June 2020
Volume 61, Issue 7
ARVO Annual Meeting Abstract  |   June 2020
An approach to identify progression using the dynamic structure-function model
Author Affiliations & Notes
  • Sampson Listowell Abu
    University of Alabama at Birmingham, Birmingham, Alabama, United States
  • Lyne Racette
    University of Alabama at Birmingham, Birmingham, Alabama, United States
  • Footnotes
    Commercial Relationships   Sampson Abu, None; Lyne Racette, None
  • Footnotes
    Support  NIH Grant EY025756 (LR). The DIGS and ADAGES studies were supported by NIH Grants P30EY022589, EY021818, EY11008, U10EY14267, and EY019869; Eyesight Foundation of Alabama; Alcon Laborato-ries, Inc.; Allergan, Inc.; Pfizer, Inc.; Merck, Inc.; Santen, Inc.; Edith C. Blum Research Fund of the New York Glaucoma Research Institute (New York, NY, USA); and an unrestricted grant from Research to Prevent Blindness (New York, NY, USA).
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 1991. doi:
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    • Get Citation

      Sampson Listowell Abu, Lyne Racette; An approach to identify progression using the dynamic structure-function model. Invest. Ophthalmol. Vis. Sci. 2020;61(7):1991.

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

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Purpose : The detection of glaucoma progression is limited by several factors including measurement variability. Predictive models can be used to estimate future measurements from observed series of data. In this study, we assessed progression using predicted structural and functional measurements obtained from the dynamic structure-function model (DSF) and ordinary least squares linear regression (OLSLR). We hypothesized that the reduced variability in the predicted data would result in improved detection of change.

Methods : The study included 337 eyes with ocular hypertension or primary open-angle glaucoma selected from the Diagnostic Innovations in Glaucoma Study or the African Descent and Glaucoma Evaluation Study. Each eye had at least 9 retinal nerve fiber layer thickness (RNFLT; Spectralis OCT) and mean sensitivity (MS; 24-2 SITA Standard test HFA II) measurements. The average follow-up time was 5.5 ± 1.1 years. Both indices were expressed in percent of mean normal. The DSF and OLSLR models were applied to the first 3 pairs of RNFLT and MS to predict the 4th pair. This procedure was repeated until the 9th pair was predicted from the first 8 pairs of RNFLT and MS. Simple linear regression was performed on the DSF-predicted data, OLSLR-predicted data and on the observed data. We fixed specificity at 95% and 99% and applied two criteria to assess progression: the ANY criterion (significant worsening on either RNFLT or MS) and the ALL criterion (worsening on both measurements). The proportions of eyes flagged as worsening for each type of data and criterion were compared. The mean squared residuals from the regressions were compared to ascertain the difference in variability between the observed data and predicted data.

Results : Between 6 and 23% more eyes were identified as progressing with the DSF-predicted data compared to the observed data and OLSLR-predicted data (Figure 1). Figure 2 illustrates the agreement on progressing eyes between the three types of data. The mean squared residuals for the DSF-predicted RNFLT and MS were significantly smaller compared to that for OLSLR and observed data (Table 1).

Conclusions : The DSF-predicted data identified a greater proportion of progressing eyes, even at a higher level of specificity. The reduced variability in the predicted data may allow for a more precise assessment of glaucoma progression.

This is a 2020 ARVO Annual Meeting abstract.




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