Combining Structural and Vascular Parameters to Discriminate Among Glaucoma Patients, Glaucoma Suspects, and Healthy Subjects

ALESSANDRO RABIOLO, F. Fantaguzzi, R. Sacconi, F. Gelormini, E. Borrelli, G. Triolo, P. Bettin, A. I. McNaught, J. Caprioli, G. Querques, F. Bandello

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Abstract

Purpose: Compare the ability of peripapillary and macular structural parameters, vascular parameters, and their integration to discriminate among glaucoma, suspected glaucoma (GS), and healthy controls (HCs). Methods: In this study, 196 eyes of 119 patients with glaucoma (n = 81), patients with GS (n = 48), and HCs (n = 67) underwent optical coherence tomography (OCT) and OCT angiography to measure peripapillary retinal nerve fiber layer (pRNFL), macular ganglion cell-inner plexiform layer (mGCIPL) thicknesses, radial peripapillary capillary perfusion density (RPC-PD), and macular GCIPL perfusion density (GCIPL-PD). Parameters were integrated regionally with logistic regression and globally with machine learning algorithms. Diagnostic performances were evaluated with area under the receiver operating characteristic (AUROC) curves. Results: Patients with glaucoma had mild to moderate damage (median, -3.3 dB; interquartile range, -6.5 to -1.4). In discriminating between patients with glaucoma and the HCs, pRNFL thickness had higher AUROC curve values than RPC-PD for average (0.87 vs. 0.62; P < 0.001), superior (0.86 vs. 0.54; P < 0.001), inferior (0.90 vs. 0.71; P < 0.001), and temporal (0.65 vs. 0.51; P = 0.02) quadrants. mGCIPL thickness had higher AUROC curve values than GCIPL-PD for average (0.84 vs. 0.68; P < 0.001), superotemporal (0.76 vs. 0.65; P = 0.016), superior (0.72 vs. 0.57; P = 0.004), superonasal (0.70 vs. 0.56; P = 0.01), inferotemporal (0.90 vs. 0.72; P < 0.001), inferior (0.87 vs. 0.69; P < 0.001), and inferonasal (0.78 vs. 0.65, P = 0.012) sectors. All structural multisector indices had higher diagnostic ability than vascular ones (P < 0.001). Combined structural-vascular indices did not outperform structural indices. Similar results were found to discriminate glaucoma from GS. Conclusions: Combining structural and vascular parameters in a structural-vascular index does not improve diagnostic ability over structural parameters alone. Translational Relevance: OCT angiography does not add additional benefit to structural OCT in early to moderate glaucoma diagnosis.
Lingua originaleInglese
pagine (da-a)20
RivistaTranslational Vision Science and Technology
Volume10
Numero di pubblicazione14
DOI
Stato di pubblicazionePubblicato - 2021

Keywords

  • peripapillary retinal nerve fiber layer
  • macular ganglion cell-inner plexiform layer
  • perfusion density
  • glaucoma diagnosis
  • machine learning

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