Retinal layer thinning predicts treatment failure in relapsing multiple sclerosis.
GCIPL
OCT
disease-modifying treatment
multiple sclerosis
retinal thinning
Journal
European journal of neurology
ISSN: 1468-1331
Titre abrégé: Eur J Neurol
Pays: England
ID NLM: 9506311
Informations de publication
Date de publication:
06 2021
06 2021
Historique:
received:
13
03
2021
accepted:
16
03
2021
pubmed:
19
3
2021
medline:
13
8
2021
entrez:
18
3
2021
Statut:
ppublish
Résumé
Peripapillary retinal nerve fiber layer (pRNFL) and macular ganglion cell plus inner plexiform layer (GCIPL) thinning are markers of neuroaxonal degeneration in multiple sclerosis (MS), which is reduced by disease-modifying treatment (DMT). We aimed to investigate the potential of pRNFL and GCIPL thinning for prediction of DMT failure in relapsing MS (RMS). In this 4-year prospective observational study on 113 RMS patients, pRNFL and GCIPL were measured at DMT initiation and after 12 months (M12) and 24 months (M24). Treatment failure was defined as 6-month confirmed Expanded Disability Status Scale (EDSS) progression and/or Symbol Digit Modalities Test (SDMT) worsening. Optimal cutoff values for predicting treatment failure were determined by receiver operating characteristic analyses and hazard ratios (HRs) by multivariable Cox regression adjusting for age, sex, disease duration, EDSS/SDMT, and DMT class. Thinning of GCIPL >0.5 μm/year at M24 showed superior value for treatment failure prediction (HR: 4.5, 95% confidence interval [CI]: 1.8-7.6, p < 0.001; specificity 91%, sensitivity 81%), followed by GCIPL >0.5 μm at M12 (odds ratio [OR]: 3.9, 95% CI: 1.4-6.9, p < 0.001; specificity 85%, sensitivity 78%), and pRNFL ≥2 μm/year at M24 (OR: 3.7, 95% CI: 1.1-6.5, p = 0.023; specificity 84%, sensitivity 69%), whereas pRNFL at M12 was not predictive. GCIPL, and to a lesser degree pRNFL, thinning predicts disability progression after DMT initiation and may be a useful and accessible biomarker of treatment failure in RMS.
Sections du résumé
BACKGROUND AND PURPOSE
Peripapillary retinal nerve fiber layer (pRNFL) and macular ganglion cell plus inner plexiform layer (GCIPL) thinning are markers of neuroaxonal degeneration in multiple sclerosis (MS), which is reduced by disease-modifying treatment (DMT). We aimed to investigate the potential of pRNFL and GCIPL thinning for prediction of DMT failure in relapsing MS (RMS).
METHODS
In this 4-year prospective observational study on 113 RMS patients, pRNFL and GCIPL were measured at DMT initiation and after 12 months (M12) and 24 months (M24). Treatment failure was defined as 6-month confirmed Expanded Disability Status Scale (EDSS) progression and/or Symbol Digit Modalities Test (SDMT) worsening. Optimal cutoff values for predicting treatment failure were determined by receiver operating characteristic analyses and hazard ratios (HRs) by multivariable Cox regression adjusting for age, sex, disease duration, EDSS/SDMT, and DMT class.
RESULTS
Thinning of GCIPL >0.5 μm/year at M24 showed superior value for treatment failure prediction (HR: 4.5, 95% confidence interval [CI]: 1.8-7.6, p < 0.001; specificity 91%, sensitivity 81%), followed by GCIPL >0.5 μm at M12 (odds ratio [OR]: 3.9, 95% CI: 1.4-6.9, p < 0.001; specificity 85%, sensitivity 78%), and pRNFL ≥2 μm/year at M24 (OR: 3.7, 95% CI: 1.1-6.5, p = 0.023; specificity 84%, sensitivity 69%), whereas pRNFL at M12 was not predictive.
CONCLUSIONS
GCIPL, and to a lesser degree pRNFL, thinning predicts disability progression after DMT initiation and may be a useful and accessible biomarker of treatment failure in RMS.
Identifiants
pubmed: 33735479
doi: 10.1111/ene.14829
pmc: PMC8251588
doi:
Types de publication
Journal Article
Observational Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
2037-2045Informations de copyright
© 2021 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.
Références
Curr Opin Neurol. 2018 Jun;31(3):233-243
pubmed: 29634596
Eur J Neurol. 2019 Jun;26(6):865-871
pubmed: 30614590
Ann Neurol. 2019 May;85(5):618-629
pubmed: 30851125
Mult Scler. 2019 Apr;25(4):541-553
pubmed: 29143562
Mult Scler. 2015 Feb;21(2):163-70
pubmed: 24948688
J Neurol Neurosurg Psychiatry. 2016 Jan;87(1):93-9
pubmed: 25904813
Mult Scler. 2019 Feb;25(2):196-203
pubmed: 29095097
CNS Drugs. 2017 Mar;31(3):217-236
pubmed: 28185158
Mult Scler. 2020 Sep;26(10):1207-1216
pubmed: 31198103
Neurology. 2016 Jul 12;87(2):134-40
pubmed: 27306626
Eur J Neurol. 2018 Sep;25(9):1107-e101
pubmed: 29687559
Neuroimage. 2016 Nov 15;142:188-197
pubmed: 27431758
Can J Neurol Sci. 2013 May;40(3):307-23
pubmed: 23603165
Mult Scler Relat Disord. 2020 Oct;45:102403
pubmed: 32738702
PLoS One. 2012;7(4):e34823
pubmed: 22536333
Arch Ophthalmol. 1985 Dec;103(12):1796-806
pubmed: 2866759
J Neurol. 2017 Sep;264(9):1837-1853
pubmed: 28567539
Mult Scler. 2017 Apr;23(5):721-733
pubmed: 28206827
Ann Neurol. 2011 Feb;69(2):292-302
pubmed: 21387374
Mult Scler. 2008 Aug;14(7):940-6
pubmed: 18573822
Mult Scler Relat Disord. 2019 Dec 23;39:101908
pubmed: 31896060
Neurology. 2017 Feb 7;88(6):525-532
pubmed: 28077493
Curr Opin Neurol. 2019 Jun;32(3):365-377
pubmed: 30985372
Stat Med. 2013 Dec 20;32(29):5077-90
pubmed: 23824874
Mult Scler. 2017 Feb;23(2):242-252
pubmed: 27230790
Neurol Neuroimmunol Neuroinflamm. 2021 Feb 17;8(3):
pubmed: 33597189
Mult Scler. 2009 Jul;15(7):848-53
pubmed: 19542263
Lancet. 2002 Apr 6;359(9313):1221-31
pubmed: 11955556
Lancet Neurol. 2006 Feb;5(2):158-70
pubmed: 16426992
Lancet Neurol. 2017 Oct;16(10):797-812
pubmed: 28920886
Mult Scler. 2021 Apr;27(5):684-694
pubmed: 32613912
Mult Scler. 2014 Apr;20(5):566-76
pubmed: 23999607
Mult Scler. 2013 Apr;19(5):605-12
pubmed: 23012253
Neurotherapeutics. 2017 Jan;14(1):24-34
pubmed: 27699722