frames per second) with simultaneous ophthalmodynamometry at 26 Meditron units. A video of the optic nerve was analyzed using custom software. A harmonic regression model was fitted to each pixel in the time series and used to quantify the retinal vascular pulse wave parameters. Retinal venous pulsation at the optic disc was observed as a complex dynamic wall motion, whereas contraction commenced at a point in the vein at the center of the optic disc, and progressed centrifugally. The empirically estimated retinal venous pulse wave velocity at this segment was approximately 22.24694 mm/s. This measurement provides an estimate for future studies in the field.

Posture-Induced Changes in Intraocular, Orbital, Cranial, Jugular Vein, and Arterial Pressures in a Porcine Model.

Purpose: The purpose of this study was to determine posture-induced changes in arterial blood pressure (ABP), intraocular pressure (IOP), orbital pressure (Porb), intracranial pressure (ICP), and jugular vein pressure (JVP) at various tilt angles in an in vivo pig. Methods: Anesthetized and ventilated pigs (n = 8) were placed prone on a tiltable operating table. ABP, IOP, Porb, ICP, and JVP were monitored while the table was tilted at various angles between 15 degrees head up tilt (HUT) and 25 degrees head down tilt (HDT) either in stepwise changes (5 degrees per step) or continuously. The mean pressure was calculated from digitized pressure waveforms from each compartment. For stepwise changes in tilt angle the pressures were plotted as a function of tilt angle. For continuous tilt changes, the pressures were plotted as a function of time. Results: In the case of stepwise changes, ABP remained relatively stable whilst IOP, Porb, ICP, and JVP demonstrated significant differences between most angles (typically P < 0.0001). The difference was greatest for IOP (P < 0.0001) where the average IOP increased from 13.1 ± 1.23 mm Hg at 15 degrees HUT to 46.3 ± 2.03 mm Hg at 25 degrees HDT. The relationship between pressure and tilt angle was almost linear for ICP and JVP, and sigmoidal for IOP and Porb. Interestingly, the effect of changes in tilt angle occurred very rapidly, within a few seconds. Conclusions: Our results in a pig model demonstrate that changes in posture (tilt angle) induce rapid changes in IOP, Porb, ICP, and JVP, with IOP affected most severely.

MSGANet-RAV: A multiscale guided attention network for artery-vein segmentation and classification from optic disc and retinal images.

BACKGROUND: Retinal and optic disc images are used to assess changes in the retinal vasculature. These can be changes associated with diseases such as diabetic retinopathy and glaucoma or induced using ophthalmodynamometry to measure arterial and venous pressure. Key steps toward automating the assessment of these changes are the segmentation and classification of the veins and arteries. However, such segmentation and classification are still required to be manually labelled by experts. Such automated labelling is challenging because of the complex morphology, anatomical variations, alterations due to disease and scarcity of labelled data for algorithm development. We present a deep machine learning solution called the multiscale guided attention network for retinal artery and vein segmentation and classification (MSGANet-RAV). METHODS: MSGANet-RAV was developed and tested on 383 colour clinical optic disc images from LEI-CENTRAL, constructed in-house and 40 colour fundus images from the AV-DRIVE public dataset. The datasets have a mean optic disc occupancy per image of 60.6% and 2.18%, respectively. MSGANet-RAV is a U-shaped encoder-decoder network, where the encoder extracts multiscale features, and the decoder includes a sequence of self-attention modules. The self-attention modules explore, guide and incorporate vessel-specific structural and contextual feature information to segment and classify central optic disc and retinal vessel pixels. RESULTS: MSGANet-RAV achieved a pixel classification accuracy of 93.15%, sensitivity of 92.19%, and specificity of 94.13% on LEI-CENTRAL, outperforming several reference models. It similarly performed highly on AV-DRIVE with an accuracy, sensitivity and specificity of 95.48%, 93.59% and 97.27%, respectively. CONCLUSION: The results show the efficacy of MSGANet-RAV for identifying central optic disc and retinal arteries and veins. The method can be used in automated systems designed to assess vascular changes in retinal and optic disc images quantitatively.