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 José Manuel, Moreno-Montañés Javier, Martinez-de-la-Casa José María, Polo Vicente, Velázquez-Villoria Álvaro, Berrozpe Clara, García-Granero Marta
AI Summary
Researchers developed a calculator combining OCT's RNFL, GCIPL, and optic disc data. This combined approach significantly improved glaucoma detection compared to individual measures, offering a better diagnostic tool.
Abstract
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 spectral-domain 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 = 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.
MeSH Terms
Shields Classification
Key Concepts6
The best parameters for detecting glaucoma using spectral-domain optical coherence tomography (OCT) were inferior RNFL, average RNFL, vertical cup/disc ratio, minimal GCIPL, and inferior-temporal GCIPL.
The combined predictive model, which used a combination of the best spectral-domain optical coherence tomography (OCT) parameters, achieved the highest area under the receiver operating characteristic curve (AUC) of 0.937 (95% confidence interval, 0.911-0.957) for detecting glaucoma.
The combined predictive model for glaucoma detection (AUC 0.937) was significantly (P = 0.0001) better than isolated spectral-domain optical coherence tomography (OCT) parameters in both early and advanced glaucoma.
The best GCIPL, RNFL, and optic disc parameters measured by spectral-domain optical coherence tomography (OCT) showed a similar ability to detect glaucoma.
A diagnostic calculator, based on a combined multivariate model using spectral-domain optical coherence tomography (OCT) parameters, achieved good classification of participants in both the study and validation groups for glaucoma detection.
A diagnostic calculator for detecting glaucoma was developed and validated using a combination of retinal nerve fiber layer (RNFL), retinal ganglion cell-inner plexiform (GCIPL), and optic disc parameters measured by spectral-domain optical coherence tomography (OCT).
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