July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
A New Method to Detect Visual Field Progression based on Spatial Pattern Analysis
Author Affiliations & Notes
  • Dian Li
    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
  • 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
  • Mengyu Wang
    Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts, United States
  • Footnotes
    Commercial Relationships   Dian Li, None; Louis Pasquale, Alcon (S), Bausch+Lomb (C), Eyenovia (S); Lucy Shen, Genentech (C), Topcon (F); Michael Boland, Alcon (F); Sarah Wellik, None; C Gustavo De Moraes, None; Jonathan Myers, None; Neda Baniasadi, None; Hui Wang, None; Peter Bex, United States PCT/US2014/052414 (patent) (P); Tobias Elze, United States PCT/US2014/052414 (patent) (P); Mengyu Wang, None
  • 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, 6028. doi:
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      Dian Li, Louis R. Pasquale, Lucy Q Shen, Michael V Boland, Sarah R. Wellik, C Gustavo De Moraes, Jonathan S. Myers, Neda Baniasadi, Hui Wang, Peter J. Bex, Tobias Elze, Mengyu Wang; A New Method to Detect Visual Field Progression based on Spatial Pattern Analysis. Invest. Ophthalmol. Vis. Sci. 2018;59(9):6028.

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

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Abstract

Purpose : Current methods for detecting visual field (VF) change only provide global assessments of functional deterioration, while clinician’s judgement of VF progression heavily relies on examination of VF spatial patterns. We aim to develop a new algorithm to detect VF worsening to provide both status outcome and spatial patterns of VF progression.

Methods : In this retrospective cohort study, we selected eyes from multiple sites with at least 5 reliable automated VFs and at least 5 years follow-up using the SITA Standard strategy and 24-2 pattern. The time between each VF was restricted to be at least 6 months. Each VF was decomposed into a weighed sum of 16 VF archetypes (ATs) including 1 normal AT and 15 VF loss ATs (Fig. 1) identified previously [1]. For each eye, linear regressions were applied from follow-up time to the 16 AT weights of VFs. If any of the weights substantially changes for the 15 VF loss ATs (AT slope≥0.01/year and p<0.01) and the normal AT (AT slope≤-0.01/year and p<0.01), the eye is determined to be worsening. Our new algorithm was compared to existing algorithms including MD slope, Advanced Glaucoma Intervention Study (AGIS) scoring, Collaborative Initial Glaucoma Treatment Study (CIGTS) scoring and the permutation of pointwise linear regression (PoPLR). The concordance between those algorithms was evaluated by Kappa coefficient.

Results : 12,217 eyes were selected for analyses. The mean±standard deviation of age, MD and PSD at the first VF were 63.8±12.7 years, -4.1±5.2 dB and 3.9±3.5 dB. The median of follow-up time and number of VFs was 7.1 years and 6. The prevalence of VF progression by AT slope, MD slope, AGIS scoring, CIGTS scoring and PoPLR were 10.3%, 9.3%, 3.9%, 9.4% and 9.2%. The progression detection by AT slope was in fair agreement (Kappa 0.2 to 0.4) with MD slope (0.38), AGIS (0.23), CIGTS (0.25) and PoPLR (0.27). The overall Kappa coefficient between existing algorithms was 0.34. Among the progressed 1,262 eyes determined by AT analysis, 89.6%, 9.8% and 0.6% of the eyes had 1, 2 and 3 ATs progressed, and the 3 most frequent progressed ATs are AT 8 (17.3%), 6 (15.2%) and 3 (8.3%) (Fig. 2).

Conclusions : Our new algorithm based on AT analysis can provide information of spatial pattern of VF progression in addition to status outcome. The spatial patterns of VF progression can be used to assist clinicians to assess progression.

[1] Elze et al., 2015, J. R. Soc. Interface

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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