Macular ganglion cell/inner plexiform layer measurements by spectral domain optical coherence tomography for detection of early glaucoma and comparison to retinal nerve fiber layer measurements.
OBJECTIVE
To evaluate the performance of ganglion cell layer/inner plexiform layer (GCL/IPL) measurements with spectral-domain optical coherence tomography (Cirrus HD-OCT) for detection of early glaucoma and to compare results to retinal nerve fiber layer (RNFL) measurements.


METHODS
Cross-sectional prospective diagnostic study.


METHODS
We enrolled 99 subjects, including 59 eyes with glaucoma (47 subjects) (mean deviation >-6.0 dB) and 91 normal eyes (52 subjects). Patients underwent biometry and peripapillary and macular OCT imaging. Performance of the GCL/IPL and RNFL algorithms was evaluated with area under receiver operating characteristic curves (AUC), likelihood ratios, and sensitivities/specificities adjusting for covariates. Combination of best parameters was explored.


RESULTS
Average (SD) mean deviation in the glaucoma group was -2.5 (1.9) dB. On multivariate analyses, age (P < 0.001) and axial length (P = 0.03) predicted GC/IPL measurements in normal subjects. No significant correlation was found between average or regional GC/IPL thickness and respective outer retina (OR) thickness measurements (P > 0.05). Average RNFL thickness performed better than average GCL/IPL measurements for detection of glaucoma (AUC = 0.964 vs 0.937; P = 0.04). The best regional measures from each algorithm (inferior quadrant RNFL vs minimum GCL/IPL) had comparable performances (P = 0.78). Entering the GC/IPL to OR ratio into prediction models did not enhance the performance of the GCL/IPL measures. Combining the best parameters from each algorithm improved detection of glaucoma (P = 0.04).


CONCLUSIONS
Regional GCL/IPL measures derived from Cirrus HD-OCT performed as well as regional RNFL outcomes for detection of early glaucoma. Using the GC/IPL to OR ratio did not enhance the performance of GCL/IPL parameters. Combining the best measures from the 2 algorithms improved detection of glaucoma.
Glaucoma Division, Jules Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California. Electronic address: nouri-mahdavi@jsei.ucla.edu.
Kouros
K
Nouri-Mahdavi
Sara
S
Nowroozizadeh
Nariman
N
Nassiri
Nila
N
Cirineo
Shane
S
Knipping
Joann
J
Giaconi
Joseph
J
Caprioli
D000368:Aged; D000465:Algorithms; D019540:Area Under Curve; D001699:Biometry; D003430:Cross-Sectional Studies; D042241:Early Diagnosis; D005260:Female; D005902:Glaucoma, Open-Angle / Q000175:diagnosis*; D006801:Humans; D007429:Intraocular Pressure; D008297:Male; D008875:Middle Aged; D009412:Nerve Fibers / Q000473:pathology*; D009901:Optic Nerve Diseases / Q000175:diagnosis*; D011446:Prospective Studies; D012372:ROC Curve; D015203:Reproducibility of Results; D012165:Retinal Ganglion Cells / Q000473:pathology*; D012680:Sensitivity and Specificity; D041623:Tomography, Optical Coherence / Q000379:methods*; D058609:Visual Field Tests