April 2014
Volume 55, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2014
Assessment of Age Effect in Structural and Functional Glaucoma Progression Analysis
Author Affiliations & Notes
  • Yu-Ying Liu
    College of Computing, Georgia Institute of Technology, Atlanta, GA
  • Hiroshi Ishikawa
    UPMC Eye Center, Eye and Ear Institute, Ophthalmology and Visual Science Research Center, Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA
    Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA
  • Gadi Wollstein
    UPMC Eye Center, Eye and Ear Institute, Ophthalmology and Visual Science Research Center, Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA
  • Richard Anthony Bilonick
    UPMC Eye Center, Eye and Ear Institute, Ophthalmology and Visual Science Research Center, Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA
    Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
  • James G Fujimoto
    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
  • Cynthia Mattox
    New England Eye Center, Tufts Medical Center, Boston, MA
  • Jay S Duker
    New England Eye Center, Tufts Medical Center, Boston, MA
  • Joel S Schuman
    UPMC Eye Center, Eye and Ear Institute, Ophthalmology and Visual Science Research Center, Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA
    Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA
  • James M Rehg
    College of Computing, Georgia Institute of Technology, Atlanta, GA
  • Footnotes
    Commercial Relationships Yu-Ying Liu, None; Hiroshi Ishikawa, None; Gadi Wollstein, None; Richard Bilonick, None; James Fujimoto, Zeiss (C), Zeiss (P); Cynthia Mattox, None; Jay Duker, None; Joel Schuman, Zeiss (C), Zeiss (P); James Rehg, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science April 2014, Vol.55, 988. doi:
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      Yu-Ying Liu, Hiroshi Ishikawa, Gadi Wollstein, Richard Anthony Bilonick, James G Fujimoto, Cynthia Mattox, Jay S Duker, Joel S Schuman, James M Rehg; Assessment of Age Effect in Structural and Functional Glaucoma Progression Analysis. Invest. Ophthalmol. Vis. Sci. 2014;55(13):988.

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

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Abstract
 
Purpose
 

To assess the effect of age in structural and functional glaucoma progression, using a novel progression analysis based on two-dimensional (2-D) state-based longitudinal models accounting for both structural and functional components.

 
Methods
 

Glaucoma suspect and glaucoma subjects were followed longitudinally. Optical coherence tomography (OCT) circumpapillary retinal nerve fiber layer (RNFL) thickness (proprietary prototype OCT, and OCT 1 and 2, Stratus OCT, Cirrus OCT (Zeiss)) and visual field index (VFI; Humphrey Field Analyzer; Zeiss) were measured at every visit. Calibration equations were employed to normalize RNFL thickness measurements across OCT machines as a one-parameter continuum. 2D continuous-time Hidden Markov Model using VFI and mean RNFL thickness for 2D disease state definition was employed to model progression. Cox proportional hazard model was used to estimate the effect of age in state transition rate.

 
Results
 

197 qualified eyes were followed for an average of 10.6+-5.0 years. 1 year of aging was associated with a 2.24% greater risk of functional loss (95% CI: 0.87%~3.61%), but the aging risk for structural loss was not statistically significant (0.84%: -0.18%~1.82%). The intricate and complex structure-function relationship was intuitively visualized as 2D state change plot (Figure). The age group [0-70] showed dominantly vertical state change (structural progression), while the age group [70-100] showed mainly horizontal state change (functional progression).

 
Conclusions
 

The proposed novel 2D progression analysis efficiently visualized and quantified non-linear relationship between structural and functional progression. Age-varying modeling using the 2D progression analysis may aid in more informed progression analysis and prediction.

 
 
Comparison of 2D Glaucoma Progression Map for Age Group [0-70] and [70-100]
 
Comparison of 2D Glaucoma Progression Map for Age Group [0-70] and [70-100]
 
Keywords: 473 computational modeling  
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