OBJECTIVE: Clinical and neuroimaging parameters predictive of the changing clinical course of multiple sclerosis (MS) from relapsing-remitting to secondary progressive have not been clarified yet. We specifically designed a prospective 5-year longitudinal study aimed at assessing demographic, clinical, and magnetic resonance imaging (MRI) parameters that could predict the changing clinical course of MS. METHODS: At study entry and after 5 years, clinical and MRI (ie, gray matter and white matter lesions, including spinal cord lesions, and global and regional cortical thinning) parameters were assessed in a training set of 334 consecutive relapsing-remitting MS patients and in an independent validation set of 84 relapsing-remitting MS patients. RESULTS: Sixty-six (19.7%) relapsing-remitting MS patients changed their clinical course during the study and entered into the secondary progressive phase. Age (p = 0.001, odds ratio [OR] = 1.2), cortical lesion volume (p < 0.001, OR = 1.7), and cerebellar cortical volume (p < 0.001, OR = 0.2) at study entry were found to predict the changing clinical course. The model including only these 3 variables correctly identified 252 of 268 (94.0%) patients who maintained the relapsing-remitting course and 58 of 66 (87.8%) patients who became secondary progressive (cross-validated error rate = 7.2%). When applied on the validation set, the model obtained a similar error rate (8.4%). INTERPRETATION: A prediction model based on age, cortical lesion load, and cerebellar cortical volume suitably explains the probability of relapsing-remitting MS patients evolving into the progressive phase. Gray matter damage appears to play a pivotal role in determining the changing clinical course of MS.
OBJECTIVE: Clinical and neuroimaging parameters predictive of the changing clinical course of multiple sclerosis (MS) from relapsing-remitting to secondary progressive have not been clarified yet. We specifically designed a prospective 5-year longitudinal study aimed at assessing demographic, clinical, and magnetic resonance imaging (MRI) parameters that could predict the changing clinical course of MS. METHODS: At study entry and after 5 years, clinical and MRI (ie, gray matter and white matter lesions, including spinal cord lesions, and global and regional cortical thinning) parameters were assessed in a training set of 334 consecutive relapsing-remitting MSpatients and in an independent validation set of 84 relapsing-remitting MSpatients. RESULTS: Sixty-six (19.7%) relapsing-remitting MSpatients changed their clinical course during the study and entered into the secondary progressive phase. Age (p = 0.001, odds ratio [OR] = 1.2), cortical lesion volume (p < 0.001, OR = 1.7), and cerebellar cortical volume (p < 0.001, OR = 0.2) at study entry were found to predict the changing clinical course. The model including only these 3 variables correctly identified 252 of 268 (94.0%) patients who maintained the relapsing-remitting course and 58 of 66 (87.8%) patients who became secondary progressive (cross-validated error rate = 7.2%). When applied on the validation set, the model obtained a similar error rate (8.4%). INTERPRETATION: A prediction model based on age, cortical lesion load, and cerebellar cortical volume suitably explains the probability of relapsing-remitting MSpatients evolving into the progressive phase. Gray matter damage appears to play a pivotal role in determining the changing clinical course of MS.
Authors: E S Beck; P Sati; V Sethi; T Kober; B Dewey; P Bhargava; G Nair; I C Cortese; D S Reich Journal: AJNR Am J Neuroradiol Date: 2018-02-08 Impact factor: 3.825
Authors: A F Kuceyeski; W Vargas; M Dayan; E Monohan; C Blackwell; A Raj; K Fujimoto; S A Gauthier Journal: AJNR Am J Neuroradiol Date: 2014-11-20 Impact factor: 3.825
Authors: C Guglielmetti; J Veraart; E Roelant; Z Mai; J Daans; J Van Audekerke; M Naeyaert; G Vanhoutte; R Delgado Y Palacios; J Praet; E Fieremans; P Ponsaerts; J Sijbers; A Van der Linden; M Verhoye Journal: Neuroimage Date: 2015-10-23 Impact factor: 6.556
Authors: Ruthger Righart; Viola Biberacher; Laura E Jonkman; Roel Klaver; Paul Schmidt; Dorothea Buck; Achim Berthele; Jan S Kirschke; Claus Zimmer; Bernhard Hemmer; Jeroen J G Geurts; Mark Mühlau Journal: Ann Neurol Date: 2017-09-16 Impact factor: 10.422
Authors: Arman Eshaghi; Benedetta Bodini; Gerard R Ridgway; Daniel García-Lorenzo; Daniel J Tozer; Mohammad Ali Sahraian; Alan J Thompson; Olga Ciccarelli Journal: Neuroimage Date: 2013-10-04 Impact factor: 6.556
Authors: Lewis M Watkins; James W Neal; Sam Loveless; Iliana Michailidou; Valeria Ramaglia; Mark I Rees; Richard Reynolds; Neil P Robertson; B Paul Morgan; Owain W Howell Journal: J Neuroinflammation Date: 2016-06-22 Impact factor: 8.322