Objective: To determine the optimal thresholds for intereye differences in retinal nerve fiber and ganglion cell + inner plexiform layer thicknesses for identifying unilateral optic nerve lesions in multiple sclerosis. Current international diagnostic criteria for multiple sclerosis do not include the optic nerve as a lesion site despite frequent involvement. Optical coherence tomography detects retinal thinning associated with optic nerve lesions. Methods: In this multicenter international study at 11 sites, optical coherence tomography was measured for patients and healthy controls as part of the International Multiple Sclerosis Visual System Consortium. High-and low-contrast acuity were also collected in a subset of participants. Presence of an optic nerve lesion for this study was defined as history of acute unilateral optic neuritis.View this article online at wileyonlinelibrary.com.
IntroductionOptical coherence tomography (OCT) with retinal segmentation analysis is a valuable tool in assessing axonal loss and neuro-degeneration in multiple sclerosis (MS) by in-vivo imaging, delineation and quantification of retinal layers. There is evidence of deep retinal involvement in MS beyond the inner retinal layers. The ultra-structural retinal changes in MS in different MS phenotypes can reflect differences in the pathophysiologic mechanisms. There is limited data on the pattern of deeper retinal layer involvement in progressive MS (PMS) versus relapsing remitting MS (RRMS). We have compared the OCT segmentation analysis in patients with relapsing-remitting MS and progressive MS.MethodsCross-sectional study of 113 MS patients (226 eyes) (29 PMS, 84 RRMS) and 38 healthy controls (72 eyes). Spectral domain OCT (SDOCT) using the macular cube acquisition protocol (Cirrus HDOCT 5000; Carl Zeiss Meditec) and segmentation of the retinal layers for quantifying the thicknesses of the retinal layers. Segmentation of the retinal layers was carried out utilizing Orion software (Voxeleron, USA) for quantifying the thicknesses of individual retinal layers.ResultsThe retinal nerve finer layer (RNFL) (p = 0.023), the ganglion-cell/inner plexiform layer (GCIPL) (p = 0.006) and the outer plexiform layer (OPL) (p = 0.033) were significantly thinner in PMS compared to RRMS. There was significant negative correlation between the outer nuclear layer (ONL) and EDSS (r = -0.554, p = 0.02) in PMS patients. In RRMS patients with prior optic neuritis, the GCIPL correlated negatively (r = -0.317; p = 0.046), while the photoreceptor layer (PR) correlated positively with EDSS (r = 0.478; p = 0.003).ConclusionsPatients with PMS exhibit more atrophy of both the inner and outer retinal layers than RRMS. The ONL in PMS and the GCIPL and PR in RRMS can serve as potential surrogate of disease burden and progression (EDSS). The specific retinal layer predilection and its correlation with disability may reflect different pathophysiologic mechanisms and various stages of progression in MS.
Background and Objectives:Recent studies have suggested that inter-eye differences (IEDs) in peripapillary retinal nerve fiber layer (pRNFL) or ganglion cell+inner plexiform (GCIPL) thickness by spectral-domain optical coherence tomography (SD-OCT) may identify people with a history of unilateral optic neuritis (ON). However, this requires further validation. Machine learning classification may be useful for validating thresholds for OCT IEDs and for examining added utility for visual function tests, such as low-contrast letter acuity (LCLA), in the diagnosis of people with multiple sclerosis (PwMS) and for unilateral ON history.Methods:Participants were from 11 sites within the International Multiple Sclerosis Visual System (IMSVISUAL) consortium. pRNFL and GCIPL thicknesses were measured using SD-OCT. A composite score combining OCT and visual measures was compared individual measurements to determine the best model to distinguish PwMS from controls. These methods were also used to distinguish those with history of ON among PwMS. ROC curve analysis was performed on a training dataset (2/3 of cohort), then applied to a testing dataset (1/3 of cohort). Support vector machine (SVM) analysis was used to assess whether machine learning models improved diagnostic capability of OCT.Results:Among 1,568 PwMS and 552 controls, variable selection models identified GCIPL IED, average GCIPL thickness (both eyes), and binocular 2.5% LCLA as most important for classifying PwMS vs. controls. This composite score performed best, with AUC=0.89 (95% CI 0.85, 0.93), sensitivity=81% and specificity=80%. The composite score ROC curve performed better than any of the individual measures from the model (p<0.0001). GCIPL IED remained the best single discriminator of unilateral ON history among PwMS (AUC=0.77, 95% CI 0.71,0.83, sensitivity=68%, specificity=77%). SVM analysis performed comparably to standard logistic regression models.Conclusions:A composite score combining visual structure and function improved the capacity of SD-OCT to distinguish PwMS from controls. GCIPL IED best distinguished those with history of unilateral ON. SVM performed as well as standard statistical models for these classifications.Classification of Evidence:The study provides Class III evidence that SD-OCT accurately distinguishes multiple sclerosis from normal controls as compared to clinical criteria.
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