April 2011
Volume 52, Issue 14
Free
ARVO Annual Meeting Abstract  |   April 2011
Partitioning Fast and Slow Components of Visual Field Decay in Glaucoma
Author Affiliations & Notes
  • Joseph Caprioli
    Glaucoma, Jules Stein Eye Institute, UCLA, Los Angeles, California
  • Dennis Mock
    Glaucoma, Jules Stein Eye Institute, UCLA, Los Angeles, California
  • Elena Bitrian
    Glaucoma, Jules Stein Eye Institute, UCLA, Los Angeles, California
  • Adelmonem Afifi
    Biostatistics, UCLA School of Public Health, Los Angeles, California
  • Fei Yu
    Glaucoma, Jules Stein Eye Institute, Los Angeles, California
  • Kouros Nouri-Mahdavi
    Glaucoma, Jules Stein Eye Institute, Los Angeles, California
  • Anne L. Coleman
    Glaucoma, Jules Stein Eye Institute, Los Angeles, California
  • Footnotes
    Commercial Relationships  Joseph Caprioli, Alcon (F), Allergan (F), Pfizer (F), Research to Prevent Blindness (F); Dennis Mock, Oppenheimer Research Grant (F); Elena Bitrian, Oppenheimer Research Grant (F); Adelmonem Afifi, None; Fei Yu, None; Kouros Nouri-Mahdavi, None; Anne L. Coleman, Alcon (F), Allergan (F), Pfizer (F), Research to Prevent Blindness (F)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science April 2011, Vol.52, 4410. doi:
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      Joseph Caprioli, Dennis Mock, Elena Bitrian, Adelmonem Afifi, Fei Yu, Kouros Nouri-Mahdavi, Anne L. Coleman; Partitioning Fast and Slow Components of Visual Field Decay in Glaucoma. Invest. Ophthalmol. Vis. Sci. 2011;52(14):4410.

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

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Abstract
 
Purpose:
 

To confirm, in a separate glaucoma visual field (VF) database, a previously reported model that statistically partitions test locations into fast and slow decay rates for a clinic population with less severe VF damage.

 
Methods:
 

We analyzed longitudinal visual field (VF) series of patients treated for primary-open angle glaucoma (POAG) from a database of patients at UCLA (n=517). With an exponential model, we calculated the rate of decay of 54 test locations (SAP 24-2) in the VF series for each eye and partitioned these rates into fast and slow components. We calculated the mean decay rate as a %/year all locations, the fast and slow components, and the MD. For worsening test locations, fast and slow rates were plotted against MD rate."Fast progressors" were defined as eyes with a fast component half-life of ≤ 5 years.

 
Results:
 

The mean age and MD for the group were [62.9 ± 11.3 years; initial MD: -5.2 ± 5.5dB, final MD: -6.4 ± 7.1dB]. Mean follow-up was 13.1 ± 6.7 years. The histograms of the slow and fast component rates are shown in the Figure. For all decaying VFs, the rates of VF decay for the overall, slow and fast components were 5.2, 2.3 and 20.2 %/year, resp. The plots of the slow and fast rates vs ΔMD (Figure 2) demonstrates that the slow component aligns with MD values (slope=1.0, p<.01), while the fast component is largely independent of MD (slope=0.05, p<.01). The number of "fast progressors" was 255 (49%).

 
Conclusions:
 

The initial model was developed and tested in the AGIS dataset of moderate to severe VF loss (ARVO 2010). Here we test the model in a separate, less severely affected group. VF decay rates can be separated into a slow component (diffuse, non-specific loss that includes aging and media effects) that aligns with MD change and a fast component (more focal, mostly glaucomatous loss) that is less sensitive to changes in MD. This method operates smoothly across a wide range of VF severity, can identify regions of the VF that are decaying rapidly, and can identify patients who are "fast progressors".  

 
Keywords: visual fields • perimetry 
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