April 2010
Volume 51, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2010
Discrimination of Fast and Slow Components of Visual Field Changes in Glaucoma
Author Affiliations & Notes
  • D. A. Mock
    Opthalmalogy, Jules Stein Eye Institute, Los Angeles, California
  • A. Coleman
    Opthalmalogy, Jules Stein Eye Institute, Los Angeles, California
  • A. Afifi
    Opthalmalogy, Jules Stein Eye Institute, Los Angeles, California
  • E. Bitrian
    Opthalmalogy, Jules Stein Eye Institute, Los Angeles, California
  • F. Yu
    Opthalmalogy, Jules Stein Eye Institute, Los Angeles, California
  • K. Nouri-Madhavi
    Opthalmalogy, Jules Stein Eye Institute, Los Angeles, California
  • Y. Shaikh
    Opthalmalogy, Jules Stein Eye Institute, Los Angeles, California
  • J. Caprioli
    Opthalmalogy, Jules Stein Eye Institute, Los Angeles, California
  • Footnotes
    Commercial Relationships  D.A. Mock, None; A. Coleman, Alcon Allergan Pfizer, C; A. Afifi, None; E. Bitrian, None; F. Yu, None; K. Nouri-Madhavi, None; Y. Shaikh, None; J. Caprioli, Alcon Allergan Pfizer, C.
  • Footnotes
    Support  Pfizer
Investigative Ophthalmology & Visual Science April 2010, Vol.51, 5502. doi:
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      D. A. Mock, A. Coleman, A. Afifi, E. Bitrian, F. Yu, K. Nouri-Madhavi, Y. Shaikh, J. Caprioli; Discrimination of Fast and Slow Components of Visual Field Changes in Glaucoma. Invest. Ophthalmol. Vis. Sci. 2010;51(13):5502.

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

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Abstract
 
Purpose:
 

To establish mathematical models, insensitive to the diffuse effects due to cataracts, that statistically discriminate fast from slow regional rates of progressionfrom glaucoma in visual field series.

 
Methods:
 

We analyzed longitudinal visual field (VF) series (SAP 24-2) in pseudophakic patients treated for primary-open angle glaucoma with progressive glaucoma (POAG) and phakic patients with no glaucomatous damage but progressive cataract (CAT). Progression rates are calculated with an exponentially fitted model. From previously archived VF databases with known patterns of progression, we developed algorithms that differentiate fast from slow regions of progression. A regression-based cluster analysis of the progression rates checked the model for relevance with prior knowledge. We used maximally ranked statistics, adjusted with multiple testing, that provided field-dependent cutoffs that partition the rates into fast and slow clusters. The cutoffs were determined by finding the minimum p-value that was generated under an adjusted statistical test for each possible subgroup of ranked rates (min size=3). The mean was then calculated for the partitioned subgroups. With the same procedure, we compared the progression rates in POAG and CAT patients.

 
Results:
 

The distribution of differences between fast and slow regional rates is statistically different between POAG [n= 67, mean follow-up time=8.3 yrs, mean difference fast&slow=0.23 dB/yr, sd=0.12, kurtosis= -0.25] and CAT [n=20, mean follow-up=7.4 yrs, mean difference fast&slow=0.11 dB/yr, sd=.10, kurtosis=2.5], unpaired t-test p<.00001.

 
Conclusions:
 

The regional progression rates in CAT are more homogeneous and the regional progression rates in POAG are more heterogeneous. This approach may be a useful method to help separate the components of glaucomatous and cataractous visual field change in patients with glaucoma and provide specific regional rates of deterioration from glaucoma.  

 
Keywords: visual fields • visual impairment: neuro-ophthalmological disease • visual acuity 
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