RT Journal Article
T1 Towards personalized therapy for multiple sclerosis: prediction of individual treatment response
A1 Kalincik, Tomas
A1 Manouchehrinia, Ali
A1 Sobisek, Lukas
A1 Jokubaitis, Vilija
A1 Spelman, Tim
A1 Horakova, Dana
A1 Havrdova, Eva
A1 Trojano, Maria
A1 Izquierdo, Guillermo
A1 Lugaresi, Alessandra
A1 Girard, Marc
A1 Prat, Alexandre
A1 Duquette, Pierre
A1 Grammond, Pierre
A1 Sola, Patrizia
A1 Hupperts, Raymond
A1 Grand'Maison, Francois
A1 Pucci, Eugenio
A1 Boz, Cavit
A1 Alroughani, Raed
A1 Van Pesch, Vincent
A1 Lechner-Scott, Jeannette
A1 Terzi, Murat
A1 Bergamaschi, Roberto
A1 Iuliano, Gerardo
A1 Granella, Franco
A1 Spitaleri, Daniele
A1 Shaygannejad, Vahid
A1 Oreja-Guevara, Celia
A1 Slee, Mark
A1 Ampapa, Radek
A1 Verheul, Freek
A1 McCombe, Pamela
A1 Olascoaga, Javier
A1 Amato, Maria Pia
A1 Vucic, Steve
A1 Hodgkinson, Suzanne
A1 Ramo-Tello, Cristina
A1 Flechter, Shlomo
A1 Cristiano, Edgardo
A1 Rozsa, Csilla
A1 Moore, Fraser
A1 Luis Sanchez-Menoyo, Jose
A1 Laura Saladino, Maria
A1 Barnett, Michael
A1 Hillert, Jan
A1 Butzkueven, Helmut
A1 MSBase Study Grp, 
K1 multiple sclerosis
K1 prediction
K1 disability
K1 relapses
K1 precision medicine
K1 Interferon-beta therapy
K1 Glatiramer acetate
K1 Prognostic-factors
K1 Disability
K1 Natalizumab
K1 Progression
K1 Registry
K1 Failure
K1 Switch
AB Timely initiation of effective therapy is crucial for preventing disability in multiple sclerosis; however, treatment response varies greatly among patients. Comprehensive predictive models of individual treatment response are lacking. Our aims were: (i) to develop predictive algorithms for individual treatment response using demographic, clinical and paraclinical predictors in patients with multiple sclerosis; and (ii) to evaluate accuracy, and internal and external validity of these algorithms. This study evaluated 27 demographic, clinical and paraclinical predictors of individual response to seven disease-modifying therapies in MSBase, a large global cohort study. Treatment response was analysed separately for disability progression, disability regression, relapse frequency, conversion to secondary progressive disease, change in the cumulative disease burden, and the probability of treatment discontinuation. Multivariable survival and generalized linear models were used, together with the principal component analysis to reduce model dimensionality and prevent overparameterization. Accuracy of the individual prediction was tested and its internal validity was evaluated in a separate, non-overlapping cohort. External validity was evaluated in a geographically distinct cohort, the Swedish Multiple Sclerosis Registry. In the training cohort (n = 8513), the most prominent modifiers of treatment response comprised age, disease duration, disease course, previous relapse activity, disability, predominant relapse phenotype and previous therapy. Importantly, the magnitude and direction of the associations varied among therapies and disease outcomes. Higher probability of disability progression during treatment with injectable therapies was predominantly associated with a greater disability at treatment start and the previous therapy. For fingolimod, natalizumab or mitoxantrone, it was mainly associated with lower pretreatment relapse activity. The probability of disability regression was predominantly associated with pre-baseline disability, therapy and relapse activity. Relapse incidence was associated with pretreatment relapse activity, age and relapsing disease course, with the strength of these associations varying among therapies. Accuracy and internal validity (n = 1196) of the resulting predictive models was high (>80%) for relapse incidence during the first year and for disability outcomes, moderate for relapse incidence in Years 2-4 and for the change in the cumulative disease burden, and low for conversion to secondary progressive disease and treatment discontinuation. External validation showed similar results, demonstrating high external validity for disability and relapse outcomes, moderate external validity for cumulative disease burden and low external validity for conversion to secondary progressive disease and treatment discontinuation. We conclude that demographic, clinical and paraclinical information helps predict individual response to disease-modifying therapies at the time of their commencement.
PB Oxford univ press
SN 0006-8950
YR 2017
FD 2017-09-01
LK http://hdl.handle.net/10668/18936
UL http://hdl.handle.net/10668/18936
LA en
DS RISalud
RD Apr 9, 2025