Hiroyo Hirasawa

Peripapillary Retinal Nerve Fiber Layer Thickness Determined by Spectral-Domain Optical Coherence Tomography in Ophthalmologically Normal Eyes

OBJECTIVES 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 (P < .001, mixed linear model). Retinal nerve fiber layer thickness correlated with age in all diameters (partial correlation coefficient [PCC] = -0.40 to -0.32; P < .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 ≤ .005). Sex and axial length did not correlate with RNFLT (P > .08). The decay slope was smallest in the temporal and largest in the nasal and inferior quadrants (P < .001); positively correlated with disc area (PCC = 0.13 to 0.51; P ≤ .04); and negatively correlated with RNFLT (PCC = -0.51 to -0.15; P ≤ .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.

Identifying areas of the visual field important for quality of life in patients with glaucoma.

Purpose The 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. Method VRQoL 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. Results The 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. Conclusion The VRQoL prediction model with the Random Forest method enables clinicians to better understand patients’ VRQoL based on standard clinical measurements of VA and VF.

Reproducibility of thickness measurements of macular inner retinal layers using SD-OCT with or without correction of ocular rotation.

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 (GCL+IPL) thickness; and ganglion cell complex (GCC) thicknesses (sum of mRNFLT and GCL+IPL 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 × 4.8-mm macular area and subareas (upper and lower halves, 2 × 2, 4 × 4, and 8 × 8 grids), and the 360°, upper, and lower halves mean cpRNFLT with and without correction of ocular rotation. RESULTS The coefficients of variation (CVs) of GCL+IPL 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 (GCL+IPL) 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.

Ultrasound Biomicroscopy in Narrow Peripheral Anterior Chamber Eyes With or Without Peripheral Anterior Synechiae

AimTo compare the configurations of the anterior ocular segment including the anterior chamber (AC) angle and ciliary body between eyes with a narrow AC angle (ACA) with and without peripheral anterior synechiae (PAS). Patients and MethodsOne hundred and one eyes of 101 consecutive patients with a temporal peripheral AC depth one-quarter of the corneal thickness or less were included. Gonioscopy and ultrasound biomicroscopy were performed under light and dark conditions. The existence of PAS was further confirmed with compression gonioscopy with indentation. Eyes with findings suggestive of plateau iris configuration or those with glaucomatous optic neuropathy were carefully excluded. The biometric parameters including the ACA, the angle opening distance at 500 μm, the trabecular-ciliary process distance, the iris-zonule distance, and the scleral-ciliary process angle were determined. ResultsPAS were found in 43 (43%) of the 101 eyes. There were no differences in age, refractive error, or intraocular pressure between PAS-positive and PAS-negative eyes (P>0.1). ACA, iris-zonule distance, scleral-ciliary process angle under light and/or dark conditions were significantly smaller in the PAS-positive eyes than in the PAS-negative eyes (P<0.05). ConclusionsShallow peripheral AC depth and relatively anteriorly located ciliary body was significantly associated with the presence of PAS in eyes with a narrow ACA.

Estimating the Usefulness of Humphrey Perimetry Gaze Tracking for Evaluating Structure–Function Relationship in Glaucoma

Purpose We have previously reported that fixation loss (FL) rates, false-positive (FP) rates, and gaze tracking (GT) parameters (average tracking failure frequency per stimulus [TFF], average blinking frequency [BF], average frequency of eye movements between 1° and 2° [move1-2], between 3° and 5° [move3-5], and equal to or more than 6° [move≥6]) are related to the over- or underestimation of visual field (VF) results. The purpose of the current study was to validate these results by investigating the effect of implementing the GT parameters on the relationship between VF results and optical coherence tomography (OCT) measurements. Methods Two hundred forty-four eyes of 155 open-angle glaucoma patients were included. Vision fixation during VF tests with the Humphrey Field Analyzer (24-2 SITA standard) was evaluated using the gaze fixation chart at the bottom of the VF printout. Mean total deviation (mTD) values were calculated, and their relationship with OCT-determined circumpapillary retinal nerve fiber layer (cp-RNFL), OCT-determined macular ganglion cell complex (GCC) thickness, and axial length was investigated using the corrected Akaike Information Criterion (AICc) in linear mixed modeling. Results In the best model, average total cpRNFL thickness, average total GCC thickness, axial length, FL, FP, move3-5, move≥6, TFF, and BF were selected as significant predictors (mTD = 2.1 + 0.097 × average total cpRNFL thickness + 0.089 × average total GCC thickness - 0.94 × axial length + 2.7 × FL + 7.2 × FP - 7.0 × move3-5 - 1.8 × move≥6 - 4.2 × TFF - 1.7 × BF). Conclusions Both GT parameters and classic VF reliability indices had significant influence on the structure-function relationship analysis in glaucoma.

Discriminating between Glaucoma and Normal Eyes Using Optical Coherence Tomography and the ‘Random Forests’ Classifier

Purpose To diagnose glaucoma based on spectral domain optical coherence tomography (SD-OCT) measurements using the ‘Random Forests’ method. Methods SD-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. Results The 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). Conclusions The Random Forests method, analyzing multiple SD-OCT parameters concurrently, significantly improves the diagnosis of glaucoma compared with using any single SD-OCT measurement.

Diagnosis of Early-Stage Glaucoma by Grid-Wise Macular Inner Retinal Layer Thickness Measurement and Effect of Compensation of Disc-Fovea Inclination.

PURPOSE To evaluate grid-wise analyses of macular inner retinal layer thicknesses and effect of compensation of disc-fovea inclination for diagnosing early-stage glaucoma. METHODS Spectral-domain optical coherence tomography measurements over a 6.0 × 6.0-mm macular area were prospectively obtained in 104 eyes of 104 patients with early-stage glaucoma with a mean deviation of -1.8 ± 1.9 dB and 104 eyes of 104 age- and refraction-matched normal subjects. Macular retinal nerve fiber layer (mRNFL), ganglion cell-inner plexiform layer (GCIPL) combined, and ganglion cell complex (GCC) thickness of the entire area and each subdivided macular grid were determined to compare diagnostic capability for glaucoma using receiver operating characteristic curves and various normal cutoff values for each layer thickness and number of grids flagged as abnormal. Diagnostic capability was then compared with that of circumpapillary RNFL (cpRNFL) measurements. Effects of compensation of inclination of disc-fovea line by reconfiguration of the macular grid were also studied. RESULTS Macular inner retinal layer analyses using 8 × 8 grids generally yielded higher diagnostic capability. Only the 8 × 8 grid GCC analyses using the various normal cutoff values yielded a sensitivity ≥ 0.90 with specificity ≥ 0.95 under several conditions in discriminating the glaucoma eyes. In glaucoma and normal eyes with both reliable cpRNFL and macular measurements, the best sensitivity/specificity were 0.98/0.95 for the 8 × 8 grid-mRNFL analysis and 0.93/0.96 for the 8 × 8 grid GCC analysis using various normal cutoff values, which were better than that (0.78/0.95) for clock-hour cpRNFL analysis (P = 0.001). Compensation of the disc-fovea inclination did not improve the diagnostic capability. CONCLUSIONS 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.

Evaluation of various machine learning methods to predict vision-related quality of life from visual field data and visual acuity in patients with glaucoma

Background/aims We investigated whether it was useful to use machine learning algorithms to predict patients’ vision related quality of life (VRQoL) from visual field (VF) and visual acuity (VA). Methods VRQoL was surveyed in 164 glaucomatous patients using the Sumi questionnaire. Their VRQoL score was predicted using machine learning algorithms (Random Forest, gradient boosting, support vector machine) based on total deviation (TD) values from integrated VF (IVF), VA, age and gender. For comparison, VRQoL score was predicted using standard linear regression with mean of IVF, TD values, and VA, and also the stepwise model selection by Akaike Information Criterion. Prediction error was calculated as root mean of the squared prediction error (RMSE) associated with the leave one out cross validation. Results RMSEs associated with general VRQoL score were smaller for the machine learning algorithms (1.99 to 2.21) compared with the standard linear model and the stepwise model selection (2.35 to 3.20). A similar tendency was found in each individual VRQoL task score. Conclusions We found that it was advantageous to use machine learning methods to predict VRQoL accurately. These statistical methods could be used to help clinicians better understand patients’ VRQoL without the need for extra tests other than standard VA and VF.

Cross-sectional study: Does combining optical coherence tomography measurements using the 'Random Forest' decision tree classifier improve the prediction of the presence of perimetric deterioration in glaucoma suspects?

Objectives To 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. Design Case–control study. Participants 293 eyes of 179 participants with open angle glaucoma (OAG) or suspected OAG. Interventions Spectral 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 measures The 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. Results The 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). Conclusions Evaluating OCT measurements using the Random Forest method provides an accurate prediction of the presence of perimetric deterioration in glaucoma suspects.

Detection of Progression of Glaucomatous Visual Field Damage Using the Point-Wise Method with the Binomial Test

Purpose To compare the performance of newly proposed point-wise linear regression (PLR) with the binomial test (binomial PLR) against mean deviation (MD) trend analysis and permutation analyses of PLR (PoPLR), in detecting global visual field (VF) progression in glaucoma. Methods 15 VFs (Humphrey Field Analyzer, SITA standard, 24-2) were collected from 96 eyes of 59 open angle glaucoma patients (6.0 ± 1.5 [mean ± standard deviation] years). Using the total deviation of each point on the 2nd to 16th VFs (VF2-16), linear regression analysis was carried out. The numbers of VF test points with a significant trend at various probability levels (p<0.025, 0.05, 0.075 and 0.1) were investigated with the binomial test (one-side). A VF series was defined as “significant” if the median p-value from the binomial test was <0.025. Similarly, the progression analysis was carried out using only second to sixth VFs (VF2-6). The performance of each method was evaluated using the ‘consistency measures’; proportion both significant (PBS): both VF series (VF2-6 and VF2-16) were “significant”, proportion both were not significant (PBNS): both were “not significant”, proportion inconsistently significant (PIS): VF2-16 was “not significant” but VF2-6 was “significant”. A similar analysis was carried out using VF2-7 and VF2-15 series, and the performance was compared with MD trend analysis and PoPLR. Results The PBS of the binomial PLR method (0.14 to 0.86) was significantly higher than MD trend analysis (0.04 to 0.89) and PoPLR (0.09 to 0.93). The PIS of the proposed method (0.0 to 0.17) was significantly lower than the MD approach (0.0 to 0.67) and PoPLR (0.07 to 0.33). The PBNS of the three approaches were not significantly different. Conclusions The binomial BLR method gives more consistent results than MD trend analysis and PoPLR, hence it will be helpful as a tool to ‘flag’ possible VF deterioration.