Scientific publications

A Diagnostic Calculator for Detecting Glaucoma on the Basis of Retinal Nerve Fiber Layer, Optic Disc, and Retinal Ganglion Cell Analysis by Optical Coherence Tomography

Larrosa JM (1), Moreno-Montañés J (2), Martinez-de-la-Casa JM (3), Polo V (1), Velázquez-Villoria Á (2), Berrozpe C (3), García-Granero M (4).
(1) Ophthalmology Service Hospital Universitario Miguel Servet, Faculty of Medicine, University of Saragossa, Saragossa, Spain.
(2) Department of Ophthalmology, Clínica Universidad de Navarra, Faculty of Medicine, Universidad of Navarra, Pamplona, Spain.
(3) Ophthalmology Service, Hospital Clínico San Carlos, Faculty of Medicine, Universidad Complutense de Madrid, Madrid, Spain.
(4) Unidad de Estadística, Department of Biochemistry and Genetics, University of Navarra, Pamplona, Spain. 

Magazine: Investigative Ophthalmology & Visual Science

Date: Oct 1, 2015



The purpose of this study was to develop and validate a multivariate predictive model to detect glaucoma by using a combination of retinal nerve fiber layer (RNFL), retinal ganglion cell-inner plexiform (GCIPL), and optic disc parameters measured using spectral-domain optical coherence tomography (OCT).


Five hundred eyes from 500 participants and 187 eyes of another 187 participants were included in the study and validation groups, respectively. Patients with glaucoma were classified in five groups based on visual field damage. Sensitivity and specificity of all glaucoma OCT parameters were analyzed.

Receiver operating characteristic curves (ROC) and areas under the ROC (AUC) were compared. Three predictive multivariate models (quantitative, qualitative, and combined) that used a combination of the best OCT parameters were constructed. A diagnostic calculator was created using the combined multivariate model.


The best AUC parameters were: inferior RNFL, average RNFL, vertical cup/disc ratio, minimal GCIPL, and inferior-temporal GCIPL. Comparisons among the parameters did not show that the GCIPL parameters were better than those of the RNFL in early and advanced glaucoma.

The highest AUC was in the combined predictive model (0.937; 95% confidence interval, 0.911-0.957) and was significantly (P = 0.0001) higher than the other isolated parameters considered in early and advanced glaucoma. The validation group displayed similar results to those of the study group.


Best GCIPL, RNFL, and optic disc parameters showed a similar ability to detect glaucoma. The combined predictive formula improved the glaucoma detection compared to the best isolated parameters evaluated. The diagnostic calculator obtained good classification from participants in both the study and validation groups.

CITATION  Invest Ophthalmol Vis Sci. 2015 Oct;56(11):6788-95. doi: 10.1167/iovs.15-17176

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