June 2021
Volume 62, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2021
Association of Environmental Factors with Neovascular Age-Related Macular Degeneration (nvAMD) using the Intelligent Research in Sight (IRIS)® Registry
Author Affiliations & Notes
  • Matthew Hunt
    Department of Ophthalmology, University of Washington, Seattle, Washington, United States
  • Steven Saraf
    Department of Ophthalmology, University of Washington, Seattle, Washington, United States
  • Yewlin Chee
    Department of Ophthalmology, University of Washington, Seattle, Washington, United States
  • Yue Wu
    Department of Ophthalmology, University of Washington, Seattle, Washington, United States
  • Cecilia S Lee
    Department of Ophthalmology, University of Washington, Seattle, Washington, United States
  • Aaron Y Lee
    Department of Ophthalmology, University of Washington, Seattle, Washington, United States
  • Michael B Manookin
    Department of Ophthalmology, University of Washington, Seattle, Washington, United States
  • Footnotes
    Commercial Relationships   Matthew Hunt, Boston Scientific, Patent ID: US20180228896A1 (P); Steven Saraf, None; Yewlin Chee, None; Yue Wu, None; Cecilia Lee, None; Aaron Lee, Carl Zeiss Meditec (F), Genentec (C), Microsoft (F), Novartis (F), NVIDIA (F), Santen (F), Topcon (R), US Food and Drug Administration (E), Verana Health (C); Michael Manookin, None
  • Footnotes
    Support  Financial support from Fight for Sight is gratefully acknowledged. Work supported by FFS Summer Student Fellowship (Matthew Hunt). NEI R01-EY027323 (Michael B Manookin); NIH/NIA R01AG060942 (Cecilia S. Lee); NIH/NEI K23EY029246 (Aaron Y. Lee); Latham Vision Innovation Award, and an unrestricted grant from Research to Prevent Blindness (Cecilia S. Lee and Aaron Y. Lee)
Investigative Ophthalmology & Visual Science June 2021, Vol.62, 165. doi:
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    • Get Citation

      Matthew Hunt, Steven Saraf, Yewlin Chee, Yue Wu, Cecilia S Lee, Aaron Y Lee, Michael B Manookin; Association of Environmental Factors with Neovascular Age-Related Macular Degeneration (nvAMD) using the Intelligent Research in Sight (IRIS)® Registry. Invest. Ophthalmol. Vis. Sci. 2021;62(8):165.

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

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Abstract

Purpose : Known AMD risks include demographic variables such as age, sex, and race, but the impact of environmental exposures is currently unclear. We investigate associations of environmental exposures with nvAMD development across the US.

Methods : All patients ≥ 55 years in the IRIS Registry during 2016-2018 (period of interest [POI]) were analyzed. Patients undergoing treatment for nvAMD were identified by nvAMD ICD code and CPT code for anti-VEGF intravitreal injections. Patients without provider-level zip codes matching any zip code tabular area were excluded. Environmental data was obtained from public sources including the US Geological Survey, National Renewable Energy Laboratory, and National Oceanic and Atmospheric Administration. Patients were assigned environmental variables from measurements nearest their zip code. Variable selection was done using elastic-net regularization. Multivariable logistic regression quantified the association of each environmental variable with nvAMD while adjusting for age, sex, race, and phakic status.

Results : A total of 18,166,512 patients were included. All demographic variables, phakic status, elevation, latitude, solar irradiance measured in global horizontal irradiance (GHI) and direct normal irradiance (DNI), and temperature and precipitation variables were included in our model after regularization. After adjusting for demographic factors and phakic status, the strongest environmental associations were DNI (OR: 0.550, 95%CI: [0.524, 0.578]), GHI (3.874, [3.452, 4.348]), and latitude (1.099, [1.092, 1.106]), while elevation had minimal association (1.000, [1.000, 1.000]) (Figure 1). The risk of nvAMD for a 75-year-old, pseudophakic, Caucasian female was calculated for the US (Figure 2).

Conclusions : Strongest associations for environmental factors were seen for DNI, GHI, and latitude. Further studies are warranted to investigate the clinical relevance of these associations.

This is a 2021 ARVO Annual Meeting abstract.

 

Figure 1: Odds ratios for environmental predictor variables of a multivariable logistic regression model, adjusted for age, sex, race, and phakic status.

Figure 1: Odds ratios for environmental predictor variables of a multivariable logistic regression model, adjusted for age, sex, race, and phakic status.

 

Figure 2: Risk prediction map of the continental United States for nvAMD for a pseudophakic 75-year-old Caucasian female.

Figure 2: Risk prediction map of the continental United States for nvAMD for a pseudophakic 75-year-old Caucasian female.

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