June 2020
Volume 61, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2020
A comparison of methods to detect structure-function progression in early glaucoma
Author Affiliations & Notes
  • Emmanouil (Manos) Tsamis
    Psychology, Columbia University, New York, New York, United States
  • Ashley Sun
    Psychology, Columbia University, New York, New York, United States
  • Sol La Bruna
    Psychology, Columbia University, New York, New York, United States
  • Carlos Gustavo De Moraes
    Ophthalmology, Columbia University, New York, New York, United States
  • Donald C Hood
    Psychology, Columbia University, New York, New York, United States
    Ophthalmology, Columbia University, New York, New York, United States
  • Footnotes
    Commercial Relationships   Emmanouil (Manos) Tsamis, Topcon Inc. (R); Ashley Sun, None; Sol La Bruna, None; Carlos De Moraes, Belite (C), Carl Zeiss (C), Galimedix (C), Heidelberg (R), National Institutes of Health (R), Novartis (C), Perfuse Therapeutics (C), Reichert (C), Research to Prevent Blindness (R), Topcon Inc (R); Donald Hood, Heidelberg (F), Heidelberg (C), Heidelberg (R), Novartis (C), Novartis (F), Novartis (R), Topcon (F), Topcon (C), Topcon (R)
  • Footnotes
    Support  EY-025253, EY-02115
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 1990. doi:
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      Emmanouil (Manos) Tsamis, Ashley Sun, Sol La Bruna, Carlos Gustavo De Moraes, Donald C Hood; A comparison of methods to detect structure-function progression in early glaucoma. Invest. Ophthalmol. Vis. Sci. 2020;61(7):1990.

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

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Abstract

Purpose : To compare methods of combined structure-function information to common structure-only and function-only metrics for detecting progression in early glaucomatous eyes.

Methods : 151 eyes/individuals had repeat OCT scans and 24-2&10-2 visual fields (VF) within 6 months (mean: 3.2 visits, range 2-5). This formed a variability (V) group, with 49 healthy (H), and 102 glaucoma (G) or glaucoma suspect (GS) eyes. OCT and VF tests >1year after the baseline tests were obtained from 100 (74 G/GS, 26 H) of the 151 eyes to comprise the study group. Summary metrics of the thickness measures from the circumpapillary retinal nerve fiber layer (cRNFL) and a ±8° retinal ganglion cell (RGC) region around the fovea, as well as mean and pattern standard deviation (MD; PSD) metrics from the 24-2/10-2 VF, were obtained (Table). Topographical agreement of abnormal structure-abnormal function (aS-aF) locations between RNFL and RGC probability and 24-2&10-2 PD probability maps from each session was determined with an objective, automated method.[1] Quantile regression was applied to each metric to derive 95% limits and define “statistical progressors”.[2] Four glaucoma experts evaluated all OCT and 24-2&10-2 VF tests to form a progression reference standard (P-RS).

Results : The P-RS identified 27 progressing G eyes. For the OCT alone, the RGC metrics in general, and the I-Mac in particular, detected the largest number of the 27 P-RS eyes (Hits in Table)(I-Mac: 19 hits). However, the VF metrics alone detected the fewest; although the PSD 10-2 identified almost double the number of P-RS eyes than PSD 24-2 (n= 9 vs 5). A combination of global cRNFL or RGC (G OR G Mac) metrics identified more P-RS eyes, (n=23), but also produced another 19 false positives (FPs). Fig shows a progressing eye that was not detected by G or the VFs. For structure-function, a combined summary metric [(G or G Mac) AND (PSD 24-2 or 10-2)] was more specific [4 (5.5%) FPs] but detected only a third of the P-RS eyes (n=9). An automated aS-aF agreement method slightly improved performance with 3 FPs and 13 P-RS eyes.

Conclusions : Summary metrics perform poorly in identifying progressing eyes, due to a high number of FPs. A structure-function summary metric significantly reduces the number of FPs, while a topographic agreement method slightly improves sensitivity, while maintaining high specificity. [1]Hood, Tsamis et al. IOVS 2019 [2]Wall, Doyle et al. IOVS 2013

This is a 2020 ARVO Annual Meeting abstract.

 

 

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