Maria Pia Sormani1, Ludwig Kappos2, Ernst-Wilhelm Radue3, Jeffrey Cohen4, Frederik Barkhof5, Till Sprenger6, Daniela Piani Meier7, Dieter Häring7, Davorka Tomic7, Nicola De Stefano8. 1. Biostatistics Unit, Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy. 2. Neurological Clinic and Polyclinic, Departments of Medicine, Clinical Research, Biomedicine and Biomedical Engineering, University Hospital Basel, Basel, Switzerland. 3. Medical Image Analysis Center (MIAC), University Hospital Basel, Basel, Switzerland. 4. Neurological Institute, The Cleveland Clinic Foundation, Cleveland, OH, USA. 5. Department of Radiology, VU University Medical Center, Amsterdam, Netherlands. 6. Department of Neurology, DKD Helios Klinik Wiesbaden, Wiesbaden, Germany. 7. Novartis Pharma AG, Basel, Switzerland. 8. University of Siena, Siena, Italy.
Abstract
OBJECTIVE: To define values of normalized brain volume (NBV) that can be categorized as low, medium, or high, according to baseline characteristics of relapsing-remitting multiple sclerosis (RRMS) patients. METHODS: Expected NBV (eNBV) was calculated for each patient based on age, disease duration, sex, baseline Expanded Disability Status Scale (EDSS), and T2-lesion volume, entering these variables into a multiple regression model run on 2342 RRMS patients (pooled FREEDOMS/FREEDOMS-II population). According to the difference between their observed NBV and their eNBV, patients were classified as having low NBV, medium NBV, or high NBV. We evaluated whether these NBV categories were clinically meaningful by assessing correlation with disability worsening. RESULTS: The distribution of differences between observed NBV and eNBV was used to categorize patients as having low NBV, medium NBV or high NBV. Taking the high-NBV group as reference, the hazard ratios (HRs) for 2-year disability worsening, adjusted for treatment effect, were 1.23 (95% confidence interval (CI): 0.92-1.63, p = 0.16) for the medium NBV and 1.75 (95% CI: 1.26-2.44, p = 0.001) for the low NBV. The predictive value of NBV groups was preserved over 4 years. Treatment effect appeared more evident in low-NBV patients (HR = 0.58) than in medium-NBV (HR = 0.72) and in high-NBV (HR = 0.80) patients; however, the difference was not significant ( p = 0.57). CONCLUSION: RRMS patients can be categorized into disability risk groups based on individual eNBV values according to baseline demographics and clinical characteristics.
OBJECTIVE: To define values of normalized brain volume (NBV) that can be categorized as low, medium, or high, according to baseline characteristics of relapsing-remitting multiple sclerosis (RRMS) patients. METHODS: Expected NBV (eNBV) was calculated for each patient based on age, disease duration, sex, baseline Expanded Disability Status Scale (EDSS), and T2-lesion volume, entering these variables into a multiple regression model run on 2342 RRMS patients (pooled FREEDOMS/FREEDOMS-II population). According to the difference between their observed NBV and their eNBV, patients were classified as having low NBV, medium NBV, or high NBV. We evaluated whether these NBV categories were clinically meaningful by assessing correlation with disability worsening. RESULTS: The distribution of differences between observed NBV and eNBV was used to categorize patients as having low NBV, medium NBV or high NBV. Taking the high-NBV group as reference, the hazard ratios (HRs) for 2-year disability worsening, adjusted for treatment effect, were 1.23 (95% confidence interval (CI): 0.92-1.63, p = 0.16) for the medium NBV and 1.75 (95% CI: 1.26-2.44, p = 0.001) for the low NBV. The predictive value of NBV groups was preserved over 4 years. Treatment effect appeared more evident in low-NBVpatients (HR = 0.58) than in medium-NBV (HR = 0.72) and in high-NBV (HR = 0.80) patients; however, the difference was not significant ( p = 0.57). CONCLUSION: RRMS patients can be categorized into disability risk groups based on individual eNBV values according to baseline demographics and clinical characteristics.
Authors: Brian M Sandroff; Robert W Motl; Cristina A F Román; Glenn R Wylie; John DeLuca; Gary R Cutter; Ralph H B Benedict; Michael G Dwyer; Robert Zivadinov Journal: J Neurol Date: 2022-06-19 Impact factor: 6.682
Authors: David H Miller; Fred D Lublin; Maria Pia Sormani; Ludwig Kappos; Özgür Yaldizli; Mark S Freedman; Bruce A C Cree; Howard L Weiner; Catherine Lubetzki; Hans-Peter Hartung; Xavier Montalban; Bernard M J Uitdehaag; David G MacManus; Tarek A Yousry; Claudia A M Gandini Wheeler-Kingshott; Bingbing Li; Norman Putzki; Martin Merschhemke; Dieter A Häring; Jerry S Wolinsky Journal: Ann Clin Transl Neurol Date: 2018-01-30 Impact factor: 4.511
Authors: Michael G Dwyer; Jesper Hagemeier; Niels Bergsland; Dana Horakova; Jonathan R Korn; Nasreen Khan; Tomas Uher; Jennie Medin; Diego Silva; Manuela Vaneckova; Eva Kubala Havrdova; Robert Zivadinov Journal: Neuroimage Clin Date: 2018-02-07 Impact factor: 4.881
Authors: Laura Gaetano; Baldur Magnusson; Petya Kindalova; Davorka Tomic; Diego Silva; Anna Altermatt; Stefano Magon; Nicole Müller-Lenke; Ernst-Wilhelm Radue; David Leppert; Ludwig Kappos; Jens Wuerfel; Dieter A Häring; Till Sprenger Journal: Mult Scler J Exp Transl Clin Date: 2020-02-18
Authors: Jaume Sastre-Garriga; Deborah Pareto; Marco Battaglini; Maria A Rocca; Olga Ciccarelli; Christian Enzinger; Jens Wuerfel; Maria P Sormani; Frederik Barkhof; Tarek A Yousry; Nicola De Stefano; Mar Tintoré; Massimo Filippi; Claudio Gasperini; Ludwig Kappos; Jordi Río; Jette Frederiksen; Jackie Palace; Hugo Vrenken; Xavier Montalban; Àlex Rovira Journal: Nat Rev Neurol Date: 2020-02-24 Impact factor: 42.937
Authors: Frank Dahlke; Douglas L Arnold; Piet Aarden; Habib Ganjgahi; Dieter A Häring; Jelena Čuklina; Thomas E Nichols; Stephen Gardiner; Robert Bermel; Heinz Wiendl Journal: Mult Scler Date: 2021-01-28 Impact factor: 6.312