OBJECTIVE: To identify early clinical and MRI predictors of non-response to interferon (IFN) treatment in multiple sclerosis (MS). METHODS: In 172 patients with relapsing-remitting MS treated withIFNβ, we evaluated prediction of future treatment non-response. Candidate predictors comprised disability and its sustained progression, relapse score (combining frequency and severity of relapses), brain volume change, brain parenchymal fraction, number of new T2 lesions, and T2 and T1 lesion volume within the initial year of treatment. Treatment non-response was evaluated as confirmed disability progression or overall average annual relapse score exceeding 1 over the following 5 years. Logistic regression model was adjusted for patient age, gender, disease duration and changes in treatment. RESULTS: Ninety patients (52%) reached the status of IFN non-responders in years 2-6. Patients with ≥1 new T2 lesion and relapse score ≥2 (odds ratio ≥5.7) or those with ≥3 new T2 lesions regardless of the relapse score (odds ratio = 3) were in a significantly higher risk of future treatment non-response. CONCLUSIONS: In patients with MS treated with IFNβ for 1 year, number of new T2 lesions and annualized relapse score predict individual risk of treatment non-response over the following 5 years.
RCT Entities:
OBJECTIVE: To identify early clinical and MRI predictors of non-response to interferon (IFN) treatment in multiple sclerosis (MS). METHODS: In 172 patients with relapsing-remitting MS treated with IFNβ, we evaluated prediction of future treatment non-response. Candidate predictors comprised disability and its sustained progression, relapse score (combining frequency and severity of relapses), brain volume change, brain parenchymal fraction, number of new T2 lesions, and T2 and T1 lesion volume within the initial year of treatment. Treatment non-response was evaluated as confirmed disability progression or overall average annual relapse score exceeding 1 over the following 5 years. Logistic regression model was adjusted for patient age, gender, disease duration and changes in treatment. RESULTS: Ninety patients (52%) reached the status of IFN non-responders in years 2-6. Patients with ≥1 new T2 lesion and relapse score ≥2 (odds ratio ≥5.7) or those with ≥3 new T2 lesions regardless of the relapse score (odds ratio = 3) were in a significantly higher risk of future treatment non-response. CONCLUSIONS: In patients with MS treated with IFNβ for 1 year, number of new T2 lesions and annualized relapse score predict individual risk of treatment non-response over the following 5 years.
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: F Esposito; L Ferrè; F Clarelli; M A Rocca; G Sferruzza; L Storelli; M Radaelli; F Sangalli; L Moiola; B Colombo; F Martinelli Boneschi; G Comi; M Filippi; V Martinelli Journal: J Neurol Date: 2018-02-12 Impact factor: 4.849
Authors: A Burgetova; P Dusek; M Vaneckova; D Horakova; C Langkammer; J Krasensky; L Sobisek; P Matras; M Masek; Z Seidl Journal: AJNR Am J Neuroradiol Date: 2017-04-27 Impact factor: 3.825
Authors: R Zivadinov; N Bergsland; O Dolezal; S Hussein; Z Seidl; M G Dwyer; M Vaneckova; J Krasensky; J A Potts; T Kalincik; E Havrdová; D Horáková Journal: AJNR Am J Neuroradiol Date: 2013-04-11 Impact factor: 3.825
Authors: Tomas Kalincik; Manuela Vaneckova; Michaela Tyblova; Jan Krasensky; Zdenek Seidl; Eva Havrdova; Dana Horakova Journal: PLoS One Date: 2012-11-15 Impact factor: 3.240