July 2019
Volume 60, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2019
Vision Modeling Prediction of Senofilcon-A with HYDRACLEAR® PLUS and Senofilcon-A with HydraLuxe™ Contact Lenses
Author Affiliations & Notes
  • Vrushali Korde
    R&D, Johnson & Johnson Vision Care, Jacksonville, Florida, United States
  • Xin Wei
    R&D, Johnson & Johnson Vision Care, Jacksonville, Florida, United States
  • Greg Hofmann
    R&D, Johnson & Johnson Vision Care, Jacksonville, Florida, United States
  • Pierre Gerligand
    R&D, Johnson & Johnson Vision Care, Jacksonville, Florida, United States
  • Philippe Jubin
    R&D, Johnson & Johnson Vision Care, Jacksonville, Florida, United States
  • Ben Wooley
    Artech Information Systems, LLC, Jacksonville, Florida, United States
  • Footnotes
    Commercial Relationships   Vrushali Korde, Johnson & Johnson Vision Care, Inc. (E); Xin Wei, Johnson & Johnson Vision Care (E); Greg Hofmann, Johnson & Johnson Vision Care, Inc. (E); Pierre Gerligand, Johnson & Johnson Vision Care, Inc. (E); Philippe Jubin, Johnson & Johnson Vision Care, Inc. (E); Ben Wooley, Johnson & Johnson Vision Care, Inc. (C)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 6356. doi:
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      Vrushali Korde, Xin Wei, Greg Hofmann, Pierre Gerligand, Philippe Jubin, Ben Wooley; Vision Modeling Prediction of Senofilcon-A with HYDRACLEAR® PLUS and Senofilcon-A with HydraLuxe™ Contact Lenses. Invest. Ophthalmol. Vis. Sci. 2019;60(9):6356.

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

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Abstract

Purpose : In silico prediction of clinically measured vision differences between contact lens products is challenging, but this capability would provide a deeper understanding of contact lens vision performance. A difference of 2.5, 95% confidence interval (1.65, 3.5) letters of High Luminance Low Contrast logMAR Visual Acuity was measured clinically between ACUVUE OASYS® with HYDRACLEAR® PLUS and ACUVUE OASYS® 1-Day with HydraLuxe™, with the latter providing better vision. A vision model simulated the contribution of optical manufacturing quality, lens decentration, and tear film surface quality to predict the clinically measured difference.

Methods : A Monte Carlo simulation was used to predict the vision performance reduction due to optical manufacturing quality and lens decentration. The optical manufacturing quality input distribution for the simulation was obtained using in-vitro interferometer wavefront measurements performed on contact lenses immersed in saline from the same contact lens lots used in the clinical study. The lens decentration input distribution was created such that the cumulative distribution function matched the clinical grading of the product being centered or slightly decentered, vs. substantially decentered. The eye distributions included a uniform residual refractive error of 0.25D in width and a normal distribution of primary spherical aberration (Z40) centered at 0.1 micron (6 mm pupil).

The tear film surface quality contribution to vision performance was independently calculated for each product using prior in-vivo tear surface interferometer clinical data. The resulting loss in VA was added to the Monte Carlo simulation results.

Results : The visual acuity difference between products when the optical manufacturing quality input distributions were used and decentration was assumed to be 0 was only 1.14 letters. When both optical manufacturing quality and lens decentration were included, the difference between products was 1.46 letters. After adding the tear film contribution, the predicted difference between the products is 1.79 letters, which falls within the 95% confidence interval of the clinical measurement (1.65, 3.5).

Conclusions : The model presented is a holistic approach to predicting vision performance and the results fall within the 95% confidence interval of the clinically measured difference, which adds confidence in the model.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

 

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