TY - JOUR
T1 - Towards personalized therapy for multiple sclerosis
T2 - prediction of individual treatment response
AU - Kalincik, Tomas
AU - Manouchehrinia, Ali
AU - Sobisek, Lukas
AU - Jokubaitis, Vilija
AU - Spelman, Tim
AU - Horakova, Dana
AU - Havrdova, Eva
AU - Trojano, Maria
AU - Izquierdo, Guillermo
AU - Lugaresi, Alessandra
AU - Girard, Marc
AU - Prat, Alexandre
AU - Duquette, Pierre
AU - Grammond, Pierre
AU - Sola, Patrizia
AU - Hupperts, Raymond
AU - Grand'Maison, Francois
AU - Pucci, Eugenio
AU - Boz, Cavit
AU - Alroughani, Raed
AU - Van Pesch, Vincent
AU - Lechner-Scott, Jeannette
AU - Terzi, Murat
AU - Bergamaschi, Roberto
AU - Iuliano, Gerardo
AU - Granella, Franco
AU - Spitaleri, Daniele
AU - Shaygannejad, Vahid
AU - Oreja-Guevara, Celia
AU - Slee, Mark
AU - Ampapa, Radek
AU - Verheul, Freek
AU - McCombe, Pamela
AU - Olascoaga, Javier
AU - Amato, Maria Pia
AU - Vucic, Steve
AU - Hodgkinson, Suzanne
AU - Ramo-Tello, Cristina
AU - Flechter, Shlomo
AU - Cristiano, Edgardo
AU - Rozsa, Csilla
AU - Moore, Fraser
AU - Luis Sanchez-Menoyo, Jose
AU - Laura Saladino, Maria
AU - Barnett, Michael
AU - Hillert, Jan
AU - Butzkueven, Helmut
AU - MSBase Study Group
N1 - © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
PY - 2017/9/1
Y1 - 2017/9/1
N2 - 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.
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.
KW - Adult
KW - Algorithms
KW - Databases, Factual
KW - Demography
KW - Disability Evaluation
KW - Disease Progression
KW - Female
KW - Forecasting
KW - Humans
KW - Immunosuppressive Agents
KW - Male
KW - Multiple Sclerosis
KW - Precision Medicine
KW - Prognosis
KW - Recurrence
KW - Reproducibility of Results
KW - Risk Factors
KW - Treatment Outcome
KW - Young Adult
KW - Clinical Trial
KW - Journal Article
KW - Multicenter Study
U2 - 10.1093/brain/awx185
DO - 10.1093/brain/awx185
M3 - Article
C2 - 29050389
SN - 0006-8950
VL - 140
SP - 2426
EP - 2443
JO - Brain
JF - Brain
IS - 9
ER -