Tomas Uher1, Manuela Vaneckova2, Lukas Sobisek3, Michaela Tyblova1, Zdenek Seidl2, Jan Krasensky2, Deepa Ramasamy4, Robert Zivadinov5, Eva Havrdova1, Tomas Kalincik6, Dana Horakova1. 1. Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine and General University Hospital, Charles University, Prague, Czech Republic. 2. Department of Radiology, First Faculty of Medicine and General University Hospital, Charles University, Prague, Czech Republic. 3. Department of Statistics and Probability, University of Economics, Prague, Czech Republic. 4. Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA. 5. Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA/MR Imaging Clinical Translational Research Center, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA. 6. Department of Medicine, University of Melbourne, Melbourne, VIC, Australia/Department of Neurology, Royal Melbourne Hospital, Melbourne, VIC, Australia.
Abstract
BACKGROUND: Disease progression and treatment efficacy vary among individuals with multiple sclerosis. Reliable predictors of individual disease outcomes are lacking. OBJECTIVE: To examine the accuracy of the early prediction of 12-year disability outcomes using clinical and magnetic resonance imaging (MRI) parameters. METHODS: A total of 177 patients from the original Avonex-Steroids-Azathioprine study were included. Participants underwent 3-month clinical follow-ups. Cox models were used to model the associations between clinical and MRI markers at baseline or after 12 months with sustained disability progression (SDP) over the 12-year observation period. RESULTS: At baseline, T2 lesion number, T1 and T2 lesion volumes, corpus callosum (CC), and thalamic fraction were the best predictors of SDP (hazard ratio (HR) = 1.7-4.6; p ⩽ 0.001-0.012). At 12 months, Expanded Disability Status Scale (EDSS) and its change, number of new or enlarging T2 lesions, and CC volume % change were the best predictors of SDP over the follow-up (HR = 1.7-3.5; p ⩽ 0.001-0.017). A composite score was generated from a subset of the best predictors of SDP. Scores of ⩾4 had greater specificity (90%-100%) and were associated with greater cumulative risk of SDP (HR = 3.2-21.6; p < 0.001) compared to the individual predictors. CONCLUSION: The combination of established MRI and clinical indices with MRI volumetric predictors improves the prediction of SDP over long-term follow-up and may provide valuable information for therapeutic decisions.
BACKGROUND: Disease progression and treatment efficacy vary among individuals with multiple sclerosis. Reliable predictors of individual disease outcomes are lacking. OBJECTIVE: To examine the accuracy of the early prediction of 12-year disability outcomes using clinical and magnetic resonance imaging (MRI) parameters. METHODS: A total of 177 patients from the original Avonex-Steroids-Azathioprine study were included. Participants underwent 3-month clinical follow-ups. Cox models were used to model the associations between clinical and MRI markers at baseline or after 12 months with sustained disability progression (SDP) over the 12-year observation period. RESULTS: At baseline, T2 lesion number, T1 and T2 lesion volumes, corpus callosum (CC), and thalamic fraction were the best predictors of SDP (hazard ratio (HR) = 1.7-4.6; p ⩽ 0.001-0.012). At 12 months, Expanded Disability Status Scale (EDSS) and its change, number of new or enlarging T2 lesions, and CC volume % change were the best predictors of SDP over the follow-up (HR = 1.7-3.5; p ⩽ 0.001-0.017). A composite score was generated from a subset of the best predictors of SDP. Scores of ⩾4 had greater specificity (90%-100%) and were associated with greater cumulative risk of SDP (HR = 3.2-21.6; p < 0.001) compared to the individual predictors. CONCLUSION: The combination of established MRI and clinical indices with MRI volumetric predictors improves the prediction of SDP over long-term follow-up and may provide valuable information for therapeutic decisions.
Authors: Maria Trojano; Mar Tintore; Xavier Montalban; Jan Hillert; Tomas Kalincik; Pietro Iaffaldano; Tim Spelman; Maria Pia Sormani; Helmut Butzkueven Journal: Nat Rev Neurol Date: 2017-01-13 Impact factor: 42.937
Authors: Tobias Bobinger; Lisa May; Hannes Lücking; Stephan P Kloska; Petra Burkardt; Philipp Spitzer; Juan M Maler; Denis Corbeil; Hagen B Huttner Journal: Front Cell Neurosci Date: 2017-03-27 Impact factor: 5.505
Authors: Fraser S Brown; Stella A Glasmacher; Patrick K A Kearns; Niall MacDougall; David Hunt; Peter Connick; Siddharthan Chandran Journal: PLoS One Date: 2020-05-26 Impact factor: 3.240
Authors: Tomas Uher; Jan Krasensky; Lukas Sobisek; Jana Blahova Dusankova; Zdenek Seidl; Eva Kubala Havrdova; Maria Pia Sormani; Dana Horakova; Tomas Kalincik; Manuela Vaneckova Journal: Ann Clin Transl Neurol Date: 2017-12-15 Impact factor: 4.511