PurposeThe purpose of this study was to create a vision-related quality of life (VRQoL) prediction system to identify visual field (VF) test points associated with decreased VRQoL in patients with glaucoma.MethodVRQoL score was surveyed in 164 patients with glaucoma using the ‘Sumi questionnaire’. A binocular VF was created from monocular VFs by using the integrated VF (IVF) method. VRQoL score was predicted using the ‘Random Forest’ method, based on visual acuity (VA) of better and worse eyes (better-eye and worse-eye VA) and total deviation (TD) values from the IVF. For comparison, VRQoL scores were regressed (linear regression) against: (i) mean of TD (IVF MD); (ii) better-eye VA; (iii) worse-eye VA; and (iv) IVF MD and better- and worse-eye VAs. The rank of importance of IVF test points was identified using the Random Forest method.ResultsThe root mean of squared prediction error associated with the Random Forest method (0.30 to 1.97) was significantly smaller than those with linear regression models (0.34 to 3.38, p<0.05, ten-fold cross validation test). Worse-eye VA was the most important variable in all VRQoL tasks. In general, important VF test points were concentrated along the horizontal meridian. Particular areas of the IVF were important for different tasks: peripheral superior and inferior areas in the left hemifield for the ‘letters and sentences’ task, peripheral, mid-peripheral and para-central inferior regions for the ‘walking’ task, the peripheral superior region for the ‘going out’ task, and a broad scattered area across the IVF for the ‘dining’ task.ConclusionThe VRQoL prediction model with the Random Forest method enables clinicians to better understand patients’ VRQoL based on standard clinical measurements of VA and VF.

To evaluate the peripapillary distribution of retinal nerve fiber layer thickness (RNFLT) in normal eyes using spectral-domain optical coherence tomography and to study potentially related factors. Methods: In 7 institutes in Japan, RNFLT in 7 concentric peripapillary circles with diameters ranging from 2.2 to 4.0 mm were measured using spectral-domain optical coherence tomography in 251 ophthalmologically normal subjects. Multiple regression analysis for the association of RNFLT with sex, age, axial length, and disc area was performed. Results: Retinal nerve fiber layer thickness decreased linearly from 125 to 89 µm as the measurement diameter increased (PϽ.001, mixed linear model). Retinal nerve fiber layer thickness correlated with age in all diameters (partial correlation coefficient [PCC] = −0.40 to −0.32; PϽ .001) and negatively correlated with disc area in the 2 innermost circles but positively correlated in the 3 outermost circles (PCC=−0.30 to −0.22 and 0.17 to 0.20; P Յ .005). Sex and axial length did not correlate with RNFLT (PϾ.08). The decay slope was smallest in the temporal and largest in the nasal and inferior quadrants (PϽ.001); positively correlated with disc area (PCC=0.13 to 0.51; PՅ .04); and negatively correlated with RNFLT (PCC=−0.51 to −0.15; P Յ.01). Conclusions: In normal Japanese eyes, RNFLT significantly correlated with age and disc area, but not with sex or axial length. Retinal nerve fiber layer thickness decreased linearly as the measurement diameter increased. The decay slope of RNFLT was steepest in the nasal and inferior quadrants and steeper in eyes with increased RNFLT or smaller optic discs.

Citation: Hirasawa H, Araie M, Tomidokoro A, et al. Reproducibility of thickness measurements of macular inner retinal layers using SD-OCT with or without correction of ocular rotation. Invest Ophthalmol Vis Sci. 2013;54:256254: -257054: . DOI:10.1167 PURPOSE. To evaluate the intervisit reproducibility of spectral-domain optical coherence tomography (SD-OCT) measurement of the macular retinal nerve fiber layer thickness (mRNFLT); combined ganglion cell layer and inner plexiform layer (GCLþIPL) thickness; and ganglion cell complex (GCC) thicknesses (sum of mRNFLT and GCLþIPL thicknesses) compared with that of circumpapillary RNFLT (cpRNFLT) and the effect of ocular rotation on reproducibility.METHODS. SD-OCT imaging was performed twice on different days in one eye of 58 normal subjects and 73 glaucoma patients. The reproducibility was evaluated for the entire 4.8-mm 3 4.8-mm macular area and subareas (upper and lower halves, 2 3 2, 4 3 4, and 8 3 8 grids), and the 3608, upper, and lower halves mean cpRNFLT with and without correction of ocular rotation.RESULTS. The coefficients of variation (CVs) of GCLþIPL and GCC thickness measurements averaged below 1.0% for the entire and upper and lower half macular areas, and below 4.2% in the macular subareas in normal and glaucoma eyes, which were significantly smaller (P < 0.001) than those of mRNFLT measurements in the same areas of the same eyes. The CVs of mRNFLT measurements were significantly smaller than those of the cpRNFLT only in the lower half mean area in normal eyes. The reproducibility was minimally affected by correction of ocular rotation or presence of glaucoma.CONCLUSIONS. The reproducibility of the macular (GCLþIPL) and GCC thickness measurements was better than that of mRFNLT and cpRNFLT in normal and glaucoma eyes and minimally affected by correction of ocular rotation.

PurposeTo diagnose glaucoma based on spectral domain optical coherence tomography (SD-OCT) measurements using the ‘Random Forests’ method.MethodsSD-OCT was conducted in 126 eyes of 126 open angle glaucoma (OAG) patients and 84 eyes of 84 normal subjects. The Random Forests method was then applied to discriminate between glaucoma and normal eyes using 151 OCT parameters including thickness measurements of circumpapillary retinal nerve fiber layer (cpRNFL), the macular RNFL (mRNFL) and the ganglion cell layer-inner plexiform layer combined (GCIPL). The area under the receiver operating characteristic curve (AROC) was calculated using the Random Forests method adopting leave-one-out cross validation. For comparison, AROCs were calculated based on each one of the 151 OCT parameters.ResultsThe AROC obtained with the Random Forests method was 98.5% [95% Confidence interval (CI): 97.1–99.9%], which was significantly larger than the AROCs derived from any single OCT parameter (maxima were: 92.8 [CI: 89.4–96.2] %, 94.3 [CI: 91.1–97.6] % and 91.8 [CI: 88.2–95.4] % for cpRNFL-, mRNFL- and GCIPL-related parameters, respectively; P<0.05, DeLong’s method with Holm’s correction for multiple comparisons). The partial AROC above specificity of 80%, for the Random Forests method was equal to 18.5 [CI: 16.8–19.6] %, which was also significantly larger than the AROCs of any single OCT parameter (P<0.05, Bootstrap method with Holm’s correction for multiple comparisons).ConclusionsThe Random Forests method, analyzing multiple SD-OCT parameters concurrently, significantly improves the diagnosis of glaucoma compared with using any single SD-OCT measurement.

Grid-wise analysis of macular GCC--especially using 8 × 8 grids and normative data-based cutoff values--was very useful for diagnosing early-stage glaucoma, though compensation of the disc-fovea inclination had little effect.

Predictions of VF progression can be improved by dividing the VF into small regions based on clusters of test points with similar progression rates.

ObjectivesTo develop a classifier to predict the presence of visual field (VF) deterioration in glaucoma suspects based on optical coherence tomography (OCT) measurements using the machine learning method known as the ‘Random Forest’ algorithm.DesignCase–control study.Participants293 eyes of 179 participants with open angle glaucoma (OAG) or suspected OAG.InterventionsSpectral domain OCT (Topcon 3D OCT-2000) and perimetry (Humphrey Field Analyser, 24-2 or 30-2 SITA standard) measurements were conducted in all of the participants. VF damage (Ocular Hypertension Treatment Study criteria (2002)) was used as a ‘gold-standard’ to classify glaucomatous eyes. The ‘Random Forest’ method was then used to analyse the relationship between the presence/absence of glaucomatous VF damage and the following variables: age, gender, right or left eye, axial length plus 237 different OCT measurements.Main outcome measuresThe area under the receiver operating characteristic curve (AROC) was then derived using the probability of glaucoma as suggested by the proportion of votes in the Random Forest classifier. For comparison, five AROCs were derived based on: (1) macular retinal nerve fibre layer (m-RNFL) alone; (2) circumpapillary (cp-RNFL) alone; (3) ganglion cell layer and inner plexiform layer (GCL+IPL) alone; (4) rim area alone and (5) a decision tree method using the same variables as the Random Forest algorithm.ResultsThe AROC from the combined Random Forest classifier (0.90) was significantly larger than the AROCs based on individual measurements of m-RNFL (0.86), cp-RNFL (0.77), GCL+IPL (0.80), rim area (0.78) and the decision tree method (0.75; p<0.05).ConclusionsEvaluating OCT measurements using the Random Forest method provides an accurate prediction of the presence of perimetric deterioration in glaucoma suspects.

The purpose of the study was to investigate the influence of VF and the VA on vision related quality of life (VRQoL) in advanced glaucoma. Subjects consist of 50 glaucoma patients with mean deviation (MD) less than −20 dB in at least one eye. Patients' VRQoL was assessed using the ‘Sumi questionnaire’. The impact of seven visual measures on VRQoL were compared using principal component regression: MDs of better and worse eyes with 10-2 and 24-2 Humphrey VFs, LogMAR VAs of better and worse eyes and the Esterman score. The root mean of the squared prediction error (RMSE) was calculated using leave-one-out cross validation. Better eye summary measurements were much more influential on VRQoL than corresponding worse eye measurements and Esterman score in every VRQoL task. In conclusion, in advanced glaucoma, VF parameters of the better eye are important for the VRQoL of the patient.