June 2023
Volume 64, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2023
Detection of Fast Progressors: A Bayesian Hierarchical Spatial Model vs. Linear Regression for Detection of Macular Structural Change in Glaucoma
Author Affiliations & Notes
  • Kouros Nouri-Mahdavi
    Ophthalmology, Jules Stein Eye Institute, Los Angeles, California, United States
  • Erica Su
    Biostatistics, University of California Los Angeles Jonathan and Karin Fielding School of Public Health, Los Angeles, California, United States
    Ophthalmology, Jules Stein Eye Institute, Los Angeles, California, United States
  • Vahid Mohammadzadeh
    Ophthalmology, Jules Stein Eye Institute, Los Angeles, California, United States
  • Massood Mohammadi
    Ophthalmology, Jules Stein Eye Institute, Los Angeles, California, United States
  • David Zhang
    Ophthalmology, Jules Stein Eye Institute, Los Angeles, California, United States
  • Joseph Caprioli
    Ophthalmology, Jules Stein Eye Institute, Los Angeles, California, United States
  • Robert E Weiss
    Biostatistics, University of California Los Angeles Jonathan and Karin Fielding School of Public Health, Los Angeles, California, United States
  • Footnotes
    Commercial Relationships   Kouros Nouri-Mahdavi Topcon, Code F (Financial Support), Heidelberg Engineering, Code R (Recipient); Erica Su None; Vahid Mohammadzadeh None; Massood Mohammadi None; David Zhang None; Joseph Caprioli None; Robert Weiss None
  • Footnotes
    Support  NEI R01 EY029792, Departmental Grant from Research to Prevent Blindness, Heidelberg Engineering
Investigative Ophthalmology & Visual Science June 2023, Vol.64, 1310. doi:
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      Kouros Nouri-Mahdavi, Erica Su, Vahid Mohammadzadeh, Massood Mohammadi, David Zhang, Joseph Caprioli, Robert E Weiss; Detection of Fast Progressors: A Bayesian Hierarchical Spatial Model vs. Linear Regression for Detection of Macular Structural Change in Glaucoma. Invest. Ophthalmol. Vis. Sci. 2023;64(8):1310.

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

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Abstract

Purpose : Test the hypothesis that a novel Bayesian hierarchical spatial longitudinal (HSL) model identifies macular superpixels with rapidly progressing ganglion cell complex (GCC) thickness more efficiently than simple linear regression (SLR).

Methods : We analyzed GCC thickness measurements within 49 macular superpixels in 111 eyes (111 patients) with ≥4 macular optical coherence tomography scans and ≥2 years of follow-up. We estimated superpixel-patient specific slopes and their posterior variances from the latest version of a Bayesian HSL model and Bayesian SLR. A simulation group with known superpixel slopes and variances was created. Outcomes of interest were a) proportion of superpixels with significantly progressing and improving slopes as a function of true slopes in the simulation study and the SLR slope in the cohort in the fastest changing deciles; b) root mean square error (RMSE), and SLR/HSL RMSE ratio within the simulation study and patient cohort. Slopes were defined as significantly negative (positive) if the upper (lower) limit of 95% credible interval was <0 (>0).

Results : Simulation study- Median SLR/HSL RMSE ratio was 1.36 and 1.61 in the top two superpixel deciles with fastest negative rates and 1.73 and 1.31 in the lowest two deciles (most positive slopes); a ratio >1 indicates HSL is better. 83% of RMSE ratios favored HSL in the fastest slope decile. HSL identified a higher proportion of significant negative slopes in the two deciles with the most negative slopes (88% and 57% vs. 52% and 19% for SLR) and 1.9% and 15% of superpixels with the most positive slopes compared to 2.3% and 1.3% for SLR.
Cohort data- The proportion of significantly negative slopes was 77% for SLR and 79% for HSL in the steepest SLR decile. In the most positive SLR decile, HSL and SLR identified 11% and 36% significantly positive slopes. In the fastest 10% of slopes per SLR, SLR/HSL ratio of posterior SDs had a median of 1.83, and 90% of ratios favored HSL. In the most positive 10% of slopes per SLR, this ratio had a median of 2.21, and 94% of SD ratios favored HSL.

Conclusions : A novel Bayesian HSL model improves estimation accuracy of local GCC rates of change regardless of the underlying true rate of change. HSL residual variances are less variable and less likely to capriciously under- or over-estimate significance.

This abstract was presented at the 2023 ARVO Annual Meeting, held in New Orleans, LA, April 23-27, 2023.

 

 

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