July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
Predicting Functional Progression in Glaucoma from Baseline Visual Fields
Author Affiliations & Notes
  • Mengyu Wang
    Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts, United States
  • Louis R. Pasquale
    Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States
  • Lucy Q Shen
    Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States
  • Michael V Boland
    Wilmer Eye Institute, Johns Hopkins University, Baltimore, Maryland, United States
  • Sarah R. Wellik
    Bascom Palmer Eye Institute, University of Miami, Miami, Florida, United States
  • C Gustavo De Moraes
    Edward S. Harkness Eye Institute, Columbia University , New York, New York, United States
  • Jonathan S. Myers
    Wills Eye Hospital, Thomas Jefferson University, Philadelphia, Pennsylvania, United States
  • Neda Baniasadi
    Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts, United States
  • Dian Li
    Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts, United States
  • Hui Wang
    Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts, United States
    Institute for Psychology and Behavior, Jilin University of Finance and Economics, Changchun, Jilin, China
  • Peter J. Bex
    Department of Psychology, Northeastern University, Boston, Massachusetts, United States
  • Tobias Elze
    Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts, United States
  • Footnotes
    Commercial Relationships   Mengyu Wang, None; Louis Pasquale, Alcon (C), Bausch+Lomb (C), Eyenovia (C); Lucy Shen, Genentech (C), Topcon (F); Michael Boland, Alcon (F); Sarah Wellik, None; C Gustavo De Moraes, None; Jonathan Myers, None; Neda Baniasadi, None; Dian Li, None; Hui Wang, None; Peter Bex, United States PCT/US2014/052414 (P); Tobias Elze, United States PCT/US2014/052414 (P)
  • Footnotes
    Support  Lions Foundation; Grimshaw-Gudewicz Foundation; Unrestricted Grant from Research to Prevent Blindness (Mass. Eye and Ear, Harvard Ophthalmology); BrightFocus Foundation; NEI Core Grant P30EYE003790; NEI R01 EY015473; Jilin University of Finance and Economics: No. 2017Z02; Unrestricted Grant from Research to Prevent Blindness (Bascom Palmer Eye Institute); NIH Center Core Grant P30EY014801; Unrestricted Grant from Research to Prevent Blindness (Department of Ophthalmology, Columbia University Medical Center)
Investigative Ophthalmology & Visual Science July 2018, Vol.59, 5107. doi:
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    • Get Citation

      Mengyu Wang, Louis R. Pasquale, Lucy Q Shen, Michael V Boland, Sarah R. Wellik, C Gustavo De Moraes, Jonathan S. Myers, Neda Baniasadi, Dian Li, Hui Wang, Peter J. Bex, Tobias Elze; Predicting Functional Progression in Glaucoma from Baseline Visual Fields. Invest. Ophthalmol. Vis. Sci. 2018;59(9):5107.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : To predict glaucomatous visual field (VF) worsening after at least 5 years using features of 3 baseline VFs.

Methods : In this retrospective multi-center cohort study, eyes with ≥ 5 reliable automated VFs and ≥ 5 years follow-up were selected. We also restricted the time between each follow-up VFs to be ≥ 6 months, the time between the first and third VFs to be ≤ 3 years, and glaucoma hemifield test (GHT) to be within/outside normal limits or borderline. VF features are extracted: (1) age, follow-up time, mean deviation (MD) and pattern standard deviation (PSD) of the 3rd VF, (2) GHT results, MD slope, PSD slope and intraclass correlation for the 3 baseline VFs, and (3) worsening VF locations using permutation of pointwise linear regression (PoPLR, slope<-1 dB/year and p<0.01) and the Collaborative Initial Glaucoma Treatment Study (CIGTS) score of the 3rd VF based on the 3 baselines. Worsening VFs were determined by CIGTS criteria, MD regression (minus slope, p<0.01) and PoPLR (≥3 progressed locations). The time of progression was defined as the follow-up time when progression was first detected with progression confirmed on the full VF series. Cox regression was applied to model risk of worsening from VF features with stepwise model selection. 10-fold cross validation was applied to evaluate the model by the area under the receiver operating characteristic curve (AUC).

Results : 7,522 eyes were selected. The mean±standard deviation of age, MD and PSD at the 3rd baseline VF were 65.9±12.4 years, -4.4±5.5 dB and 4.4±3.8 dB. The median of total follow-up time and number of VFs were 6.7 years and 7. The median time to worsening for the methods of CIGTS, MD regression and PoPLR were 6.21 (671 eyes), 6.79 (793 eyes) and 5.91 (1,035 eyes) years, respectively. CIGTS progression was positively associated with age, MD, PSD slope, ICC and CIGTS score, and negatively associated with follow-up time at the 3rd VF and MD slope (Fig. 1 (a)). Abnormal GHT results of the second and 3rd VFs were associated with progression. The AUCs to predict 5-year progression defined by CIGTS (142 eyes), MD (95 eyes) and PoPLR (240 eyes) were 0.912, 0.830 and 0.819, respectively. Fig. 2 (a) and (b) show the optimal models to predict progression defined by MD and PoPLR.

Conclusions : Our model using 3 baseline VFs with mean follow-up time of 2.1 years accurately predicts the 5-year progression risk using various progression algorithms.

This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.

 

 

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