Purpose: We determine the contrast-to-noise ratios (CNRs) of structural and functional measurements to assess their sensitivity to detect progression in the various stages of glaucoma.
Methods: We calculated the CNRs for the mean peripapillary retinal nerve fiber layer (RNFL) thickness measured by spectral domain optical coherence tomography, and the mean deviation (MD) and visual field index (VFI) determined by standard automated perimetry for the transitions between five stages. Longitudinal data from healthy and glaucomatous eyes from a prospective study were used. Contrast was defined as the change in the mean value of the parameter between two successive stages. Noise was defined as the variability of the parameter and calculated from the residuals of linear regression on the data from five subsequent visits per eye.
Results: We studied 205 eyes from 125 participants (46% men, 54% women). CNRs for different parameters varied considerably across the range of disease severity (0.8-12.2). The RNFL thickness had a higher CNR in the transition from normal to mild glaucoma (12.2) compared to the CNRs of the functional measures (MD 4.1, VFI 4.5). The CNRs for the functional measures were higher in the transition from moderate to advanced (MD 5.2, VFI 5.8) and advanced to severe glaucoma (MD 7.2, VFI 5.8) compared to the RNFL thickness (CNR 0.8 and 3.2, respectively).
Conclusions: The RNFL thickness is more sensitive for detecting glaucomatous progression at the onset of glaucoma compared to the functional measures, while the latter are more sensitive for detecting progression in the later stages of glaucoma.
Translational Relevance: The CNR method can be used to determine which measurement is most sensitive for detecting progression in glaucoma, differentiated for the severity of the disease. Furthermore, it creates a basic toolset for determining the most sensitive measurement in detecting progression not only in glaucoma, but other (ophthalmic) diseases as well.
|Journal||Translational vision science & technology|
|Publication status||Published - May 2019|