Kouros Nouri-Mahdavi1, Vahid Mohammadzadeh2, Alessandro Rabiolo3, Kiumars Edalati2, Joseph Caprioli2, Siamak Yousefi4. 1. Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, California, USA. Electronic address: nouri-mahdavi@jsei.ucla.edu. 2. Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, California, USA. 3. Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, California, USA; Department of Ophthalmology, Gloucestershire Hospitals National Health Service Foundation Trust, Cheltenham, United Kingdom. 4. Departments of Ophthalmology and Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA.
Abstract
PURPOSE: To test the hypothesis that visual field (VF) progression can be predicted from baseline and longitudinal optical coherence tomography (OCT) structural measurements. DESIGN: Prospective cohort study. METHODS: A total of 104 eyes (104 patients) with ≥3 years of follow-up and ≥5 VF examinations were enrolled. We defined VF progression based on pointwise linear regression on 24-2 VF (≥3 locations with slope less than or equal to -1.0 dB/year and P < .01). We used elastic net logistic regression (ENR) and machine learning to predict VF progression with demographics, baseline circumpapillary retinal nerve fiber layer (RNFL), macular ganglion cell/inner plexiform layer (GCIPL) thickness, and RNFL and GCIPL change rates at central 24 superpixels and 3 eccentricities, 3.4°, 5.5°, and 6.8°, from fovea and hemimaculas. Areas-under-ROC curves (AUC) were used to compare models. RESULTS: Average ± SD follow-up and VF examinations were 4.5 ± 0.9 years and 8.7 ± 1.6, respectively. VF progression was detected in 23 eyes (22%). ENR selected rates of change of superotemporal RNFL sector and GCIPL change rates in 5 central superpixels and at 3.4° and 5.6° eccentricities as the best predictor subset (AUC = 0.79 ± 0.12). Best machine learning predictors consisted of baseline superior hemimacular GCIPL thickness and GCIPL change rates at 3.4° eccentricity and 3 central superpixels (AUC = 0.81 ± 0.10). Models using GCIPL-only structural variables performed better than RNFL-only models. CONCLUSIONS: VF progression can be predicted with clinically relevant accuracy from baseline and longitudinal structural data. Further refinement of proposed models would assist clinicians with timely prediction of functional glaucoma progression and clinical decision making.
PURPOSE: To test the hypothesis that visual field (VF) progression can be predicted from baseline and longitudinal optical coherence tomography (OCT) structural measurements. DESIGN: Prospective cohort study. METHODS: A total of 104 eyes (104 patients) with ≥3 years of follow-up and ≥5 VF examinations were enrolled. We defined VF progression based on pointwise linear regression on 24-2 VF (≥3 locations with slope less than or equal to -1.0 dB/year and P < .01). We used elastic net logistic regression (ENR) and machine learning to predict VF progression with demographics, baseline circumpapillary retinal nerve fiber layer (RNFL), macular ganglion cell/inner plexiform layer (GCIPL) thickness, and RNFL and GCIPL change rates at central 24 superpixels and 3 eccentricities, 3.4°, 5.5°, and 6.8°, from fovea and hemimaculas. Areas-under-ROC curves (AUC) were used to compare models. RESULTS: Average ± SD follow-up and VF examinations were 4.5 ± 0.9 years and 8.7 ± 1.6, respectively. VF progression was detected in 23 eyes (22%). ENR selected rates of change of superotemporal RNFL sector and GCIPL change rates in 5 central superpixels and at 3.4° and 5.6° eccentricities as the best predictor subset (AUC = 0.79 ± 0.12). Best machine learning predictors consisted of baseline superior hemimacular GCIPL thickness and GCIPL change rates at 3.4° eccentricity and 3 central superpixels (AUC = 0.81 ± 0.10). Models using GCIPL-only structural variables performed better than RNFL-only models. CONCLUSIONS: VF progression can be predicted with clinically relevant accuracy from baseline and longitudinal structural data. Further refinement of proposed models would assist clinicians with timely prediction of functional glaucoma progression and clinical decision making.
Authors: Siamak Yousefi; Michael H Goldbaum; Madhusudhanan Balasubramanian; Tzyy-Ping Jung; Robert N Weinreb; Felipe A Medeiros; Linda M Zangwill; Jeffrey M Liebmann; Christopher A Girkin; Christopher Bowd Journal: IEEE Trans Biomed Eng Date: 2014-04 Impact factor: 4.538
Authors: Wolfgang A Schrems; Laura-M Schrems-Hoesl; Christian Y Mardin; Robert Laemmer; Friedrich E Kruse; Folkert K Horn Journal: J Glaucoma Date: 2017-04 Impact factor: 2.503
Authors: Allison K Ungar; Gadi Wollstein; Hiroshi Ishikawa; Lindsey S Folio; Yun Ling; Richard A Bilonick; Robert J Noecker; Juan Xu; Larry Kagemann; Cynthia Mattox; Joel S Schuman Journal: Ophthalmic Surg Lasers Imaging Date: 2012-05-31
Authors: Christopher Bowd; Linda M Zangwill; Robert N Weinreb; Felipe A Medeiros; Akram Belghith Journal: Am J Ophthalmol Date: 2016-11-30 Impact factor: 5.258
Authors: Carlos Gustavo de Moraes; Christian Song; Jeffrey M Liebmann; Joseph L Simonson; Rafael L Furlanetto; Robert Ritch Journal: Ophthalmology Date: 2013-11-28 Impact factor: 12.079
Authors: Alessandro Rabiolo; Federico Fantaguzzi; Riccardo Sacconi; Francesco Gelormini; Enrico Borrelli; Giacinto Triolo; Paolo Bettin; Andrew I McNaught; Joseph Caprioli; Giuseppe Querques; Francesco Bandello Journal: Transl Vis Sci Technol Date: 2021-12-01 Impact factor: 3.283
Authors: Vahid Mohammadzadeh; Erica Su; Lynn Shi; Anne L Coleman; Simon K Law; Joseph Caprioli; Robert E Weiss; Kouros Nouri-Mahdavi Journal: Ophthalmol Sci Date: 2022-06-16