**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.

^{1,2}It is a progressive optic neuropathy that, if untreated, leads to irreversible loss of retinal ganglion cells and thinning of the retinal nerve fiber layer (RNFL) before visual field (VF) loss.

^{3}Therefore, early detection of progression is essential for preventing further VF loss.

^{4}

^{5–7}and/or functional testing with perimetry (VF testing). However, the correlation between these two types of measurements generally is poor. Several studies have presented cases where structural changes were seen in the RNFL while no functional changes were detected and vice versa.

^{8–12}One possible explanation for this poor correlation between different measurement techniques is their measurement variability. Any progression is more difficult to detect with greater measurement variability.

^{13}

^{10,14–18}We would argue, however, that it is not possible to directly compare the different types of measurements, structural and functional, because they are expressed in different units of measure. Therefore, a dimensionless measure is needed to make this comparison possible as to determine which type of measurement or which parameter (e.g., mean deviation [MD] or visual field index [VFI] for perimetry or mean RNFL thickness for OCT) is actually the most sensitive for the detection of glaucomatous progression.

^{19}The CNR equals the ratio of the difference in magnitude between measurements in successive stages (i.e., contrast) and the reproducibility of the measurement (i.e., noise). We determine the contrast from cross-sectional measurements at different stages of the disease, while the noise was assessed from annual follow-up measurements. The higher the CNR, the more levels between two stages can be discriminated.

^{20}(i.e., normal, mild, moderate, and advanced glaucoma). To expand our investigations, advanced glaucoma was further divided into two stages; an advanced stage, defined as an MD between ≤−12 and >−18 dB, and a severe stage, defined as an MD ≤−18 dB.

*SNR*), which is defined as:

_{a}– μ

_{b}). The noise was defined as the variability of the parameter in those stages (σ

_{ab}).

^{21}also accounts for the factors that may change the variability over time (e.g., patient fatigue or the severity of the disease) and, therefore, provides a more accurate estimation of the measurements variability for our method (σ

_{ab}). To assess the variability of each parameter for two successive stages (σ

_{ab}), we performed an analysis of residuals from linear regression of the data from five subsequent visits per eye (Fig. 1). The glaucoma stage for each eye then was determined by the value of the MD on the regression line halfway between the first and fifth visits. Under the assumption that progression occurs at a fixed rate, the residuals from these analyses represent the variability over time of the parameter for that eye.

^{21}For each eye

*n*is the number of visits per eye (

*n*= 5). Here, the number of degrees of freedom is

*n*− 2, because two degrees of freedom are lost due to estimation of the mean and regression slope.

*is the mean noise in stage A,*

_{A}*n*is the number of eyes in this stage, σ

_{A}*is the mean noise of the subsequent stage and*

_{B}*n*the number of eyes in that stage. The noise (σ

_{B}*) for the transition between two successive stages (e.g., mild to moderate glaucoma) then was calculated as the average noise for these two stages by:*

_{AB}*is the mean of the parameter in one stage and μ*

_{A}*is the mean of the parameter in the subsequent stage. Thus, the contrast represents the effective measuring range of the measurement for detecting progression (i.e., the average measured difference between two successive stages). For example, the contrast for the VFI for the transition from normal to mild glaucoma is calculated as the difference between the mean VFI from all normal eyes and the mean VFI from all mildly glaucomatous eyes.*

_{B}*CNR*) was calculated by:

_{AB}^{22,23}In case of any age differences between the groups, the RNFL thickness data for these groups was adjusted for this aging effect.

*U*and Kruskal Wallis

*H*for continuous variables and binomial tests and χ

^{2}for categorical variables) were used for statistical analysis. For comparison of the noise between the different stages, the Kruskal Wallis test was used.

*P*< 0.05 was considered statistically significant.

*P*< 0.05). The time variable only differed significantly between the normal and mild subgroups (

*P*< 0.05). There were no significant differences for age and time variables between the remaining successive stages. The average RNFL thickness, MD, and VFI were significantly different between the normal and glaucoma groups and between successive stages. Post hoc Dunn-Bonferroni correction showed significant differences for the study parameters (RNFL, MD, and VFI) between all successive stages (

*P*< 0.05), except for the average RNFL thickness between the moderate and advanced subgroups (

*P*= 1.00).

^{10,14–18}

^{24}If we look at the summed CNR of all transitions for each technology separately, it shows that OCT and SAP are approximately equally sensitive for monitoring progression across the entire disease spectrum: their CNRs are all approximately 20. However, the structural measure is more sensitive in detecting progression earlier on in the disease, suggesting that using OCT in early stages and SAP in later stages is more cost effective. By using different instruments for monitoring different disease stages, 29 steps compared to the aforementioned 20 steps from healthy to severe glaucoma can be discriminated and one can limit the use of these technologies to only one at a time. Such a tailored and combined approach not only saves time and expenses, it also reduces the burden of testing to our patients. Combining measurements has also been supported by the work of Medeiros et al.

^{15}who developed a combined structure and function approach that later was used by Zhang et al.

^{25}to measure rates of progression in glaucoma. This approach describes a single index for estimating the number of ganglion cells in individual eyes by combining two models developed by Harwerth et al.

^{26}: the perimetric model, which converts SAP data into an estimate of number of ganglion cells, and the RNFL model, which converts RNFL data in a number of ganglion cells. The two models are combined in a weighted sum that relies more on the RNFL estimate at the onset of glaucoma and on the SAP estimate in the later stages of glaucoma.

^{15}Similar to their approach, we also took the differences between the added value of OCT and SAP for the various stages of glaucoma into account and showed that OCT is of more value for detecting progression at the onset of glaucoma and SAP is of more value for detecting progression in the later stages of glaucoma.

^{23}used longitudinal SNRs (LSNRs), which were defined for each eye as the ratio of the mean annual rate of change (signal) to the standard deviation of residuals (noise) from linear regression analysis. The authors showed better mean LSNRs for the average RNFL thickness from peripapillary scans (−0.601 y-1) compared to the MD from SAP (−0.045 y-1) in 226 eyes with MDs at baseline ranging from −15.46 to 2.50 dB. The mean LSNR of a subgroup of suspected glaucomatous eyes with an MD within normal limits at baseline also showed better results for the average RNFL thickness compared to the MD. This is consistent with our results, indicating that the OCT is more sensitive for detecting progression compared to SAP at the onset of glaucoma. In their study, progression was defined as the rate of change per year. Therefore, this method could be used to determine which parameter is more sensitive for detecting progression in the short run. Contrary to our study, they did not extend their study to more advanced glaucoma.

^{27,28}Further RNFL thinning, therefore, is not expected at these stages and consequently the expected contrast becomes negligible.

^{21,29,30}Due to this left censoring effect, sensitivity threshold values below 0 dB, although physiologically possible, cannot be detected by the device. We also noted a decreased variability of the VFI at an average MD of −12 dB. This can be explained by the fact that the VFI of the eyes in these advanced stages approaches its “floor” of 0%. In summary, the left censoring effect of the MD and the floor effect of the VFI lead to smaller ranges in MD and VFI in the advanced and severe cases of glaucoma. This resulted in an apparent decrease in variability and consequently caused an apparent increase in the CNR for the MD and VFI in these later stages.

^{31,32}However, this damaged area often passes unnoticed in the RNFL thickness with OCT peripapillary circle scans.

^{33}Furthermore, it has been suggested that the thickness of the GCC is better for detecting progression in the more advanced stages of glaucoma than the RNFL thickness.

^{18,27}

**J.E.A. Majoor**, None;

**K.A. Vermeer**, None;

**E.-R. Andrinopoulou**, None;

**H.G. Lemij**, None

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