Advances in OCT technology have enabled more detailed and precise quantitative assessment of glaucomatous structural changes, through circumpapillary RNFL thickness measurements and, more recently, GCIPL thickness measurements. Considering the current results, GCIPL assessment does not improve the ability to diagnose glaucoma from the peripapillary RNFL evaluation. These results generally agreed with previous reports.
13–15 However, RNFL and GCIPL assessments target different neuroretinal areas, and they may be potentially complementary to overcome the incidence of false positive results of some OCT RNFL parameters.
16 Thus, in Cirrus OCT, Leal-Fonseca et al.
16 reported an 11% false positive rate in the average RNFL color code and 13% in the superior and inferior quadrants and average C/D ratio. This supports the combined use of parameters from different retinal areas for diagnosing glaucoma. Consequently, multivariate models predictive of glaucoma have been proposed to improve the diagnostic ability of these OCT parameters that yield results that are highly correlated and somewhat redundant. Previous studies have used learning classifiers, linear discriminant functions, principal component analysis, and logistic regression analyses
6–8 to improve the OCT diagnostic value. Some studies combined ONH and RNFL parameters that evaluated the status of GC axons at different locations,
17 whereas other studies combined ONH, RNFL, and macular GCIPL complex parameters, which accumulate information about different retinal anatomic areas.
6–8 However, those studies did not show the mathematical functions in their reports or provide a Web link to allow external validation or simple implementation of the proposed function in a given patient. It is important that such information be accessible; otherwise, the value of the function is limited to the authors and cannot be compared.
We performed an internal validation of the formula, which ensured that the proposed model was robust and guaranteed that the function would be useful in future datasets. In addition, compared to previous reports,
6–8,17 we presented our diagnostic calculator for external validation directly and through a Web link. We also tested and validated the functions in larger populations compared with comparable reports such as that of Mwanza et al.
8 (687 subjects compared to 253 subjects). Furthermore, our subjects were recruited from three hospitals and were representative of the Spanish population. Finally, the availability of our function allows it to be compared in larger populations and with different ethnic and sociogeographic features.
The function significantly improved the diagnostic value of the single OCT parameters (
Table 7). It is interesting that the RNFL parameters, such as the average RNFL or inferior RNFL, had AUCs (0.849 and 0.867, respectively) similar to the best GCIPL parameters, such as the minimal and inferior-temporal GCIPLs (AUCs, 0.868 and 0.867, respectively), whereas the ONH parameters such as the vertical C/D ratio had smaller AUC areas. However, the factor with the highest OR in the multivariate model was the C/D ratio average color (
Table 6). The C/D ratio adds information to the model about an anatomical feature that does not overlap with other measurements of RNFL or GCIPL parameters. In contrast, the RNFL and GCIPL parameters assess different aspects of the RNFL thickness that are correlated strongly between them. The multivariate model includes the most relevant factors; in the current study, the GCIPL parameters had more weight in the formula (better odds ratios [ORs]) than the RNFL parameters (
Table 6). Therefore, GCIPL assessment showed that the peripapillary RNFL thickness evaluation was more advantageous for diagnosing glaucoma. The low RNFL ORs in the formula suggested that the information about these parameters do not add much to the GCIPL measurements and supported the idea that the RNFL and GCIPL data mainly overlap.
Performing OCT often depends on the disease severity. It is easier to distinguish advanced glaucoma stages, but the function should not be restricted to detection in patients with early or moderate glaucoma. This approach avoids restricting the range in the test measures and therefore attenuating correlations among variables that can result in falsely low estimates of factor loading. The current function was not limited to early damage but covers all disease stages, unlike other reports.
8 Compared to reports that included patients with all degrees of glaucoma severity,
17 we also provided the diagnostic performance in the early-moderate and advanced stages. The diagnostic ability of the proposed multivariate function outperforms the diagnostic value of single parameters in early moderate and advanced glaucoma (
Figs. 1,
2).
Models 2 and 3 showed similar levels of performance in the study series, and it is true that quantitative outcomes are usually preferred to categorizations, suggesting that perhaps the simpler model should be preferred. However, in this case, we must bear in mind that the qualitative measurements are not simply cutoff values of the original quantitative ones but are attributes provided by the Cirrus OCT (as indicated by the manufacturer), which take into account more patient information (like age and optic disc size) to assign a result to a given category. Thus, the information provided by the quantitative measurement is enriched (like the “gray” category, which indicates that the optic disc size is unusually small or big). Therefore, model 3 was finally selected for the Excel calculator.
The current study had limitations. All subjects were from the same geographic region despite the large number of participants and involvement of three hospitals from different cities. However, the availability of our formula through a Web link allows future testing of the function in different geographic and ethnic settings, similar to other OCT normative databases before being included in any software. The diagnostic color codes are based on the commercial version of the Cirrus OCT currently available. Their normative databases may be updated and modified in the future, and the results should not be extrapolated directly to other OCT devices. Another limitation of the proposed function was that subjects with macular disease, such as age-related macular degeneration (AMD), were excluded. Age-related macular degeneration is a relevant condition that may be considered in glaucoma suspects. The important weight of the GCIPL parameters in the formula should be considered and, therefore, implementation of this function in the presence of any macular condition is not strongly recommended. Despite these limitations, this diagnostic calculator is easy to use (
Fig. 3) and includes only 9 parameters and the optic disc size as a gray color code, indicating a size not evaluated by Cirrus OCT. The formula may also assist the diagnosis of early glaucoma in cases with artifacts in the qualitative OCT analysis (
Fig. 4), and it provides a new parameter that can facilitate, in addition to other tests, the glaucoma diagnosis in the clinical practice.
In conclusion, the GCIPL and RNFL parameters had similar diagnostic value, while the combined function increased the diagnostic value of these single parameters. The combined model includes information from 8 parameters from three structures, ONH, peripapillary RNFL, and GCIPLs, and requires only 1 minute to determine the probability of glaucoma in a suspected patient using the diagnostic calculator. The function is applicable even in large and small optic discs that do not show color classification in the OCT report (gray). In light of these results, the use of predictive models using a combination of parameters from Cirrus OCT improves glaucoma detection. The availability of the multivariate function allows external validation in other datasets (from different racial and geographic origins) and its potential use in clinical practice as another tool to interpret the OCT data analysis.