Investigative Ophthalmology & Visual Science Cover Image for Volume 59, Issue 9
July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
Quantification of longitudinal changes in microvascular density and morphology among subjects with diabetic retinopathy using SD-OCT angiography.
Author Affiliations & Notes
  • Kyle Marcus Green
    Ophthalmology, USC Roski Eye Institute, University of Southern California, Los Angeles, California, United States
  • Xuejuan Jiang
    Ophthalmology, USC Roski Eye Institute, University of Southern California, Los Angeles, California, United States
  • Zhongdi Chu
    Bioengineering, University of Washington, Seattle, Washington, United States
  • Ruikang K Wang
    Bioengineering, University of Washington, Seattle, Washington, United States
  • Amir H Kashani
    Ophthalmology, USC Roski Eye Institute, University of Southern California, Los Angeles, California, United States
  • Footnotes
    Commercial Relationships   Kyle Green, None; Xuejuan Jiang, None; Zhongdi Chu, None; Ruikang Wang, Carl Zeiss Meditec (P), Carl Zeiss Meditec (C), Insight Photonic Solutions (C), Kowa (P); Amir Kashani, Carl Zeiss Meditec (F), Carl Zeiss Meditec (R)
  • Footnotes
    Support  NIH K08EY027006, NIH R21EY027879, Research Grants from Carl Zeiss Meditec Inc, Unrestricted Department Funding from Research to Prevent Blindness
Investigative Ophthalmology & Visual Science July 2018, Vol.59, 1924. doi:
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      Kyle Marcus Green, Xuejuan Jiang, Zhongdi Chu, Ruikang K Wang, Amir H Kashani; Quantification of longitudinal changes in microvascular density and morphology among subjects with diabetic retinopathy using SD-OCT angiography.. Invest. Ophthalmol. Vis. Sci. 2018;59(9):1924.

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

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Abstract

Purpose : To quantify longitudinal changes in retinal capillary density and morphology among diabetic subjects imaged with spectral-domain optical coherence tomography angiography.

Methods : This is an IRB approved, retrospective study of subjects with history of diabetic retinopathy who underwent SD-OCTA at 2 or more clinic visits over a period of >3 months. Images with signal strength <7 or significant motion artifacts were excluded. A semi-automated algorithm was used to calculate OCTA metrics of microvascular density and morphology in non-segmented SD-OCTA images. Some OCTA metrics have been previously reported (Kim AY et al., IOVS 2016) and include: vessel skeleton density (VSD) and vessel diameter index (VDI). A novel parameter, flux, was also used to estimate relative changes in blood flow. Data analysis was performed using linear regression and Student’s t-tests comparing values from initial and last follow-ups.

Results : 34 eyes from 24 subjects (14 male) were included in this study with a mean age of 62 ± 14. There were 7 mild, 6 moderate, 7 severe NDPR, and 14 PDR eyes. 82% of eyes were treated with anti-VEGF; 64% of PDR eyes received laser treatment. The median number of follow-ups per subject was 5 over a mean of 402 days (range 79-612 days). For eyes with NDPR there was a mean decrease of 4.3% in VSD from the first to last follow-up (Student’s t-test; p = 0.028), a 1.2% decrease in VDI (p = 0.363), and a 6.6% decrease in flux (p = 0.089). For eyes with PDR there was a mean decrease of 1.7% in VSD (p = 0.321), a 2.5% decrease in VDI (p = 0.020), and a 10.8% decrease in flux (p = 0.009). In our linear regression models decreasing trends were seen in 18/20 NDPR and 8/14 PDR eyes for VSD, 10/20 and 7/14 eyes for VDI, and 13/20 and 10/14 for flux respectively.

Conclusions : OCTA metrics of capillary density and morphology demonstrate decreases in the capillary density and size over time. Comparing metrics from first and last follow-ups, there was a significant decrease in VSD for eyes with NPDR, and in VDI and flux for eyes with PDR. OCTA metrics used over time may be useful in assessing rate of disease progression.

This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.

 

Linear regression of changes in vessel skeleton density overtime. Data reflects percent change from baseline at initial visit. Each line represents one eye followed overtime.

Linear regression of changes in vessel skeleton density overtime. Data reflects percent change from baseline at initial visit. Each line represents one eye followed overtime.

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