July 2019
Volume 60, Issue 9
Free
ARVO Annual Meeting Abstract  |   July 2019
Classification of Intermediate Age-Related Macular Degeneration by Short-Term Risk for Atrophy on Spectral Domain Optical Coherence Tomography
Author Affiliations & Notes
  • Eleonora M Lad
    Ophthalmology, Duke University Medical Center, Durham, North Carolina, United States
  • Karim Sleiman
    Ophthalmology, Duke University Medical Center, Durham, North Carolina, United States
  • David L Banks
    Statistical Sciences, Duke University Medical Center, North Carolina, United States
  • Sanjay Hariharan
    Ophthalmology, Duke University Medical Center, Durham, North Carolina, United States
  • Traci E Clemons
    Emmes, Maryland, United States
  • Emily Y Chew
    National Eye Institute, Maryland, United States
  • Cynthia A Toth
    Ophthalmology, Duke University Medical Center, Durham, North Carolina, United States
  • Footnotes
    Commercial Relationships   Eleonora Lad, Boehringer Ingelheim (F), Novartis (F), Roche (F), Roche (C); Karim Sleiman, None; David Banks, None; Sanjay Hariharan, None; Traci Clemons, None; Emily Chew, None; Cynthia Toth, Alcon (P), Boehringer Ingelheim (F), Hemosonics (P)
  • Footnotes
    Support  NEI K23EY026988; N01 EY50007; R01 EY023039, R01 EY025009, and P30 EY005722
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 2213. doi:
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    • Get Citation

      Eleonora M Lad, Karim Sleiman, David L Banks, Sanjay Hariharan, Traci E Clemons, Emily Y Chew, Cynthia A Toth; Classification of Intermediate Age-Related Macular Degeneration by Short-Term Risk for Atrophy on Spectral Domain Optical Coherence Tomography. Invest. Ophthalmol. Vis. Sci. 2019;60(9):2213.

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

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Abstract

Purpose : To propose a classification of intermediate AMD (iAMD) based on specific Spectral-Domain optical coherence tomography (SDOCT) features and their risk for onset of new geographic atrophy (GA). Additional goals were to identify OCT features that may be used in machine/deep learning studies and to identify candidates for future clinical trials of iAMD.

Methods : Qualitative and quantitative multimodal variables from the Age-Related Eye Disease Study 2 (AREDS2) Ancillary SDOCT study database were derived at each yearly visit over 5 years. We analyzed imaging data from patients with iAMD (n = 316) with adequate SDOCT imaging for repeated measures. Based on statistical techniques developed for the Framingham Heart Study, a pooled database was generated using an algorithm that selected only person-years without geographic atrophy (GA) on color fundus photography or SDOCT at baseline. The analysis employed machine learning approaches to generate classification trees.

Results : Eyes were stratified based on retinal and subretinal OCT features (hyperreflective foci, subretinal drusenoid deposits, RPE abnormal thinning, RPE drusen complex, neurosensory retina thickness) and age by the risk of GA development as rare, low, low-intermediate, intermediate, and high risk for future GA onset in 1, 2, 3, and 4 years. We built a statistical logistic regression model with multiple retinal and subretinal SD-OCT features from the baseline visit, each of which independently conveyed higher risk of new-onset OCT-determined GA on each of the follow-up visits.

Conclusions : We propose a risk-stratified subgrouping of iAMD based on OCT-derived drusen characteristics, retinal pathology and age, for progression to OCT-determined GA. The composite early endpoints may be used as exclusion or inclusion criteria for future clinical studies of iAMD focused on prevention of GA progression.

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

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