July 2019
Volume 60, Issue 9
Free
ARVO Annual Meeting Abstract  |   July 2019
PREDICTORS OF LONG-TERM VISUAL FIELD FLUCTUATION
Author Affiliations & Notes
  • Alessandro Rabiolo
    Stein Eye Institute - UCLA, Los Angeles, California, United States
    San Raffaele Scientific Institute, Milan, Italy
  • Esteban Morales
    Stein Eye Institute - UCLA, Los Angeles, California, United States
  • Jihyun Kim
    Stein Eye Institute - UCLA, Los Angeles, California, United States
  • Diana Salazar
    Stein Eye Institute - UCLA, Los Angeles, California, United States
  • Abdelmonem A Afifi
    Department of Biostatistics, Jonathan and Karin Fielding School of Public Health at UCLA, Los Angeles, California, United States
  • Fei Yu
    Stein Eye Institute - UCLA, Los Angeles, California, United States
    Department of Biostatistics, Jonathan and Karin Fielding School of Public Health at UCLA, Los Angeles, California, United States
  • Kouros Nouri-Mahdavi
    Stein Eye Institute - UCLA, Los Angeles, California, United States
  • Joseph Caprioli
    Stein Eye Institute - UCLA, Los Angeles, California, United States
  • Footnotes
    Commercial Relationships   Alessandro Rabiolo, None; Esteban Morales, None; Jihyun Kim, None; Diana Salazar, None; Abdelmonem Afifi, None; Fei Yu, None; Kouros Nouri-Mahdavi, None; Joseph Caprioli, None
  • Footnotes
    Support  RPB, Simms/Mann Family Foundation, NIH K23 5K23EY022659
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 2856. doi:
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    • Get Citation

      Alessandro Rabiolo, Esteban Morales, Jihyun Kim, Diana Salazar, Abdelmonem A Afifi, Fei Yu, Kouros Nouri-Mahdavi, Joseph Caprioli; PREDICTORS OF LONG-TERM VISUAL FIELD FLUCTUATION. Invest. Ophthalmol. Vis. Sci. 2019;60(9):2856.

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

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Abstract

Purpose : The identification of visual field (VF) progression is confounded by VF fluctuation. We present baseline and longitudinal predictive factors for VF fluctuation.

Methods : 18,756 VFs of 1,392 eyes (816 glaucoma/glaucoma suspect patients) with ≥6 VFs and ≥3 years of follow-up were retrospectively included. Linear regression of mean deviation (MD) and of each pointwise series eliminated the trend; root mean square error (RMSE) of the residuals was used to measure variability. RMSEs of all test locations were averaged to a single metric for each eye. The following baseline variables were tested as potential predictors of VF fluctuation: age, gender, ethnicity, IOP, CCT, number of medications, spherical equivalent, BCVA, lens status, previous glaucoma surgery, MD, hypertension, diabetes, smoking status, family history of glaucoma. The following longitudinal variables were tested as potential predictors: IOP fluctuation, length of follow-up (FU), frequency of VFs, glaucoma and cataract surgery, rate of VF decay, and median false positive (FP), false negative (FN), and fixation losses.

Results : The mean±SD age was 62.4±11.8 years. Median (IQR) values for baseline MD, FU time, and number of VFs were –2.4 (–5.7–0.7) dB, 11.6 (7.9–14.6) years, and 12 (9–16), respectively. Median (IQR) RMSE for MD and pointwise analyses were 0.97 (0.71–1.40) dB and 2.56 (1.88–3.53). When MD RMSE (Figure 1) was used to measure variability, Asian descent, greater IOP fluctuation, longer FU, worse baseline MD, faster VF decay, and higher median FP and FN responses were significant predictors of VF fluctuation. With pointwise RMSE (Figure 2), older age, Asian descent, higher IOP fluctuation, longer FU, higher frequency of VFs, glaucoma surgery during follow-up, worse baseline MD, faster VF decay, and higher FP and FN responses were associated with increased fluctuation, while chronic angle-closure glaucoma with reduced fluctuation. The percentage of variance explained by the regression models of MD and pointwise RMSEs was 29% and 58%, respectively; when only baseline factors were included, the proportion of variance explained was 11% and 33%, respectively.

Conclusions : This study identifies novel predictors of increased VF fluctuation. Nearly 60% of the pointwise variance can be explained by the multivariable model. In the presence of factors predictive of high fluctuation, increased frequency of testing and better analytics will help to accurately identify VF progression.

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

 

 

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