Browsing by Author "Sidoti, Paul A."

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    Diagnostic Capability of OCTA-Derived Macular Biomarkers for Early to Moderate Primary Open Angle Glaucom
    (MDPI, 2024-07-18) Verticchio Vercellin, Alice; Harris, Alon; Oddone, Francesco; Carnevale, Carmela; Siesky, Brent A.; Arciero, Julia; Fry, Brendan; Eckert, George; Sidoti, Paul A.; Antman, Gal; Alabi, Denise; Coleman-Belin, Janet C.; Pasquale, Louis R.; Mathematical Sciences, School of Science
    Background/Objectives: To investigate macular vascular biomarkers for the detection of primary open-angle glaucoma (POAG). Methods: A total of 56 POAG patients and 94 non-glaucomatous controls underwent optical coherence tomography angiography (OCTA) assessment of macular vessel density (VD) in the superficial (SCP), and deep (DCP) capillary plexus, foveal avascular zone (FAZ) area, perimeter, VD, choriocapillaris and outer retina flow area. POAG patients were classified for severity based on the Glaucoma Staging System 2 of Brusini. ANCOVA comparisons adjusted for age, sex, race, hypertension, diabetes, and areas under the receiver operating characteristic curves (AUCs) for POAG/control differentiation were compared using the DeLong method. Results: Global, hemispheric, and quadrant SCP VD was significantly lower in POAG patients in the whole image, parafovea, and perifovea (p < 0.001). No significant differences were found between POAG and controls for DCP VD, FAZ parameters, and the retinal and choriocapillaris flow area (p > 0.05). SCP VD in the whole image and perifovea were significantly lower in POAG patients in stage 2 than stage 0 (p < 0.001). The AUCs of SCP VD in the whole image (0.86) and perifovea (0.84) were significantly higher than the AUCs of all DCP VD (p < 0.05), FAZ parameters (p < 0.001), and retinal (p < 0.001) and choriocapillaris flow areas (p < 0.05). Whole image SCP VD was similar to the AUC of the global retinal nerve fiber layer (RNFL) (AUC = 0.89, p = 0.53) and ganglion cell complex (GCC) thickness (AUC = 0.83, p = 0.42). Conclusions: SCP VD is lower with increasing functional damage in POAG patients. The AUC for SCP VD was similar to RNFL and GCC using clinical diagnosis as the reference standard.
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    Glaucoma Treatment Outcomes in Open-Angle Glaucoma Patients of African Descent
    (World Scientific, 2022) Siesky, Brent; Harris, Alon; Belamkar, Aditya; Zukerman, Ryan; Horn, Avery; Verticchio Vercellin, Alice; Mendoza, Kristen Ann V.; Sidoti, Paul A.; Oddone, Francesco; Ophthalmology, School of Medicine
    Open angle glaucoma (OAG), characterized by structural changes to the optic nerve head and retinal nerve fiber layer, is a progressive multifactorial optic neuropathy and leading cause of irreversible blindness globally. Currently intraocular pressure is the only modifiable risk factor; however, others have been identified including genetics and race. Importantly, OAG is much more prevalent in persons of African descent (AD) compared to those of European descent (ED). OAG patients of AD are also known to have a more severe course of the disease, a finding potentially explained by structural and/or vascular differences within eye tissues. In addition, disparities in treatment outcomes have been identified in OAG patients of AD. Specifically, prostaglandin analogues have been suggested to be more effective in patients of AD than in those ED, while beta-adrenergic receptors have been suggested to be less effective, although the evidence is inconsistent. Being of AD has also been identified as a risk factor for trabeculectomy failure while laser trabeculoplasty, has been conversely found to be very effective in lowering IOP in patients of AD. Alternative surgical options including Ex-Press shunt implantation, viscocanalostomy, and canaloplasty are promising in equivalence but require further research to properly evaluate disparity in outcomes. In addition to treatment outcomes, social disparities affecting clinical care also exist for persons of AD in the form of reduced adherence, access, and choice. Overall, data suggests the need for properly designed prospective trials with AD populations as a primary focus to identify the potential mechanisms driving disparities in treatment and address overall potential bias in glaucoma management.
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    Using Multi-Layer Perceptron Driven Diagnosis to Compare Biomarkers for Primary Open Angle Glaucoma
    (Association for Research in Vision and Ophthalmology, 2024) Riina, Nicholas; Harris, Alon; Siesky, Brent A.; Ritzer, Lukas; Pasquale, Louis R.; Tsai, James C.; Keller, James; Wirostko, Barbara; Arciero, Julia; Fry, Brendan; Eckert, George; Verticchio Vercellin, Alice; Antman, Gal; Sidoti, Paul A.; Guidoboni, Giovanna; Mathematical Sciences, School of Science
    Purpose: To use neural network machine learning (ML) models to identify the most relevant ocular biomarkers for the diagnosis of primary open-angle glaucoma (POAG). Methods: Neural network models, also known as multi-layer perceptrons (MLPs), were trained on a prospectively collected observational dataset comprised of 93 glaucoma patients confirmed by a glaucoma specialist and 113 control subjects. The base model used only intraocular pressure, blood pressure, heart rate, and visual field (VF) parameters to diagnose glaucoma. The following models were given the base parameters in addition to one of the following biomarkers: structural features (optic nerve parameters, retinal nerve fiber layer [RNFL], ganglion cell complex [GCC] and macular thickness), choroidal thickness, and RNFL and GCC thickness only, by optical coherence tomography (OCT); and vascular features by OCT angiography (OCTA). Results: MLPs of three different structures were evaluated with tenfold cross validation. The testing area under the receiver operating characteristic curve (AUC) of the models were compared with independent samples t-tests. The vascular and structural models both had significantly higher accuracies than the base model, with the hemodynamic AUC (0.819) insignificantly outperforming the structural set AUC (0.816). The GCC + RNFL model and the model containing all structural and vascular features were also significantly more accurate than the base model. Conclusions: Neural network models indicate that OCTA optic nerve head vascular biomarkers are equally useful for ML diagnosis of POAG when compared to OCT structural biomarker features alone.