April 2014
Volume 55, Issue 13
ARVO Annual Meeting Abstract  |   April 2014
Predicting refractive error from ocular biometrics using structural equation modeling
Author Affiliations & Notes
  • Christopher Anderson Clark
    School of Optometry, University of Indiana, Bloomington, IN
  • Ann E Elsner
    School of Optometry, University of Indiana, Bloomington, IN
  • Benjamin J Konynenbelt
    School of Optometry, University of Indiana, Bloomington, IN
Investigative Ophthalmology & Visual Science April 2014, Vol.55, 3608. doi:
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      Christopher Anderson Clark, Ann E Elsner, Benjamin J Konynenbelt; Predicting refractive error from ocular biometrics using structural equation modeling. Invest. Ophthalmol. Vis. Sci. 2014;55(13):3608.

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

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Purpose: Previous work has shown that retinal differences may exist due to refractive error. As an example, total retinal thickness has been shown to be relatively thicker centrally and thinner peripherally for myopes compared to emmetropes. These differences may be due to effects from axial elongation or potential variables influencing refractive development. If these changes are correct, they should be able to predict refractive error in subjects.

Methods: Eighty subjects had a battery of tests performed including axial length, corneal topography, anterior chamber depth, peripheral refraction, peripheral partial coherence interferometry, and SD OCT for retinal thickness. The group was randomly split into two groups of forty subjects, one for model development and the other for model testing. Two designs were developed to predict central refractive error. The first group was the complete model using all available data. The second model was completed using only retinal thickness changes including thicknesses from the total retina (TRT), outer nuclear layer (ONL), outer plexiform, inner nuclear layer (INL), and the inner plexiform layer/ganglion cell layer. The second model had no data from axial length, corneal topography, etc. Structured equation modeling was done through SPSS (IBM, Endicott, NY.)

Results: Structural equation modeling using retinal layer thickness only to predict refractive error had an R2 = 0.273, P = 0.008. Layers contributing significantly to the model included the TRT, INL and ONL both centrally and peripherally. Using the full model, including the axial length, the model improved R2 = 0.698, P = 0.001. As expected, axial length was the primary contributor to the full model.

Conclusions: Differences in retinal thickness can be used to predict refractive error. This suggests that these differences are associated with refractive error and are real changes being detected. It appears like both central and peripheral retinal thickness differences may be important. Longitudinal work is needed to determine whether these differences are due to changes in refraction or if they may directly be influencing refractive development.

Keywords: 605 myopia • 677 refractive error development  

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