September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
Longitudinal Assessment of Non-standard Vision Function in Early to Intermediate AMD: Baseline Update
Author Affiliations & Notes
  • Lori A Lott
    Smith-Kettlewell Eye Research Institute, San Francisco, California, United States
  • Marilyn E Schneck
    Smith-Kettlewell Eye Research Institute, San Francisco, California, United States
    School of Optometry, UC Berkeley, Berkeley, California, United States
  • Gunilla Haegerstrom-Portnoy
    Smith-Kettlewell Eye Research Institute, San Francisco, California, United States
    School of Optometry, UC Berkeley, Berkeley, California, United States
  • Susan Hewlett
    Smith-Kettlewell Eye Research Institute, San Francisco, California, United States
  • Bonnie Gauer
    Bonnie M. Gauer, OD, MS, LLC, Roseburg, Oregon, United States
  • John A Brabyn
    Smith-Kettlewell Eye Research Institute, San Francisco, California, United States
  • Footnotes
    Commercial Relationships   Lori Lott, None; Marilyn Schneck, None; Gunilla Haegerstrom-Portnoy, None; Susan Hewlett, None; Bonnie Gauer, None; John Brabyn, None
  • Footnotes
    Support  NIH Grant EY023320, NIDILRR Grant 90RE5008-02-01
Investigative Ophthalmology & Visual Science September 2016, Vol.57, 624. doi:
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      Lori A Lott, Marilyn E Schneck, Gunilla Haegerstrom-Portnoy, Susan Hewlett, Bonnie Gauer, John A Brabyn; Longitudinal Assessment of Non-standard Vision Function in Early to Intermediate AMD: Baseline Update. Invest. Ophthalmol. Vis. Sci. 2016;57(12):624.

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

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Abstract

Purpose : The goal of this ongoing longitudinal study is to determine whether non-standard vision function tests predict disease progression in patients with early (E) or intermediate (I) age-related macular degeneration (AMD). Last year at ARVO, we reported that these measures show sensitivity to E- or I- AMD, with sufficient variation to be good candidates for predicting advanced AMD. This presentation will provide an update from the baseline wave of this longitudinal assessment of vision function in people with E- to I- AMD.

Methods : Participants are individuals with E- or I-AMD, or age-matched controls with no AMD (C). Best-corrected high contrast acuity is ≤0.30 logMAR (20/40 or better) in all eyes enrolled.
AMD status is confirmed by dilated eye exam with fundus photos. Each eye is categorized as E (medium drusen only), I (large drusen and/or AMD pigment abnormalities), or C (no drusen, or small drusen only) (Ferris, et al, 2013).
Participants are refracted and tested monocularly on a battery of vision tests with appropriate correction for the 40 cm viewing distance. The test battery includes: high contrast acuity under conditions of high and low luminance, low contrast acuity at low luminance, contrast sensitivity under high and low luminance conditions, shape discrimination hyperacuity (SDH), reading performance, color discrimination (desaturated D-15 color confusion score [DesatCCS]), 14 Hz flicker modulation sensitivity, and glare recovery.

Results : Preliminary results on the sample to date (N=91, mean age =72.9 yrs [SD=12.1], range: 55-95 years, 62% female) reveal statistically significant group differences between C and I-AMD on all vision measures. E-AMD performance on most measures fell between that of C and I-AMD. The greatest differences between C and I-AMD were seen for SDH (0.2 log difference) and DesatCCS (0.6 log difference). Furthermore, when categorized as pass/fail (failure ≥ 0.3 log [i.e. 2 times] worse than normal for each test), E- and I- AMD groups fail significantly more tests than C.

Conclusions : We hypothesize that non-standard vision tests can predict which E/I AMD patients will develop advanced AMD. A true test of this hypothesis will require several years of follow-up study to determine which participants convert to advanced disease. Preliminary data from this baseline sample suggest that these measures should allow better prediction of disease progression.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.

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