September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
Visual field analysis tools for real world clinics
Author Affiliations & Notes
  • Susan R Bryan
    Optometry and Visual Science, City University London, London, United Kingdom
  • David Crabb
    Optometry and Visual Science, City University London, London, United Kingdom
  • Footnotes
    Commercial Relationships   Susan Bryan, None; David Crabb, Allergan PLC (R)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science September 2016, Vol.57, 3918. doi:
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      Susan R Bryan, David Crabb; Visual field analysis tools for real world clinics. Invest. Ophthalmol. Vis. Sci. 2016;57(12):3918.

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

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Abstract

Purpose : To demonstrate a new approach for assessing visual field progression in clinics using two easily understood parameters: Rate of Progression (RP: mean deviation [MD] loss [dB] per year) and Loss of Sight Years (LSY). These parameters are presented in a novel visualisation (Hedgehog Plot) and we aim to illustrate how they can be used to help determine patients requiring prioritised clinical care.

Methods : We used a subset of visual field series from 9884 patients in real clinics (Boodhna et al. Eye 2015). RP is calculated per eye using a two level hierarchical regression model (level 1: individual and level 2: eye). LSY is a novel parameter, linked to actuarial data, which estimates the number of years that a patient will have bilateral visual field loss worse than MD of -20dB in their predicted remaining lifetime. A reliability measure, Reliability of Rate (RR), is determined for each patient based on the variability in the MD recorded in both eyes while taking into account that the variability is expected to be higher for eyes with lower MD measurements (Crabb et al. IOVS 2012). Every eye is given a rank (percentile) within the sample based on RP and LSY allowing for ‘at risk’ patients to be easily identified.

Results : RP for every eye in a ‘clinic’ is shown in a Hedgehog Plot (Figure 1). Each line represents an eye with size indicating length of follow-up and location of the line is aligned to the patient’s age (x-axis) and severity of initial loss (y-axis); steeply declining lines indicate rapidly changing eyes. Eyes are ranked against all other eyes by RP and can be corrected for RR as a measure of reliability. The application allows the user to easily ‘drill down’ to individual patients and four are shown in Figure 2. LSY is given as a whole number in years and patients with LSY >0 can also be highlighted. A purpose written interactive application demonstrating the techniques is available: https://crabblab.shinyapps.io/hedgehog

Conclusions : RP and LSY can be ranked for all patients in a clinic in order to help identify worse cases of visual field progression without using inferential statistics. Hedgehog Plots provide a tool for clinicians to visualize all of their glaucoma patients simultaneously and could be helpful in prioritising clinical care.

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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