April 2014
Volume 55, Issue 13
ARVO Annual Meeting Abstract  |   April 2014
Estimating Health State Utility Values Associated With AMD Using A Combined Visual Function Algorithm
Author Affiliations & Notes
  • Thomas Butt
    UCL Institute of Ophthalmology, London, United Kingdom
  • Gary S Rubin
    UCL Institute of Ophthalmology, London, United Kingdom
  • Footnotes
    Commercial Relationships Thomas Butt, None; Gary Rubin, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science April 2014, Vol.55, 180. doi:
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      Thomas Butt, Gary S Rubin; Estimating Health State Utility Values Associated With AMD Using A Combined Visual Function Algorithm. Invest. Ophthalmol. Vis. Sci. 2014;55(13):180.

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

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Purpose: Clinical measures of visual function (visual acuity and contrast sensitivity) have been individually associated with health state utility values for calculating quality-adjusted life years (QALYs) in order to estimate the cost-effectiveness of interventions to treat age-related macular degeneration (AMD). We developed algorithms combining visual acuity, contrast sensitivity and visual field, in order to better predict utility values in AMD using baseline data from 81 patients with AMD enrolled in the Eccentric Fixation From Enhanced Clinical Training (EFFECT) randomised control trial.

Methods: Utility values were calculated for the patient’s health state using the EQ-5D questionnaire. Visual acuity (VA) was measured using an ETDRS letter chart, contrast sensitivity (CS) was measured using a MARS chart, retinal sensitivity was measured using a Nidek MP-1 microperimeter. Multiple ordinary least squares (OLS) and Tobit regression models were developed to associate individual and combined measures of visual function with utility. Independent variables included in the models were VA in the better eye, binocular CS, proportion of points seen in better eye microperimetry, age, gender and diagnosis (wet or dry).

Results: Both the multiple OLS regression model and Tobit regression model predicted utility (p<0.01). The OLS regression explained around 27% of the model variance. In the Tobit regression model, VA in the better eye and diagnosis were significant predictors of utility (p<0.05).

Conclusions: The algorithms may be applied to calculate improved estimates of utility when assessing the cost-effectiveness of treatments for AMD. While VA appears to be the strongest predictor of utility, other variables should be considered for accurate estimates of cost-effectiveness in AMD.

Keywords: 460 clinical (human) or epidemiologic studies: health care delivery/economics/manpower • 669 quality of life • 412 age-related macular degeneration  

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