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Longitudinal visual field variability and the ability to detect glaucoma progression in black and white individuals
  1. Brian Stagg1,2,3,
  2. Eduardo B Mariottoni1,
  3. Samuel Berchuck1,4,
  4. Alessandro Jammal1,
  5. Angela R Elam5,
  6. Rachel Hess3,6,
  7. Kensaku Kawamoto7,
  8. Benjamin Haaland3,
  9. Felipe A Medeiros1
  1. 1Ophthalmology, Vision, Imaging and Performance (VIP) Laboratory, Duke Eye Center, Durham, North Carolina, USA
  2. 2Ophthalmology and Visual Sciences, University of Utah Health John A Moran Eye Center, Salt Lake City, Utah, USA
  3. 3Population Health Sciences, University of Utah Health, Salt Lake City, Utah, USA
  4. 4Statistical Science and Forge, Duke University, Durham, North Carolina, USA
  5. 5Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, USA
  6. 6Internal Medicine, University of Utah, Salt Lake City, Utah, USA
  7. 7Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
  1. Correspondence to Professor Felipe A Medeiros, Ophthalmology, Duke University, Durham, North Carolina, USA; felipe.medeiros{at}duke.edu

Abstract

Background/Aims To investigate racial differences in the variability of longitudinal visual field testing in a ‘real-world’ clinical population, evaluate how these differences are influenced by socioeconomic status, and estimate the impact of differences in variability on the time to detect visual field progression.

Methods This retrospective observational cohort study used data from 1103 eyes from 751 White individuals and 428 eyes from 317 black individuals. Linear regression was performed on the standard automated perimetry mean deviation values for each eye over time. The SD of the residuals from the trend lines was calculated and used as a measure of variability for each eye. The association of race with the SD of the residuals was evaluated using a multivariable generalised estimating equation model with an interaction between race and zip code income. Computer simulations were used to estimate the time to detect visual field progression in the two racial groups.

Results Black patients had larger visual field variability over time compared with white patients, even when adjusting for zip code level socioeconomic variables (SD of residuals for Black patients=1.53 dB (95% CI 1.43 to 1.64); for white patients=1.26 dB (95% CI 1.14 to 1.22); mean difference: 0.28 (95% CI 0.15 to 0.41); p<0.001). The difference in visual field variability between black and white patients was greater at lower levels of income and led to a delay in detection of glaucoma progression.

Conclusion Black patients had larger visual field variability compared with white patients. This relationship was strongly influenced by socioeconomic status and may partially explain racial disparities in glaucoma outcomes.

  • glaucoma
  • diagnostic tests/investigation

Data availability statement

Data are available on reasonable request. The Duke Glaucoma Registry data are maintained on HIPAA-compliant servers at Duke University.

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Data availability statement

Data are available on reasonable request. The Duke Glaucoma Registry data are maintained on HIPAA-compliant servers at Duke University.

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Footnotes

  • Twitter @BrianStaggMD

  • Contributors FAM and BS conceived of the presented idea. FAM, SB, EBM and AJ developed the theory and planned the computations. BS, EBM, SB, AJ, ARE, RH, KK, BH and FAM discussed and revised the analysis plan. EBM performed the computations. FAM, BS, SB, AJ, RH, KK, BH verified the analytical methods. BS, EBM, SB, AJ, ARE, RH, KK, BH and FAM discussed the results and contributed to the final manuscript and revision.

  • Funding This work was supported by the National Eye Institute, Bethesda, MD (grant numbers EY029885 (FAM) and EY021818 (FAM)); an unrestricted grant from Research to Prevent Blindness, New York, NY to the Department of Ophthalmology and Visual Sciences, University of Utah (no award/grant number).

  • Disclaimer The sponsors played no role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

  • Competing interests FAM: Alcon Laboratories (Financial support (F), Research support (R)), Allergan (Consultant (C), F, R), Bausch & Lomb (F), Carl Zeiss Meditec Inc (C, F, R), Heidelberg Engineering Inc (F), Merck Inc (F), National Eye Institute (F), Novartis (C), Reichert Inc (F, R), Topcon Inc (F). Kensaku Kawamoto: McKesson InterQual (C), Hitachi (R), Pfizer (R), Premier (C), Klesis Healthcare (C), RTI International (C), Mayo Clinic (C), Vanderbilt University (C), the University of Washington (C), the University of California at San Francisco (C) and the US Office of the National Coordinator for Health IT (via ESAC, JBS International, A+ Government Solutions, Hausam Consulting, and Security Risk Solutions) (C), Health Level Seven International health IT standard development organisation (unpaid board member). Rachel Hess: Astellas Pharmaceuticals (C). The following authors have no financial disclosures: BCS, EBM, SB, AJ, ARE and BH. All authors attest that they meet the current ICMJE criteria for authorship.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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