Macular and Retinal Nerve Fiber Layer Thickness: Which Is More Helpful in the Diagnosis of Glaucoma? (original) (raw)
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Journal of Current Glaucoma Practice, 2020
Aim and objective: To appraise the validity of measuring macular ganglion cell layer (mGCL) thickness as an indicator of early glaucoma, as compared to measurement of peripapillary retinal nerve fiber layer (pRNFL) thickness. Materials and methods: This was a single-center, single-observer, cross-sectional, retrospective study. Records included Filipino adult patients seen from January 2017 onward. Patients underwent testing of both automated visual field (VF) testing with either Humphrey Visual Field Analyzer (24-2 SITA program) or Octopus 311 (G1 program), and standard Spectral-Domain Optical Coherence Tomography (Cirrus HD-OCT 5000). Modified Hodapp-Anderson-Parrish criteria were used to classify subjects as either healthy, suspect, or early glaucomatous eyes. Thickness changes were directly observed through optical coherence tomography. Area under receiver operating curve (AUC) analysis was used to determine ability of mGCL and pRNFL to discriminate between healthy and early glaucomatous states. Results: A total of 96 eyes were included. Progressive thinning for all parameters was noted for both pRNFL and mGCL from healthy to suspect to early glaucomatous eyes. The highest AUC of 0.744 was seen in average pRNFL of healthy vs early glaucomatous eyes. However, AUC values for both pRNFL and mGCL were all above 0.500. Conclusion: Measurements of mGCL thickness in Filipino patients exhibit comparable performance to pRNFL measurements in detecting early anatomic glaucomatous change. It is a tool that can be utilized for early glaucoma detection in addition to current standard diagnostic tests. Clinical significance: This study, the first to be performed on Filipino patients, validates using mGCL thickness as a good parameter in discriminating between normal and early glaucoma patients for this particular population and Ethnic group.
Purpose – To evaluate macular thickness and peripapillary RNFL thickness to analyse and monitorglaucoma patient Material and method –The present study was conducted in 200 patients who attended OPD of upgraded department of ophthalmology,NSCB Medical college during academic session October 2015-November2017.All All selected patients underwent a complete examination including visual field examination by humphrey'sautomated perimeter and macular scan with retinal nerve fiber layer (RNFL) scan by Spectral Domain-OCT (SD-OCT)after taking proper history and other necessary clinical examination. Correlation of OCT data with visual field defect was evaluated. Result –Macular thickness and RNFL thickness values were significantly reduced in glaucomatous eyes(Avg.GCIPL58±13.19µm,min.GCIPL42.93± 16.92µm and Avg.RNFL thickness58.1415±.76µm) than in healthy eyes(Avg.GCIPL 81.31±4.64µm,min.GCIPL 77.99± 4.95µm and Avg.RNFL thickness91.91. ±6.85µm)and it was correlated well with visual field global indices like MD(-9.07± 6.23) and PSD(6.34±3.36) and average CD ratio (0.75± 0.09) Conclusion – Quantitative measurement of macular thickness and peripapillary RNFL thickness using OCT correlates with visual field global indices in glaucoma patient .In this way we can say that macular and RFNL thickness analysis are excellent modality of analysing and monitoring glaucoma patient.
Purpose: The aim of this study was to evaluate the discrimination capabilities of macular and peripapillary retinal nerve fiber layer (pRNFL) thickness parameters as measured using spectral domain optical coherence tomography (SD-OCT) between primary open-angle glaucoma (POAG) and normal-tension glaucoma (NTG). Methods: A total of 90 subjects were enrolled: 30 healthy subjects, 30 subjects with POAG and 30 subjects with NTG, consecutively. Retinal nerve fiber layer thickness, macular thickness and volume measurements were obtained with circular and radial SD-OCT scans. All parameters were compared between groups using an analysis of variance test. Areas under receiver-operating characteristic (AROC) curves with sensitivities at specificities greater than or equal to 90 per cent were generated to compare discrimination capabilities of various parameters between POAG and NTG. Results: Macular thickness and volume measurements were the highest in normal subjects, followed by NTG and POAG (p < 0.05). Average retinal nerve fiber layer thickness had perfect discrimination for normal-POAG (AROC: 1.000; sensitivity: 100 per cent) and near perfect discrimination for normal-NTG (AROC: 0.979; sensitivity: 93 per cent) as well as NTG-POAG pairs (AROC: 0.900; sensitivity: 60 per cent). Inferior outer macular thickness (IOMT) and total volume were the best macular thickness and volume parameters having similar AROCs and sensitivities between normal and POAG (IOMT, AROC: 0.987; sensitivity: 92 per cent and total volume, AROC: 0.997; sensitivity: 97 per cent), normal and NTG (IOMT, AROC: 0.862, sensitivity: 47 per cent and total volume, AROC: 0.898, sensitivity: 67 per cent) and also between NTG and POAG (IOMT, AROC: 0.910, sensitivity: 53 per cent and total volume, AROC: 0.922, sensitivity: 77 per cent). In each comparison group, there was no statistically significant difference in AROCs between average retinal nerve fiber layer and inferior outer macular thickness, as well as total volume. Conclusions: The macular parameters offer comparable performance to pRNFL parameters for the discrimination of NTG and POAG. Average retinal nerve fiber layer thickness, total macular volume and inferior outer macular thickness were the best SD-OCT parameters with superior discriminating capabilities.
Investigative Opthalmology & Visual Science, 2015
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.
Acta Ophthalmologica, 2022
Purpose: Detection of early glaucoma remains limited with the conventional analysis of the retinal nerve fiber layer (RNFL). This study assessed whether compensating the RNFL thickness for multiple demographic and anatomic factors improves the detection of glaucoma. Design: Cross-sectional study. Participants: Three hundred eighty-seven patients with glaucoma and 2699 healthy participants. Methods: Two thousand six hundred ninety-nine healthy participants were enrolled to construct and test a multivariate compensation model, which then was applied in 387 healthy participants and 387 patients with glaucoma (early glaucoma, n ¼ 219; moderate glaucoma, n ¼ 97; and advanced glaucoma, n ¼ 71). Participants underwent Cirrus spectral-domain OCT (Carl Zeiss Meditec) imaging of the optic disc and macular cubes. Compensated RNFL thickness was generated based on ethnicity, age, refractive error, optic disc (ratio, orientation, and area), fovea (distance and angle), and retinal vessel density. The RNFL thickness measurements and their corresponding areas under the receiver operating characteristic curve (AUCs) were obtained. Main Outcome and Measures: Measured and compensated RNFL thickness measurements. Results: After applying the Asian-specific compensation model, the standard deviation of RNFL thickness reduced, where the effect was greatest for Chinese participants (16.9%), followed by Malay participants (13.9%), and Indian participants (12.1%). Multivariate normative comparison outperformed measured RNFL for discrimination of early glaucoma (AUC, 0.90 vs. 0.85; P < 0.001), moderate glaucoma (AUC, 0.94 vs. 0.91; P < 0.001), and advanced glaucoma (AUC, 0.98 vs. 0.96; P < 0.001). Conclusions: The multivariate normative database of RNFL showed better glaucoma discrimination capability than conventional age-matched comparisons, suggesting that accounting for demographic and anatomic variance in RNFL thickness may have usefulness in improving glaucoma detection.
Journal of Evidence Based Medicine and Healthcare, 2015
To study the RNFL thickness measured by stratus optical coherence tomography (OCT) patients with primary open angle glaucoma (POAG) and normal subjects, analyse the quadrant which is most efficient parameter for detecting glaucomatous damage and its correlation with visual fields. MATERIAL AND METHODS: This is a cross-sectional study of 50 glaucomatous eyes and 50 normal subjects. RNFL thickness was measured in different quadrants using stratus optical coherence tomography. RESULTS: The RNFL thickness measured by OCT in 50 glaucomatous and 50 normal eyes showed that the Inferior RNFL thickness in POAG is 77.54±31.11 compared to normal subjects where Inferior RNFL thickness is 124.96±16.74 (P<0.001). The Superior RNFL thickness in POAG is 78.32±34.81 compared to normal subjects where Superior RNFL thickness is 113.86±15.07 (P<0.001). The Nasal RNFL thickness in POAG is 53.52±13.88 compared to normal subjects where Nasal RNFL thickness is 78.103±17.87 (P<0.001). The Temporal RNFL thickness in POAG is 49.72±18.01 compared to normal subjects where Temporal RNFL thickness is 60.17±12.15 (P<0.001). The Average RNFL thickness in POAG is 63.94±18.01 compared to normal subjects where Average RNFL thickness is 97.97±9.59 (P<0.001). Both mean deviation (MD) and pattern standard deviation (PSD) showed a significant correlation with all the RNFL thickness parameters in eyes with glaucoma (pearson correlation coefficient >0.4). CONCLUSION: RNFL thickness measured on OCT may serve as useful adjunct in accurately detecting glaucoma. Average and inferior RNFL thicknesses are among the most efficient parameters for detecting glaucoma correlating with the visual field changes.
IOSR Journal of Dental and Medical Sciences, 2013
Purpose: To evaluate the diagnostic accuracy of retinal nerve fibre layer thickness (RNFLT), ganglion cell complex (GCC), and optic disc measurements made with the RTVue-100 Fourier-domain optical coherence tomography (OCT) to detect glaucoma in an Asian population. Methods: One randomly selected eye of 532 Asian patients (132 healthy, 112 ocular hypertensive, 134 preperimetric glaucoma, and 154 perimetric glaucoma eyes) was evaluated. Results: Using the software-provided classification, the Total population sensitivity for GCC was 82.7% , RNFLT parameters did not exceed 73.6% and for the optic nerve head 62.8. Specificity was high (92.6-100%) for most RNFLT and GCC parameters, but low (74.0-76.4%) for the optic disc parameters. Positive predictive value (PPV) varied between 96.1 and 100% for the main RNFLT parameters, 94.6 and 100% for the 16 RNFLT sectors, 96.4 and 99.0% for the GCC parameters, but did not exceed 86.3% for any of the optic disc parameters. Positive likelihood ratio (PLR) was higher than 10 for average, inferior and superior RNFLT (28.5 to infinite), 12 of the 16 RNFLT sectors (14.6 to infinite), and three of the four GCC parameters (40.0 to 48.6). No optic disc parameter had a PLR higher than 2.0. Conclusion: RNFLT and GCC parameters of the RTVue-100 Fourier-domain OCT showed moderate sensitivity but high specificity, positive predictive value and PLR for detection of glaucoma. The optic disc parameters had lower diagnostic accuracy than the RNFLT and GCC parameters.
Different Clinical Parameters to Diagnose Glaucoma Disease: A Review
International Journal of Computer Applications, 2015
Glaucoma is a severe human eye disease that causes permanent loss of vision. The main cause of glaucoma eye disease is the continuous loss of retinal nerve fiber layers due to the increase in the intra ocular pressure inside the eyes. The function of these retinal nerve fibers is the transformation of recognized object information in the form of signals to the brain, where these signals are recognized as object. Damages to these nerve fibers generate blind spots and these blind spots leads to permanent blindness. Therefore, Retinal Nerve Fiber Layer Thickness is the main parameter to diagnose glaucoma. Other parameters also leading to glaucoma are Intraocular Pressure, Vertical Cup to Disc Ratio, Neuro Retinal Rim Thickness, Central Cornea Thickness, Inferior Superior Nasal and Temporal Sector Ratio etc. Therefore, the identification of these parameters plays the major role in glaucoma assessment, since it allows timely treatment to prevent the vision loss caused by glaucoma. To estimate these parameters, clinical instruments such as Tonometry, Ophthalmoscopy, Heidelberg Retinal Tomography, Perimetry, Pachymetry, Optical Coherence Tomography, GDx etc are adopted. This paper presents the various parameters, as mentioned above, used to analyze and diagnose the Glaucoma disease and associated advantages, disadvantages and the different instruments used to analyze each clinical parameter.