Citation: Larrosa JM, Moreno-Montañés J, Martinez-de-la-Casa JM, et al. 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. Invest Ophthalmol Vis Sci. 2015;56:6788-6795. DOI:10.1167/iovs.15-17176 PURPOSE. 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 spectraldomain optical coherence tomography (OCT). METHODS.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. RESULTS.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 ¼ 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.CONCLUSIONS. 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.

Glaucoma is the leading cause of irreversible blindness worldwide and is characterized by the progressive degeneration of the optic nerve. Intraocular pressure (IOP), which is considered to be the main risk factor for glaucoma development, builds up in response to the resistance (resistance to what?) provided by the trabecular meshwork (TM) to aqueous humor (AH) outflow. Although the TM and its relationship to AH outflow have remained at the forefront of scientific interest, researchers remain uncertain regarding which mechanisms drive the deterioration of the TM. Current tissue‐engineering fabrication techniques have come up with promising approaches to successfully recreate the TM. Nonetheless, more accurate models are needed to understand the factors that make glaucoma arise. In this review, we provide a chronological evaluation of the technological milestones that have taken place in the field of glaucoma research, and we conduct a comprehensive comparison of available TM fabrication technologies. Additionally, we also discuss AH perfusion platforms, since they are essential for the validation of these scaffolds, as well as pressure–outflow relationship studies and the discovery of new IOP‐reduction therapies.

Purpose To clinically validate the diagnostic ability of two optical coherence tomography (OCT)-based glaucoma diagnostic calculators (GDCs). Methods We conducted a retrospective, consecutive sampling of 76 patients with primary open-angle glaucoma, 107 glaucoma suspects, and 67 controls. Demographics, reliable visual field testing, and macular and optic disc OCT were collected. The reference diagnosis was compared against the probability of having glaucoma obtained from two GDCs derived from multivariate logistic regressions using quantitative and qualitative (GDC1) or only quantitative (GDC2) OCT data. The discrimination (area under the curve [AUC]) and calibration (calibration plots) were compared for both calculators and the best OCT parameters. Results GDC2 was able to identify 46.9% more suspects and 14.7% more glaucomatous eyes than GDC1. Both GDCs obtained the highest discriminative ability in glaucomatous eyes (GDC1 AUC = 0.949; GDC2 = 0.943 vs inferior peripapillary retinal nerve fiber layer [pRNFL] = 0.931; P = 0.43). The discriminating ability was not as good for glaucoma suspects, but the GDCs were not inferior to pRNFL (GDC 1 AUC = 0.739; GDC2 = 0.730; inferior pRNFL = 0.760; P = 0.54) and GDC2 was still able to correctly identify up to 30.8% more cases than the conventional OCT classification. Calibration showed risk underestimation for both groups and calculators, but it was better in GDC2 and in patients with glaucoma. Conclusions OCT-based calculators showed an excellent diagnostic performance in glaucomatous eyes. GDC2 was able to identify approximately 30% more cases than the conventional pRNFL inferior OCT classification in both groups, suggesting a potential role of these composite scores in clinical practice. Translational Relevance These OCT-based calculators may improve glaucoma diagnosis in clinical care.