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Donica VC, Alexa AI, Pavel IA, Danielescu C, Ciapă MA, Donica AL, Bogdănici CM. The Evolvement of OCT and OCT-A in Identifying Multiple Sclerosis Biomarkers. Biomedicines 2023; 11:3031. [PMID: 38002031 PMCID: PMC10669604 DOI: 10.3390/biomedicines11113031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 11/09/2023] [Accepted: 11/10/2023] [Indexed: 11/26/2023] Open
Abstract
The prevalence of multiple sclerosis (MS) has been increasing among young people in developing countries over the last years. With the continuous development of new technology, the diagnosis and follow-up of these patients has received new parameters that physicians may use in their practice. This paper reviews the main biomarkers identified through Optical Coherence Tomography Angiography (OCT-A) involved in the development and progression of MS and investigates the role it may have in detecting changes to the central nervous system (CNS).
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Affiliation(s)
- Vlad Constantin Donica
- Department of Ophthalmology, Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, University Street, No. 16, 700115 Iasi, Romania; (V.C.D.); (C.D.); (C.M.B.)
| | - Anisia Iuliana Alexa
- Department of Ophthalmology, Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, University Street, No. 16, 700115 Iasi, Romania; (V.C.D.); (C.D.); (C.M.B.)
| | - Irina Andreea Pavel
- Department of Ophthalmology, Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, University Street, No. 16, 700115 Iasi, Romania; (V.C.D.); (C.D.); (C.M.B.)
| | - Ciprian Danielescu
- Department of Ophthalmology, Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, University Street, No. 16, 700115 Iasi, Romania; (V.C.D.); (C.D.); (C.M.B.)
| | | | | | - Camelia Margareta Bogdănici
- Department of Ophthalmology, Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, University Street, No. 16, 700115 Iasi, Romania; (V.C.D.); (C.D.); (C.M.B.)
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Bsteh G, Hegen H, Altmann P, Auer M, Berek K, Di Pauli F, Kornek B, Krajnc N, Leutmezer F, Macher S, Rommer PS, Zebenholzer K, Zulehner G, Zrzavy T, Deisenhammer F, Pemp B, Berger T. Diagnostic Performance of Adding the Optic Nerve Region Assessed by Optical Coherence Tomography to the Diagnostic Criteria for Multiple Sclerosis. Neurology 2023; 101:e784-e793. [PMID: 37400245 PMCID: PMC10449446 DOI: 10.1212/wnl.0000000000207507] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 04/24/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND AND OBJECTIVES The optic nerve has been recommended as an additional region for demonstrating dissemination in space (DIS) in diagnostic criteria for multiple sclerosis (MS). The aim of this study was to investigate whether adding the optic nerve region as determined by optical coherence tomography (OCT) as part of the DIS criteria improves the 2017 diagnostic criteria. METHODS From a prospective observational study, we included patients with a first demyelinating event who had complete information to assess DIS and a spectral domain OCT scan obtained within 180 days. Modified DIS criteria (DIS + OCT) were constructed by adding the optic nerve to the current DIS regions based on validated thresholds for OCT intereye differences. Time to second clinical attack was the primary endpoint. RESULTS We analyzed 267 patients with MS (mean age 31.3 years [SD 8.1], 69% female) during a median observation period of 59 months (range: 13-98). Adding the optic nerve as a fifth region improved the diagnostic performance by increasing accuracy (DIS + OCT 81.2% vs DIS 65.6%) and sensitivity (DIS + OCT 84.2% vs DIS 77.9%) without lowering specificity (DIS + OCT 52.2% vs DIS 52.2%). Fulfilling DIS + OCT criteria (≥2 of 5 DIS + OCT regions involved) indicated a similar risk of a second clinical attack (hazard ratio [HR] 3.6, CI 1.4-14.5) compared with a 2.5-fold increased risk when fulfilling DIS criteria (HR 2.5, CI 1.2-11.8). When the analysis was conducted according to topography of the first demyelinating event, DIS + OCT criteria performed similarly in both optic neuritis and nonoptic neuritis. DISCUSSION Addition of the optic nerve, assessed by OCT, as a fifth region in the current DIS criteria improves diagnostic performance by increasing sensitivity without lowering specificity. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that adding the optic nerve as determined by OCT as a fifth DIS criterion to the 2017 McDonald criteria improves diagnostic accuracy.
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Affiliation(s)
- Gabriel Bsteh
- From the Department of Neurology (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Comprehensive Center for Clinical Neurosciences and Mental Health (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Department of Neurology (H.H., M.A., K.B., F.D.P., F.D.), Medical University of Innsbruck; and Department of Ophthalmology (B.P.), Medical University of Vienna, Austria.
| | - Harald Hegen
- From the Department of Neurology (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Comprehensive Center for Clinical Neurosciences and Mental Health (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Department of Neurology (H.H., M.A., K.B., F.D.P., F.D.), Medical University of Innsbruck; and Department of Ophthalmology (B.P.), Medical University of Vienna, Austria
| | - Patrick Altmann
- From the Department of Neurology (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Comprehensive Center for Clinical Neurosciences and Mental Health (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Department of Neurology (H.H., M.A., K.B., F.D.P., F.D.), Medical University of Innsbruck; and Department of Ophthalmology (B.P.), Medical University of Vienna, Austria
| | - Michael Auer
- From the Department of Neurology (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Comprehensive Center for Clinical Neurosciences and Mental Health (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Department of Neurology (H.H., M.A., K.B., F.D.P., F.D.), Medical University of Innsbruck; and Department of Ophthalmology (B.P.), Medical University of Vienna, Austria
| | - Klaus Berek
- From the Department of Neurology (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Comprehensive Center for Clinical Neurosciences and Mental Health (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Department of Neurology (H.H., M.A., K.B., F.D.P., F.D.), Medical University of Innsbruck; and Department of Ophthalmology (B.P.), Medical University of Vienna, Austria
| | - Franziska Di Pauli
- From the Department of Neurology (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Comprehensive Center for Clinical Neurosciences and Mental Health (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Department of Neurology (H.H., M.A., K.B., F.D.P., F.D.), Medical University of Innsbruck; and Department of Ophthalmology (B.P.), Medical University of Vienna, Austria
| | - Barbara Kornek
- From the Department of Neurology (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Comprehensive Center for Clinical Neurosciences and Mental Health (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Department of Neurology (H.H., M.A., K.B., F.D.P., F.D.), Medical University of Innsbruck; and Department of Ophthalmology (B.P.), Medical University of Vienna, Austria
| | - Nik Krajnc
- From the Department of Neurology (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Comprehensive Center for Clinical Neurosciences and Mental Health (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Department of Neurology (H.H., M.A., K.B., F.D.P., F.D.), Medical University of Innsbruck; and Department of Ophthalmology (B.P.), Medical University of Vienna, Austria
| | - Fritz Leutmezer
- From the Department of Neurology (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Comprehensive Center for Clinical Neurosciences and Mental Health (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Department of Neurology (H.H., M.A., K.B., F.D.P., F.D.), Medical University of Innsbruck; and Department of Ophthalmology (B.P.), Medical University of Vienna, Austria
| | - Stefan Macher
- From the Department of Neurology (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Comprehensive Center for Clinical Neurosciences and Mental Health (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Department of Neurology (H.H., M.A., K.B., F.D.P., F.D.), Medical University of Innsbruck; and Department of Ophthalmology (B.P.), Medical University of Vienna, Austria
| | - Paulus Stefan Rommer
- From the Department of Neurology (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Comprehensive Center for Clinical Neurosciences and Mental Health (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Department of Neurology (H.H., M.A., K.B., F.D.P., F.D.), Medical University of Innsbruck; and Department of Ophthalmology (B.P.), Medical University of Vienna, Austria
| | - Karin Zebenholzer
- From the Department of Neurology (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Comprehensive Center for Clinical Neurosciences and Mental Health (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Department of Neurology (H.H., M.A., K.B., F.D.P., F.D.), Medical University of Innsbruck; and Department of Ophthalmology (B.P.), Medical University of Vienna, Austria
| | - Gudrun Zulehner
- From the Department of Neurology (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Comprehensive Center for Clinical Neurosciences and Mental Health (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Department of Neurology (H.H., M.A., K.B., F.D.P., F.D.), Medical University of Innsbruck; and Department of Ophthalmology (B.P.), Medical University of Vienna, Austria
| | - Tobias Zrzavy
- From the Department of Neurology (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Comprehensive Center for Clinical Neurosciences and Mental Health (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Department of Neurology (H.H., M.A., K.B., F.D.P., F.D.), Medical University of Innsbruck; and Department of Ophthalmology (B.P.), Medical University of Vienna, Austria
| | - Florian Deisenhammer
- From the Department of Neurology (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Comprehensive Center for Clinical Neurosciences and Mental Health (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Department of Neurology (H.H., M.A., K.B., F.D.P., F.D.), Medical University of Innsbruck; and Department of Ophthalmology (B.P.), Medical University of Vienna, Austria
| | - Berthold Pemp
- From the Department of Neurology (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Comprehensive Center for Clinical Neurosciences and Mental Health (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Department of Neurology (H.H., M.A., K.B., F.D.P., F.D.), Medical University of Innsbruck; and Department of Ophthalmology (B.P.), Medical University of Vienna, Austria
| | - Thomas Berger
- From the Department of Neurology (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Comprehensive Center for Clinical Neurosciences and Mental Health (G.B., P.A., B.K., N.K., F.L., S.M., P.S.R., K.Z., G.Z., T.Z., T.B.), Medical University of Vienna; Department of Neurology (H.H., M.A., K.B., F.D.P., F.D.), Medical University of Innsbruck; and Department of Ophthalmology (B.P.), Medical University of Vienna, Austria
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Chen JZ, Li CC, Li SH, Su YT, Zhang T, Wang YS, Dou GR, Chen T, Wang XC, Zhang ZM. A feasibility study for objective evaluation of visual acuity based on pattern-reversal visual evoked potentials and other related visual parameters with machine learning algorithm. BMC Ophthalmol 2023; 23:293. [PMID: 37369996 DOI: 10.1186/s12886-023-03044-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 06/14/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND To develop machine learning models for objectively evaluating visual acuity (VA) based on pattern-reversal visual evoked potentials (PRVEPs) and other related visual parameters. METHODS Twenty-four volunteers were recruited and forty-eight eyes were divided into four groups of 1.0, 0.8, 0.6, and 0.4 (decimal vision). The relationship between VA, peak time, or amplitude of P100 recorded at 5.7°, 2.6°, 1°, 34', 15', and 7' check sizes were analyzed using repeated-measures analysis of variance. Correlations between VA and P100, contrast sensitivity (CS), refractive error, wavefront aberrations, and visual field were analyzed by rank correlation. Based on meaningful P100 peak time, P100 amplitude, and other related visual parameters, four machine learning algorithms and an ensemble classification algorithm were used to construct objective assessment models for VA. Receiver operating characteristic (ROC) curves were used to compare the efficacy of different models by repeated sampling comparisons and ten-fold cross-validation. RESULTS The main effects of P100 peak time and amplitude between different VA and check sizes were statistically significant (all P < 0.05). Except amplitude at 2.6° and 5.7°, VA was negatively correlated with peak time and positively correlated with amplitude. The peak time initially shortened with increasing check size and gradually lengthened after the minimum value was reached at 1°. At the 1° check size, there were statistically significant differences when comparing the peak times between the vision groups with each other (all P < 0.05), and the amplitudes of the vision reduction groups were significantly lower than that of the 1.0 vision group (all P < 0.01). The correlations between peak time, amplitude, and visual acuity were all highest at 1° (rs = - 0.740, 0.438). VA positively correlated with CS and spherical equivalent (all P < 0.001). There was a negative correlation between VA and coma aberrations (P < 0.05). For different binarization classifications of VA, the classifier models with the best assessment efficacy all had the mean area under the ROC curves (AUC) above 0.95 for 500 replicate samples and above 0.84 for ten-fold cross-validation. CONCLUSIONS Machine learning models established by meaning visual parameters related to visual acuity can assist in the objective evaluation of VA.
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Affiliation(s)
- Jian Zheng Chen
- Ministry-of-Education Key Laboratory of Aerospace Medicine, School of Aerospace Medicine, Air Force Medical University, Xi'an, Shaanxi Province, China
- Qingdao Special Servicemen Recuperation Center of PLA Navy, Qingdao, Shandong Province, China
| | - Cong Cong Li
- Ministry-of-Education Key Laboratory of Aerospace Medicine, School of Aerospace Medicine, Air Force Medical University, Xi'an, Shaanxi Province, China
| | - Shao Heng Li
- Ministry-of-Education Key Laboratory of Aerospace Medicine, School of Aerospace Medicine, Air Force Medical University, Xi'an, Shaanxi Province, China
- Department of Ophthalmology, The First Affiliated Hospital, Air Force Medical University, Xi'an, Shaanxi Province, China
| | - Yu Ting Su
- Ministry-of-Education Key Laboratory of Aerospace Medicine, School of Aerospace Medicine, Air Force Medical University, Xi'an, Shaanxi Province, China
| | - Tao Zhang
- School of Biomedical Engineering, Air Force Medical University, Xi'an, Shaanxi Province, China
| | - Yu Sheng Wang
- Department of Ophthalmology, The First Affiliated Hospital, Air Force Medical University, Xi'an, Shaanxi Province, China
| | - Guo Rui Dou
- Department of Ophthalmology, The First Affiliated Hospital, Air Force Medical University, Xi'an, Shaanxi Province, China
| | - Tao Chen
- Ministry-of-Education Key Laboratory of Aerospace Medicine, School of Aerospace Medicine, Air Force Medical University, Xi'an, Shaanxi Province, China.
- Department of Aviation Medicine, The First Affiliated Hospital, Air Force Medical University, Xi'an, Shaanxi Province, China.
| | - Xiao Cheng Wang
- Ministry-of-Education Key Laboratory of Aerospace Medicine, School of Aerospace Medicine, Air Force Medical University, Xi'an, Shaanxi Province, China.
- Department of Aviation Medicine, The First Affiliated Hospital, Air Force Medical University, Xi'an, Shaanxi Province, China.
| | - Zuo Ming Zhang
- Ministry-of-Education Key Laboratory of Aerospace Medicine, School of Aerospace Medicine, Air Force Medical University, Xi'an, Shaanxi Province, China.
- Department of Aviation Medicine, The First Affiliated Hospital, Air Force Medical University, Xi'an, Shaanxi Province, China.
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Nguyen L, Wang CX, Conger DL, Sguigna PV, Singh S, Greenberg BM. Subclinical optic neuritis in pediatric myelin oligodendrocyte glycoprotein antibody-associated disease. Mult Scler Relat Disord 2023; 76:104802. [PMID: 37329787 DOI: 10.1016/j.msard.2023.104802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 05/22/2023] [Accepted: 06/05/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND AND OBJECTIVES The clinical spectrum of myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease (MOGAD) is heterogenous and has evolved over time since the commercial availability of the anti-MOG antibody assay. Subclinical disease activity has been previously reported in the visual pathway, but prevalence data remains limited. We investigated subclinical optic neuritis (ON) based on changes on retinal nerve fiber layer (RNFL) thickness on optic coherence tomography (OCT) in pediatric patients who tested positive for the anti-MOG antibody. METHODS In this retrospective, single-center cohort study, we examined children with MOGAD with at least one complete assessment of the anterior visual pathway. Subclinical ON was defined by structural visual system disease in the absence of a subjective complaint of vision loss, pain (particularly with eye movement), or color desaturation. RESULTS Records were reviewed from 85 children with MOGAD, 67 of whom (78.8%) had complete records for review. Eleven children (16.4%) had subclinical ON on OCT. Ten had significant reductions in RNFL, of which one had two distinct episodes of decreased RNFL, and one had significant elevations in RNFL. Of the eleven children with subclinical ON, six (54.5%) had a relapsing disease course. We also highlighted the clinical course of three children with subclinical ON detected on longitudinal OCT, including two who had subclinical ON outside of clinical relapses. CONCLUSION Children with MOGAD can have subclinical ON events that can manifest as significant reductions or elevations in RNFL on OCT. OCT should be used routinely in the management and monitoring of MOGAD patients.
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Affiliation(s)
- Linda Nguyen
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - Cynthia X Wang
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Darrel L Conger
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Peter V Sguigna
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Sumit Singh
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Benjamin M Greenberg
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX, USA
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Petzold A, Fraser CL, Abegg M, Alroughani R, Alshowaeir D, Alvarenga R, Andris C, Asgari N, Barnett Y, Battistella R, Behbehani R, Berger T, Bikbov MM, Biotti D, Biousse V, Boschi A, Brazdil M, Brezhnev A, Calabresi PA, Cordonnier M, Costello F, Cruz FM, Cunha LP, Daoudi S, Deschamps R, de Seze J, Diem R, Etemadifar M, Flores-Rivera J, Fonseca P, Frederiksen J, Frohman E, Frohman T, Tilikete CF, Fujihara K, Gálvez A, Gouider R, Gracia F, Grigoriadis N, Guajardo JM, Habek M, Hawlina M, Martínez-Lapiscina EH, Hooker J, Hor JY, Howlett W, Huang-Link Y, Idrissova Z, Illes Z, Jancic J, Jindahra P, Karussis D, Kerty E, Kim HJ, Lagrèze W, Leocani L, Levin N, Liskova P, Liu Y, Maiga Y, Marignier R, McGuigan C, Meira D, Merle H, Monteiro MLR, Moodley A, Moura F, Muñoz S, Mustafa S, Nakashima I, Noval S, Oehninger C, Ogun O, Omoti A, Pandit L, Paul F, Rebolleda G, Reddel S, Rejdak K, Rejdak R, Rodriguez-Morales AJ, Rougier MB, Sa MJ, Sanchez-Dalmau B, Saylor D, Shatriah I, Siva A, Stiebel-Kalish H, Szatmary G, Ta L, Tenembaum S, Tran H, Trufanov Y, van Pesch V, Wang AG, Wattjes MP, Willoughby E, Zakaria M, Zvornicanin J, Balcer L, Plant GT. Diagnosis and classification of optic neuritis. Lancet Neurol 2022; 21:1120-1134. [PMID: 36179757 DOI: 10.1016/s1474-4422(22)00200-9] [Citation(s) in RCA: 80] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 03/16/2022] [Accepted: 04/20/2022] [Indexed: 11/28/2022]
Abstract
There is no consensus regarding the classification of optic neuritis, and precise diagnostic criteria are not available. This reality means that the diagnosis of disorders that have optic neuritis as the first manifestation can be challenging. Accurate diagnosis of optic neuritis at presentation can facilitate the timely treatment of individuals with multiple sclerosis, neuromyelitis optica spectrum disorder, or myelin oligodendrocyte glycoprotein antibody-associated disease. Epidemiological data show that, cumulatively, optic neuritis is most frequently caused by many conditions other than multiple sclerosis. Worldwide, the cause and management of optic neuritis varies with geographical location, treatment availability, and ethnic background. We have developed diagnostic criteria for optic neuritis and a classification of optic neuritis subgroups. Our diagnostic criteria are based on clinical features that permit a diagnosis of possible optic neuritis; further paraclinical tests, utilising brain, orbital, and retinal imaging, together with antibody and other protein biomarker data, can lead to a diagnosis of definite optic neuritis. Paraclinical tests can also be applied retrospectively on stored samples and historical brain or retinal scans, which will be useful for future validation studies. Our criteria have the potential to reduce the risk of misdiagnosis, provide information on optic neuritis disease course that can guide future treatment trial design, and enable physicians to judge the likelihood of a need for long-term pharmacological management, which might differ according to optic neuritis subgroups.
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Klistorner A, Klistorner S, You Y, Graham SL, Yiannikas C, Parratt J, Barnett M. Long-term Effect of Permanent Demyelination on Axonal Survival in Multiple Sclerosis. NEUROLOGY(R) NEUROIMMUNOLOGY & NEUROINFLAMMATION 2022; 9:9/3/e1155. [PMID: 35241572 PMCID: PMC8893590 DOI: 10.1212/nxi.0000000000001155] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 01/24/2022] [Indexed: 12/12/2022]
Abstract
Background and Objectives To investigate the long-term effect of permanent demyelination on axonal attrition by examining an association between intereye asymmetry of the multifocal visual evoked potential (mfVEP) latency delay and subsequent thinning of retinal ganglion cell axons in patients with a long-standing history of unilateral optic neuritis (ON). Methods Only patients with a significant degree of chronic demyelination (intereye latency asymmetry >5 ms) were included in this study. The level of optic nerve demyelination was estimated at baseline by the latency delay of mfVEP, while the degree of axonal loss was assessed by thinning of the retinal nerve fiber layer (RNFL) thickness between baseline and follow-up visits. Low-contrast visual acuity (LCVA) was also evaluated at baseline and follow-up. Patients were examined twice with an average interval of 6.1 ± 1.4 years. Results From 85 examined patients with multiple sclerosis, 28 satisfied inclusion criteria. Latency of the mfVEP was delayed, and RNFL thickness was reduced in ON eyes compared with fellow eyes at both visits. There was significant correlation between latency asymmetry and baseline or follow-up intereye RNFL thickness asymmetry. Intereye asymmetry of LCVA at baseline correlated with baseline latency asymmetry of mfVEP and baseline asymmetry of RNFL thickness. Latency of the mfVEP in ON eyes improved slightly during the follow-up period, whereas latency of the fellow eye remained stable. By contrast, RNFL thickness significantly declined in both ON and fellow eyes during the follow-up period. The rate of RNFL thinning in ON eyes, however, was more than 2 times faster compared with the fellow eyes (p < 0.001). Furthermore, baseline latency asymmetry significantly correlated with the rate of RNFL thinning in ON eyes during the follow-up (p < 0.001), explaining almost half of the variability of temporal RNFL progression. For each millisecond of latency delay (i.e., ∼0.5 mm of demyelination along the optic nerve), temporal RNFL thickness was annually reduced by 0.05%. Discussion Our study provides clear in vivo evidence that chronic demyelination significantly accelerates axonal loss. However, because this process is slow and its effect is mild, long-term monitoring is required to establish and confidently measure the neurodegenerative consequences of demyelination.
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Affiliation(s)
- Alexandr Klistorner
- From the Save Sight Institute (A.K., S.K., and Y.Y.), Sydney Medical School, University of Sydney, New South Wales, Australia; Faculty of Medicine and Health Sciences (A.K., Y.Y., and S.L.G.), Macquarie University, Sydney, New South Wales, Australia; Royal North Shore Hospital (S.K., C.Y., and J.P.), Sydney, New South Wales, Australia; Brain and Mind Centre (M.B.), University of Sydney, New South Wales, Australia; and Sydney Neuroimaging Analysis Centre (M.B.), Camperdown, New South Wales, Australia.
| | - Samuel Klistorner
- From the Save Sight Institute (A.K., S.K., and Y.Y.), Sydney Medical School, University of Sydney, New South Wales, Australia; Faculty of Medicine and Health Sciences (A.K., Y.Y., and S.L.G.), Macquarie University, Sydney, New South Wales, Australia; Royal North Shore Hospital (S.K., C.Y., and J.P.), Sydney, New South Wales, Australia; Brain and Mind Centre (M.B.), University of Sydney, New South Wales, Australia; and Sydney Neuroimaging Analysis Centre (M.B.), Camperdown, New South Wales, Australia.
| | - Yuyi You
- From the Save Sight Institute (A.K., S.K., and Y.Y.), Sydney Medical School, University of Sydney, New South Wales, Australia; Faculty of Medicine and Health Sciences (A.K., Y.Y., and S.L.G.), Macquarie University, Sydney, New South Wales, Australia; Royal North Shore Hospital (S.K., C.Y., and J.P.), Sydney, New South Wales, Australia; Brain and Mind Centre (M.B.), University of Sydney, New South Wales, Australia; and Sydney Neuroimaging Analysis Centre (M.B.), Camperdown, New South Wales, Australia
| | - Stuart L Graham
- From the Save Sight Institute (A.K., S.K., and Y.Y.), Sydney Medical School, University of Sydney, New South Wales, Australia; Faculty of Medicine and Health Sciences (A.K., Y.Y., and S.L.G.), Macquarie University, Sydney, New South Wales, Australia; Royal North Shore Hospital (S.K., C.Y., and J.P.), Sydney, New South Wales, Australia; Brain and Mind Centre (M.B.), University of Sydney, New South Wales, Australia; and Sydney Neuroimaging Analysis Centre (M.B.), Camperdown, New South Wales, Australia
| | - Con Yiannikas
- From the Save Sight Institute (A.K., S.K., and Y.Y.), Sydney Medical School, University of Sydney, New South Wales, Australia; Faculty of Medicine and Health Sciences (A.K., Y.Y., and S.L.G.), Macquarie University, Sydney, New South Wales, Australia; Royal North Shore Hospital (S.K., C.Y., and J.P.), Sydney, New South Wales, Australia; Brain and Mind Centre (M.B.), University of Sydney, New South Wales, Australia; and Sydney Neuroimaging Analysis Centre (M.B.), Camperdown, New South Wales, Australia
| | - John Parratt
- From the Save Sight Institute (A.K., S.K., and Y.Y.), Sydney Medical School, University of Sydney, New South Wales, Australia; Faculty of Medicine and Health Sciences (A.K., Y.Y., and S.L.G.), Macquarie University, Sydney, New South Wales, Australia; Royal North Shore Hospital (S.K., C.Y., and J.P.), Sydney, New South Wales, Australia; Brain and Mind Centre (M.B.), University of Sydney, New South Wales, Australia; and Sydney Neuroimaging Analysis Centre (M.B.), Camperdown, New South Wales, Australia
| | - Michael Barnett
- From the Save Sight Institute (A.K., S.K., and Y.Y.), Sydney Medical School, University of Sydney, New South Wales, Australia; Faculty of Medicine and Health Sciences (A.K., Y.Y., and S.L.G.), Macquarie University, Sydney, New South Wales, Australia; Royal North Shore Hospital (S.K., C.Y., and J.P.), Sydney, New South Wales, Australia; Brain and Mind Centre (M.B.), University of Sydney, New South Wales, Australia; and Sydney Neuroimaging Analysis Centre (M.B.), Camperdown, New South Wales, Australia
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7
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Olbert E, Struhal W. Retinal imaging with optical coherence tomography in multiple sclerosis: novel aspects. Wien Med Wochenschr 2022; 172:329-336. [PMID: 35347500 PMCID: PMC9606096 DOI: 10.1007/s10354-022-00925-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 02/21/2022] [Indexed: 11/30/2022]
Abstract
Optical coherence tomography (OCT) is of increasing interest in the clinical assessment of multiple sclerosis (MS) patients beyond the scope of clinical studies. In this narrative review, we discuss novel changes of OCT parameters during acute optic neuritis and the disease course of MS patients. OCT images document the changes of retinal layers during an episode of acute optic neuritis and can therefore provide valuable insights into the pathophysiology. Moreover, MS patients show progredient thinning of retinal layers throughout the disease. The thinning is accelerated through relapses as well as disease progression without relapse. The OCT parameters are also associated with clinical outcome parameters, including disability, cognitive function, and brain atrophy. The impact of disease-modifying therapies on OCT parameters is the subject of ongoing research and depends on the agent used. Additional data are still necessary before OCT parameters can be implemented in the clinical standard of care of MS patients.
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Affiliation(s)
- Elisabeth Olbert
- Department of Neurology, University Hospital Tulln, Alter Ziegelweg 10, 3430, Tulln an der Donau, Austria. .,Karl Landsteiner University of Health Sciences, Tulln, Austria.
| | - Walter Struhal
- Department of Neurology, University Hospital Tulln, Alter Ziegelweg 10, 3430, Tulln an der Donau, Austria.,Karl Landsteiner University of Health Sciences, Tulln, Austria
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8
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Nij Bijvank J, Uitdehaag BMJ, Petzold A. Retinal inter-eye difference and atrophy progression in multiple sclerosis diagnostics. J Neurol Neurosurg Psychiatry 2022; 93:216-219. [PMID: 34764152 PMCID: PMC8785044 DOI: 10.1136/jnnp-2021-327468] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 10/28/2021] [Indexed: 11/16/2022]
Abstract
BACKGROUND The visual system could be included in the diagnostic criteria for multiple sclerosis (MS) to demonstrate dissemination in space (DIS) and dissemination in time (DIT). OBJECTIVE To investigate the diagnostic value of retinal asymmetry in MS. METHODS A prospective, longitudinal study in individuals with MS (n=151) and healthy controls (n=27). Optical coherence tomography (OCT) was performed at 0, 2 and 4 years. Macular ganglion cell and inner plexiform layer (mGCIPL) thickness was determined as well as measures for retinal asymmetry: the inter-eye percentage difference (IEPD) and inter-eye absolute difference (IEAD). Receiver operator characteristics curves were plotted and the area under the curve (AUC) was calculated for group comparisons of the mGCIPL, IEPD, IEAD and atrophy rates. RESULTS The diagnostic accuracy of both the IEPD and IEAD for differentiating bilateral and unilateral MS optic neuritis was high and stable over time (AUCs 0.88-0.93). The IEPD slightly outperformed the IEAD. Atrophy rates showed low discriminatory abilities for differentiating MS from controls (AUC 0.49-0.58). CONCLUSION The inter-eye differences of the mGCIPL have value for demonstration of DIS but in individuals with longstanding MS not for DIT. This may be considered as a test to detect DIS in future diagnostic criteria. Validation in a large prospective study in people presenting with symptoms suggestive of MS is required.
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Affiliation(s)
- Jenny Nij Bijvank
- Neurology, MS Center and Neuro-ophthalmology Expertise Center, Amsterdam Neuroscience, Amsterdam UMC Locatie VUmc, Amsterdam, Noord-Holland, Netherlands .,Ophthalmology, Neuro-ophthalmology Expertise Center, Amsterdam Neuroscience, Amsterdam UMC Locatie VUmc, Amsterdam, Noord-Holland, Netherlands
| | - B M J Uitdehaag
- Neurology, MS Center and Neuro-ophthalmology Expertise Center, Amsterdam Neuroscience, Amsterdam UMC Locatie VUmc, Amsterdam, Noord-Holland, Netherlands
| | - Axel Petzold
- Neurology, MS Center and Neuro-ophthalmology Expertise Center, Amsterdam Neuroscience, Amsterdam UMC Locatie VUmc, Amsterdam, Noord-Holland, Netherlands.,Ophthalmology, Neuro-ophthalmology Expertise Center, Amsterdam Neuroscience, Amsterdam UMC Locatie VUmc, Amsterdam, Noord-Holland, Netherlands.,Neuro-ophthalmology, Moorfields Eye Hospital, The National Hospital for Neurology and Neurosurgery and the UCL Queen Square Institute of Neurology, London, UK
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9
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Petzold A, Albrecht P, Balcer L, Bekkers E, Brandt AU, Calabresi PA, Deborah OG, Graves JS, Green A, Keane PA, Nij Bijvank JA, Sander JW, Paul F, Saidha S, Villoslada P, Wagner SK, Yeh EA. Artificial intelligence extension of the OSCAR-IB criteria. Ann Clin Transl Neurol 2021; 8:1528-1542. [PMID: 34008926 PMCID: PMC8283174 DOI: 10.1002/acn3.51320] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 12/31/2020] [Accepted: 01/03/2021] [Indexed: 12/12/2022] Open
Abstract
Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines.
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Affiliation(s)
- Axel Petzold
- Moorfields Eye HospitalCity Road, The National Hospital for Neurology and NeurosurgeryQueen SquareUCL Queen Square Institute of NeurologyLondonUK
- Neuro‐ophthalmology Expert CenterAmsterdam UMCThe Netherlands
| | - Philipp Albrecht
- Department of NeurologyMedical FacultyHeinrich‐Heine UniversityDüsseldorfGermany
| | - Laura Balcer
- Departments of NeurologyPopulation Health and OphthalmologyNYU Grossman School of MedicineNew YorkUSA
| | | | | | - Peter A. Calabresi
- Department of NeurologyJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | | | | | - Ari Green
- Department of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Pearse A Keane
- Moorfields Eye HospitalCity Road, The National Hospital for Neurology and NeurosurgeryQueen SquareUCL Queen Square Institute of NeurologyLondonUK
| | | | - Josemir W. Sander
- NIHR UCL Hospitals Biomedical Research CentreUCL Queen Square Institute of NeurologyLondonUK
- Chalfont Centre for EpilepsyChalfont St PeterUK
- Stichting Epilepsie Instellingen Nederland (SEIN)HeemstedeThe Netherlands
| | - Friedemann Paul
- Experimental and Clinical Research CenterMax Delbrück Center for Molecular Medicine and Charité – Universitätsmedizin Berlincorporate member of Freie Universität BerlinHumboldt‐Universität zu Berlin, and Berlin Institute of HealthGermany
| | - Shiv Saidha
- Department of NeurologyJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Pablo Villoslada
- Institut d’Investigacion Biomediques August Pi Sunyer (DIBAPS) and Hospital ClinicUniversity of BarcelonaBarcelonaSpain
| | - Siegfried K Wagner
- Moorfields Eye HospitalCity Road, The National Hospital for Neurology and NeurosurgeryQueen SquareUCL Queen Square Institute of NeurologyLondonUK
| | - E. Ann Yeh
- Division of NeurologyDepartment of PediatricsHospital for Sick ChildrenDivision of Neurosciences and Mental Health SickKids Research InstituteUniversity of TorontoCanada
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10
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Petzold A, Chua SYL, Khawaja AP, Keane PA, Khaw PT, Reisman C, Dhillon B, Strouthidis NG, Foster PJ, Patel PJ. Retinal asymmetry in multiple sclerosis. Brain 2021; 144:224-235. [PMID: 33253371 PMCID: PMC7880665 DOI: 10.1093/brain/awaa361] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 07/15/2020] [Accepted: 08/11/2020] [Indexed: 12/21/2022] Open
Abstract
The diagnosis of multiple sclerosis is based on a combination of clinical and paraclinical tests. The potential contribution of retinal optical coherence tomography (OCT) has been recognized. We tested the feasibility of OCT measures of retinal asymmetry as a diagnostic test for multiple sclerosis at the community level. In this community-based study of 72 120 subjects, we examined the diagnostic potential of the inter-eye difference of inner retinal OCT data for multiple sclerosis using the UK Biobank data collected at 22 sites between 2007 and 2010. OCT reporting and quality control guidelines were followed. The inter-eye percentage difference (IEPD) and inter-eye absolute difference (IEAD) were calculated for the macular retinal nerve fibre layer (RNFL), ganglion cell inner plexiform layer (GCIPL) complex and ganglion cell complex. Area under the receiver operating characteristic curve (AUROC) comparisons were followed by univariate and multivariable comparisons accounting for a large range of diseases and co-morbidities. Cut-off levels were optimized by ROC and the Youden index. The prevalence of multiple sclerosis was 0.0023 [95% confidence interval (CI) 0.00229–0.00231]. Overall the discriminatory power of diagnosing multiple sclerosis with the IEPD AUROC curve (0.71, 95% CI 0.67–0.76) and IEAD (0.71, 95% CI 0.67–0.75) for the macular GCIPL complex were significantly higher if compared to the macular ganglion cell complex IEPD AUROC curve (0.64, 95% CI 0.59–0.69, P = 0.0017); IEAD AUROC curve (0.63, 95% CI 0.58–0.68, P < 0.0001) and macular RNFL IEPD AUROC curve (0.59, 95% CI 0.54–0.63, P < 0.0001); IEAD AUROC curve (0.55, 95% CI 0.50–0.59, P < 0.0001). Screening sensitivity levels for the macular GCIPL complex IEPD (4% cut-off) were 51.7% and for the IEAD (4 μm cut-off) 43.5%. Specificity levels were 82.8% and 86.8%, respectively. The number of co-morbidities was important. There was a stepwise decrease of the AUROC curve from 0.72 in control subjects to 0.66 in more than nine co-morbidities or presence of neuromyelitis optica spectrum disease. In the multivariable analyses greater age, diabetes mellitus, other eye disease and a non-white ethnic background were relevant confounders. For most interactions, the effect sizes were large (partial ω2 > 0.14) with narrow confidence intervals. In conclusion, the OCT macular GCIPL complex IEPD and IEAD may be considered as supportive measurements for multiple sclerosis diagnostic criteria in a young patient without relevant co-morbidity. The metric does not allow separation of multiple sclerosis from neuromyelitis optica. Retinal OCT imaging is accurate, rapid, non-invasive, widely available and may therefore help to reduce need for invasive and more costly procedures. To be viable, higher sensitivity and specificity levels are needed.
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Affiliation(s)
- Axel Petzold
- Moorfields Eye Hospital and The National Hospital for Neurology and Neurosurgery, London, UK.,UCL Queen Square Institute of Neurology, London, UK.,Dutch Expertise Centre for Neuro-ophthalmology and MS Centre, Departments of Neurology and Ophthalmology, Amsterdam UMC, Amsterdam, The Netherlands
| | - Sharon Y L Chua
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Anthony P Khawaja
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Pearse A Keane
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Peng T Khaw
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Charles Reisman
- Topcon Healthcare Solutions Research and Development, Oakland, New Jersey, USA
| | - Baljean Dhillon
- Centre for Clinical Brain Sciences, School of Clinical Sciences, NHS Lothian, Edinburgh, UK
| | - Nicholas G Strouthidis
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Paul J Foster
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Praveen J Patel
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
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11
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Jiang H, Delgado S, Wang J. Advances in ophthalmic structural and functional measures in multiple sclerosis: do the potential ocular biomarkers meet the unmet needs? Curr Opin Neurol 2021; 34:97-107. [PMID: 33278142 PMCID: PMC7856092 DOI: 10.1097/wco.0000000000000897] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
PURPOSE OF REVIEW Multiple sclerosis is a heterogeneous disorder. Biomarkers to monitor disease activities are highly desirable especially because of the recent shift toward personalized medicine that coincides with the expansion of disease-modifying therapy. The visual system is highly involved in multiple sclerosis, and the rapid advancement of ophthalmic techniques has boosted the development of potential ocular biomarkers for multiple sclerosis management. RECENT FINDINGS Recent studies have found that the rapid thinning of the peripapillary retinal nerve fiber layer and ganglion cell-inner plexiform layer (GCIPL) occurs in the progressive stage. Furthermore, the inter-eye thickness difference of the GCIPL could be used in identifying unilateral optic neuritis to facilitate the early diagnosis of multiple sclerosis. Moreover, the retinal microvascular alterations measured as vessel density were found to be related to the disability and visual function, although a standardized protocol to measure retinal microvascular alterations has not been well established. Additionally, aberrant ocular motility, such as fixation microsaccades, can be used to measure disability objectively. SUMMARY The fast expansion of potential ocular biomarkers measured as retinal microstructural, microvascular, and ocular motility changes may facilitate the diagnosis and management of multiple sclerosis.
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Affiliation(s)
- Hong Jiang
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA
- Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Silvia Delgado
- Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Jianhua Wang
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA
- Department of Electrical and Computer Engineering, University of Miami, Miami, FL, USA
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12
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Murphy OC, Kalaitzidis G, Vasileiou E, Filippatou AG, Lambe J, Ehrhardt H, Pellegrini N, Sotirchos ES, Luciano NJ, Liu Y, Fitzgerald KC, Prince JL, Calabresi PA, Saidha S. Optical Coherence Tomography and Optical Coherence Tomography Angiography Findings After Optic Neuritis in Multiple Sclerosis. Front Neurol 2020; 11:618879. [PMID: 33384660 PMCID: PMC7769949 DOI: 10.3389/fneur.2020.618879] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 11/24/2020] [Indexed: 12/15/2022] Open
Abstract
Background: In people with multiple sclerosis (MS), optic neuritis (ON) results in inner retinal layer thinning, and reduced density of the retinal microvasculature. Objective: To compare inter-eye differences (IEDs) in macular optical coherence tomography (OCT) and OCT angiography (OCTA) measures in MS patients with a history of unilateral ON (MS ON) vs. MS patients with no history of ON (MS non-ON), and to assess how these measures correlate with visual function outcomes after ON. Methods: In this cross-sectional study, people with MS underwent OCT and OCTA. Superficial vascular plexus (SVP) density of each eye was quantified using a deep neural network. IEDs were calculated with respect to the ON eye in MS ON patients, and with respect to the right eye in MS non-ON patients. Statistical analyses used mixed-effect regression models accounting for intra-subject correlations. Results: We included 43 MS ON patients (with 92 discrete OCT/OCTA visits) and 14 MS non-ON patients (with 24 OCT/OCTA visits). Across the cohorts, mean IED in SVP density was −2.69% (SD 3.23) in MS ON patients, as compared to 0.17% (SD 2.39) in MS non-ON patients (p = 0.002). When the MS ON patients were further stratified according to time from ON and compared to MS non-ON patients with multiple cross-sectional analyses, we identified that IED in SVP density was significantly increased in MS ON patients at 1–3 years (p = < 0.001) and >3 years post-ON (p < 0.001), but not at <3 months (p = 0.21) or 3–12 months post-ON (p = 0.07), while IED in ganglion cell + inner plexiform layer (GCIPL) thickness was significantly increased in MS ON patients at all time points post-ON (p ≦ 0.01 for all). IED in SVP density and IED in GCIPL thickness demonstrated significant relationships with IEDs in 100% contrast, 2.5% contrast, and 1.25% contrast letter acuity in MS ON patients (p < 0.001 for all). Conclusions: Our findings suggest that increased IED in SVP density can be detected after ON in MS using OCTA, and detectable changes in SVP density after ON may occur after changes in GCIPL thickness. IED in SVP density and IED in GCIPL thickness correlate well with visual function outcomes in MS ON patients.
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Affiliation(s)
- Olwen C Murphy
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Grigorios Kalaitzidis
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Eleni Vasileiou
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Angeliki G Filippatou
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Jeffrey Lambe
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Henrik Ehrhardt
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Nicole Pellegrini
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Elias S Sotirchos
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Nicholas J Luciano
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Yihao Liu
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Kathryn C Fitzgerald
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Peter A Calabresi
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Shiv Saidha
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
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