Original Research Paper
Brain atrophy as a non-response predictor to interferon-beta in relapsing–remitting multiple sclerosis Juan Ignacio Rojas1, Liliana Patrucco1, Jimena Miguez2, Cristina Besada3, Edgardo Cristiano1 1
Multiple Sclerosis Center of Buenos Aires (CEMBA), Neurology Department, Hospital Italiano de Buenos Aires, Argentina, 2Neurology Department, Hospital Italiano de Buenos Aires, Argentina, 3Neuroradiology Department, Hospital Italiano de Buenos Aires, Argentina Background: Several predictors for treatment failure to interferon-beta (IFN-beta) have been proposed; however, brain atrophy has not been well studied. Methods: In this prospective and longitudinal study, all consecutive relapsing–remitting multiple sclerosis (RRMS) patients treated with sc IFN-beta-1a were included. Confirmed disability progression or a new relapse between weeks 48 and 144 after beginning with IFN-beta was considered as treatment nonresponse. EDSS progression, relapses, number of active lesions at 1 year (new or enlarging T2-weighted plus gadolinium-enhancing lesions, categorized in . 2 or # 2), and brain parenchymal fraction (%BVC) volume change within the initial year of treatment were used as predictive factors. Cox regression model was adjusted for age, gender, and disease duration. Results: Seventy-one patients were included (71.8% female) with a follow-up of 144 weeks. Thirty-four (48%) fulfilled criteria of non-response to IFN-beta treatment. The model showed: (1) relapseszdisability progression: HR 5 4.6, 95% IC: 3.1–6.7 (P , 0.001); (2) relapseszBVC decrease: HR 5 4.1, 95% IC: 3.2– 7.3 (P 5 0.001); (3) relapseszdisability progressionznew active lesions: HR 5 10.1, 95% IC: 7.1–15.2 (P , 0.001); and (4) relapseszdisability progressionznew active lesionszBVC decrease: HR 5 14.4, 95% IC: 11.4–21.2 (P , 0.001). Conclusions: Adding BVC measures to previously described predictive failure factors may increase sensitivity to early identify non-responder patients to IFN-beta-1a in the second and third years of therapy. Keywords: Multiple sclerosis, Interferon, Brain atrophy, MR, Response
Introduction Interferon-beta (IFN-beta) is one of the more widespread treatments in the world for the control of the activity of relapsing–remitting multiple sclerosis (RRMS).1,2 Phase III clinical trials have shown that they have a partial efficacy in reducing the annualized relapse rate and the appearance of new lesions on magnetic resonance imaging (MRI)1–3. However, there is a group of patients in whom disease activity persists, despite the use of these drugs, in terms of clinical relapses, increase of lesions observed in MRI, and the progression of physical disability.4,5 While there are no clear established criteria of loss of efficacy for each drug or intervention, there is consensus that proposes the monitoring of certain parameters to consider what has been called suboptimal response or treatment failure.4 Early identi-
fication of patients with treatment failure is crucial for treatment decisions that are staggered in order to control and prevent disease progression thereof.5 There are some studies that have pointed to the value that certain clinical (relapses and EDSS progression) and radiological (increased number of lesions on MRI) markers would have during the first year of using the IFN-beta as predictors of risk for short-term treatment failure.4–6 From the perspective of MRI, the increase in the number of lesions during the first year of treatment was the main variable studied as a predictor, without having assessed the role of the percentage of brain volume change (%BVC) during that period.4,6 Considering the above, the objective of this study was to evaluate the value of %BVC during the first year of treatment with IFN-beta in predicting shortterm treatment failure.
Patients Correspondence to: J. I. Rojas, Multiple Sclerosis Center of Buenos Aires (CEMBA), Neurology Department, Hospital Italiano de Buenos Aires, Argentina, Pero´n 4272, (1411) Buenos Aires, Argentina. Email: juan.rojas@ hospitalitaliano.org.ar
ß W. S. Maney & Son Ltd 2014 DOI 10.1179/1743132813Y.0000000304
RRMS patients7–9 who initiated treatment with IFNbeta-1a sc 44 mcg three times a week were prospectively included between January 2005 and January 2009.
Neurological Research
2014
VOL .
36
NO .
7
615
Rojas et al.
Brain atrophy and response to interferon-beta
Figure 1 Study design. MRI: magnetic resonance image.
Methods All patients were included in an observational followup protocol. During the follow-up, visits were made every 12 weeks for 144 weeks and data were collected based on the number and characteristics of clinical relapses and EDSS changes. Brain MRI was performed with and without gadolinium at baseline and at 48 weeks of starting treatment. The analysis identified two phases: a prediction phase (the first 48 weeks) and the observation phase (weeks 49–144). During the observation phase, we identified those patients who had fulfilled treatment failure criteria (Fig. 1).
During the prediction phase (the first 48 weeks) for each patient, we considered the number of clinical relapses and the progression of EDSS. We compared MRI performed at baseline and 48 weeks of treatment to identify the presence of new lesions and %BVC. We considered a patient as positive for relapse during the prediction phase if the patient presented during the first 48 weeks at least one clinical relapse, defined as the onset of a new neurological symptoms that lasted more than 24 hours, in the absence of clinical uneventful, followed by a period of clinical stability or improvement of at least 30 days. The patient was considered positive for progression also at 48 weeks if EDSS increased § 1 or §1.5 points or if the baseline EDSS was 0, which was sustained for at least 3 months. And the patient was considered positive for MRI activity if . 2 new lesions (or larger in T2, FLAIR, or gadolinium-positive) were observed at 48 weeks. Regarding %BVC, we measured the change in brain volume between baseline and MRI performed at 48 weeks to be used as a predictor along with the other variables.
MRI protocol All patients underwent brain MRI 1.5 T resonator Siemens imaging techniques standardized for patients with demyelinating diseases (proton density, conventional T2, FLAIR, T1 without and with intravenous
Neurological Research
Analysis of brain atrophy Using SIENA (Structural Image Evaluation using Normalization of Atrophy)11, we measured the %BVC between MRI 1 (baseline) and MRI 2 (1 year after starting treatment).12
Observation Phase
Prediction Phase
616
contrast). MRI was evaluated by neuroradiologists blinded to the objective of the study who analyzed the presence of new or enlarger T2 lesion, by direct comparison with the baseline scan according to previously published guidelines.10 Visual analysis of contrast-enhanced T1W images together with proton density/T2 images was performed by the ratter.
2014
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NO .
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This phase included the period between weeks 49 and 144 and aimed to identify those patients who met the criteria of treatment failure. The criteria used to determine therapeutic failure was the presence of at least one clinical relapse or progression of EDSS §1 or §1.5 point increase if the baseline EDSS was 0, which was sustained for at least 3 months. Ethics approval was obtained from the Ethics Committee of Hospital Italiano de Buenos Aires, Argentina. Informed consent was obtained from each patient included.
Statistical analysis We performed a logistic regression analysis to assess the association between the variables obtained in the phase of prediction and observation. The cumulative risk of the event was calculated by Cox regression analysis. P values ,0.05 were considered significant.
Results We included 71 patients (71.8% women) who were followed up for 144 weeks. The initial age of the study population was 30.3¡9.4 years. Of those patients included, 34 (48%) met the criteria for treatment failure. Baseline characteristics and the prediction phase of each group are presented in Table 1. Treatment adherence was over 80% according to the Morisky–Green scale.13 Only one (1.4%) patient was lost to follow-up. The averaged %BVC was 20.7¡0.42 (MRI 2–MRI 1). The following variables
Rojas et al.
Brain atrophy and response to interferon-beta
Table 1 Baseline characteristics and prediction phase of each group Treatment failure2 N (%) Age at onset (years) Female Disease duration (years) EDSS OBz 1 Predictive criteriaz{ 2 Predictive criteriaz{ 3 Predictive criteriaz{ %BVC Lost follow-up
39 (53.4) 30.5¡1.4 27 (69%) 1.5¡0.3 1.1¡0.2 37 (95) 16 (41) 5 (12) 0 20.5¡0.25 0
Treatment failurez
P*
34 (46.6) 30¡1.8 24 (71) 1.3¡0.2 1¡0.5 31 (91) 33 (97) 16 (47) 9 (26.5) 20.88¡0.4 1 (3.4)
… 0.83 0.59 0.34 0.25 0.12 ,0.01 ,0.01 ,0.01 ,0.01 0.22
Note: *Univariate. { Variable phase prediction using data from the first year under IFN-beta and considering positive MR, EDSS, or relapse criteria. %BVC 5 % brain volume change; OB5oligoclonal bands.
were predictive of the risk of treatment failure: (1) presence of relapseszEDSS progression: HR 5 4.6, 95% CI: 3.1–6.7 (P , 0.001); (2) presence of relapsez %BVC: HR 5 4.1, 95% CI: 3.2–7.3 (P 5 0.001); (3) presence of EDSS progressionzrelapsesznew MRI lesions: HR 5 10.1, 95% CI: 7.1–15.2 (P ,0.001); and (4) presence of EDSS progressionzrelapsesznew lesions on MRIz%BVC: HR 5 14.4, 95% CI: 11.4– 21.2 (P , 0.001) (Table 2).
Discussion The present study shows the role that a decrease in %BVC in the first year of treatment with IFN-beta would be a predictor of treatment failure in the short term. We observed that the group who met treatment failure criteria presented a higher decrease in %BVC when adjusting for other variables already known (relapses during the first year, disability progression, and new lesions on MRI). Previous studies have demonstrated the predictive value for treatment failure of the combination of relapses, EDSS progression, and new lesions during the first year of treatment.4–6 However, these studies did not included %BVC as a predictor of risk.
Table 2 Risk of treatment failure considering the criteria evaluated during the predictive phase (MRI, EDSS, relapses, and brain atrophy) P MRIz EDSSz Relapsez %BVC MRIz/EDSSz MRIz/relapsez MRIz/brain atrophy rate Relapsesz/EDSSz Relapsesz/%BVC EDSSz/%BVC MRIz/relapsesz/EDSSz MRIz/relapsesz/EDSSz/%BVC
HR
95% CI
0.36 1.1 0.65–1.7 0.11 1.4 0.87–1.9 0.09 1.8 0.68–2.7 0.1 1.3 0.76–2.5 0.07 1.8 0.7–2.3 0.05 2.4 0.89–3.6 0.09 2.1 0.9–2.9 ,0.001 4.6 3.1–6.7 ,0.001 4.1 3.2–7.3 0.06 1.6 0.75–2.3 ,0.001 10.1 7.1–15.2 ,0.001 14.4 11.4–21.2
Note: MRI 5 magnetic resonance image; %BVC 5 % brain volume change.
In our model, the inclusion of brain atrophy coupled with the presence of relapse, EDSS progression, and new lesions per year increased by approximately four times the risk of treatment failure. The finding is likely to be a result of an increase in neurodegenerative issues at the central nervous system.14 However, the pathophysiological mechanism of this observation is not clear at present. The limitations of the study should be noted. Primarily, it is an observational study, with the risk that this implies. However, given the objective of the measurements and the close monitoring of patients, the possibility of bias is reduced significantly. In conclusion, we found that the decrease in %BVC per year after starting treatment with clinical and radiological factors would increase the predictive value for identifying patients at risk of treatment failure during the follow-up. Given the partial efficacy of current treatments for RRMS, it is necessary to identify early markers of response in order to optimize therapy for each patient.15–17
Conflict of Interest Juan Ignacio Rojas has received honoraria from Novartis as a scientific advisor. He has received travel grants and attended courses and conferences on behalf of Merck-Serono Argentina, Novartis Argentina. Liliana Patrucco has received honoraria for scientific and research grants from Teva Tuteur, Merck Serono, Biogen Idec, and Bayer Schering. Cristina Besada declares no conflict of interest. Jimena Miguez declares no conflict of interest Edgardo Cristiano has received fees for consultations as a scientific advisory board member and for travel to meetings, conferences, and clinical trials of the following companies: Avanir, Bayer, Biogen, Merck, Novartis, and Teva.
References 1 Randomised double-blind placebo-controlled study of interferon beta-1a in relapsing/remitting multiple sclerosis. PRISMS
Neurological Research
2014
VOL .
36
NO .
7
617
Rojas et al.
2
3
4 5 6 7
8
9
618
Brain atrophy and response to interferon-beta
(Prevention of Relapses and Disability by Interferon beta-1a Subcutaneously in Multiple Sclerosis) Study Group. Lancet. 1998;352(9139):1498–504. Placebo-controlled multicentre randomised trial of interferon beta-1b in treatment of secondary progressive multiple sclerosis. European Study Group on interferon beta-1b in secondary progressive MS. Lancet. 1998;352(9139):1491–7. Jacobs LD, Cookfair DL, Rudick RA, Herndon RM, Richert JR, Salazar AM, et al. Intramuscular interferon beta-1a for disease progression in relapsing multiple sclerosis. The Multiple Sclerosis Collaborative Research Group (MSCRG). Ann Neurol. 1996;39(3):285–94. Rio J, Comabella M, Montalban X. Predicting responders to therapies for multiple sclerosis. Nat Rev Neurol. 2009;5(10): 553–60. Rio J, Nos C, Tintore M, Tellez N, Galan I, Pelayo R, et al. Defining the response to interferon-beta in relapsing–remitting multiple sclerosis patients. Ann Neurol. 2006;59(2):344–52. Horakova D, Kalincik T, Dolezal O, Krasensky J, Vaneckova M, Seidl Z, et al. Early predictors of non-response to interferon in multiple sclerosis. Acta Neurol Scand. 2012;126(6):390–7. Polman CH, Reingold SC, Edan G, Filippi M, Hartung HP, Kappos L, et al. Diagnostic criteria for multiple sclerosis: 2005 revisions to the ‘McDonald Criteria’. Ann Neurol. 2005;58(6): 840–6. McDonald WI, Compston A, Edan G, Goodkin D, Hartung HP, Lublin FD, et al. Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis. Ann Neurol. 2001;50(1):121– 7. Poser CM, Paty DW, Scheinberg L, McDonald WI, Davis FA, Ebers GC, et al. New diagnostic criteria for multiple sclerosis:
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11 12
13
14 15
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guidelines for research protocols. Ann Neurol. 1983;13(3):227– 31. Molyneux PD, Miller DH, Filippi M, Yousry TA, Radu EW, Ader HJ, et al. Visual analysis of serial T2-weighted MRI in multiple sclerosis: intra- and interobserver reproducibility. Neuroradiology. 1999;41(12):882–8. Smith SM, de Stefano N, Jenkinson M, Matthews PM. Normalized accurate measurement of longitudinal brain change. J Comput Assist Tomogr. 2001;25(3):466–75. Smith SM, Zhang Y, Jenkinson M, Chen J, Matthews PM, Federico A, et al. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage. 2002; 17(1):479–89. Cardoso L, Miasso AI, Galera SA, Maia BM, Esteves RB. Adherence level and knowledge about psychopharmacological treatment among patients discharged from psychiatric internment. Rev Lat Am Enfermagem. 2011;19(5):1146–54. Trapp BD, Peterson J, Ransohoff RM, Rudick R, Mork S, Bo L. Axonal transection in the lesions of multiple sclerosis. N Engl J Med. 1998;338(5):278–85. Filippi M, Rocca MA, de Stefano N, Enzinger C, Fisher E, Horsfield MA, et al. Magnetic resonance techniques in multiple sclerosis: the present and the future. Arch Neurol. 2011;68(12): 1514–20. Filippi M, Rocca MA, Benedict RH, DeLuca J, Geurts JJ, Rombouts SA, et al. The contribution of MRI in assessing cognitive impairment in multiple sclerosis. Neurology. 2010;75(23):2121–8. Barkhof F, Simon JH, Fazekas F, Rovaris M, Kappos L, de Stefano N, et al. MRI monitoring of immunomodulation in relapse-onset multiple sclerosis trials. Nat Rev Neurol. 2012;8(1):13–21.
Brain atrophy as a non-response predictor to interferon-beta in relapsing–remitting multiple sclerosis Juan Ignacio Rojas1, Liliana Patrucco1, Jimena Miguez2, Cristina Besada3, Edgardo Cristiano1 1
Multiple Sclerosis Center of Buenos Aires (CEMBA), Neurology Department, Hospital Italiano de Buenos Aires, Argentina, 2Neurology Department, Hospital Italiano de Buenos Aires, Argentina, 3Neuroradiology Department, Hospital Italiano de Buenos Aires, Argentina Background: Several predictors for treatment failure to interferon-beta (IFN-beta) have been proposed; however, brain atrophy has not been well studied. Methods: In this prospective and longitudinal study, all consecutive relapsing–remitting multiple sclerosis (RRMS) patients treated with sc IFN-beta-1a were included. Confirmed disability progression or a new relapse between weeks 48 and 144 after beginning with IFN-beta was considered as treatment nonresponse. EDSS progression, relapses, number of active lesions at 1 year (new or enlarging T2-weighted plus gadolinium-enhancing lesions, categorized in . 2 or # 2), and brain parenchymal fraction (%BVC) volume change within the initial year of treatment were used as predictive factors. Cox regression model was adjusted for age, gender, and disease duration. Results: Seventy-one patients were included (71.8% female) with a follow-up of 144 weeks. Thirty-four (48%) fulfilled criteria of non-response to IFN-beta treatment. The model showed: (1) relapseszdisability progression: HR 5 4.6, 95% IC: 3.1–6.7 (P , 0.001); (2) relapseszBVC decrease: HR 5 4.1, 95% IC: 3.2– 7.3 (P 5 0.001); (3) relapseszdisability progressionznew active lesions: HR 5 10.1, 95% IC: 7.1–15.2 (P , 0.001); and (4) relapseszdisability progressionznew active lesionszBVC decrease: HR 5 14.4, 95% IC: 11.4–21.2 (P , 0.001). Conclusions: Adding BVC measures to previously described predictive failure factors may increase sensitivity to early identify non-responder patients to IFN-beta-1a in the second and third years of therapy. Keywords: Multiple sclerosis, Interferon, Brain atrophy, MR, Response
Introduction Interferon-beta (IFN-beta) is one of the more widespread treatments in the world for the control of the activity of relapsing–remitting multiple sclerosis (RRMS).1,2 Phase III clinical trials have shown that they have a partial efficacy in reducing the annualized relapse rate and the appearance of new lesions on magnetic resonance imaging (MRI)1–3. However, there is a group of patients in whom disease activity persists, despite the use of these drugs, in terms of clinical relapses, increase of lesions observed in MRI, and the progression of physical disability.4,5 While there are no clear established criteria of loss of efficacy for each drug or intervention, there is consensus that proposes the monitoring of certain parameters to consider what has been called suboptimal response or treatment failure.4 Early identi-
fication of patients with treatment failure is crucial for treatment decisions that are staggered in order to control and prevent disease progression thereof.5 There are some studies that have pointed to the value that certain clinical (relapses and EDSS progression) and radiological (increased number of lesions on MRI) markers would have during the first year of using the IFN-beta as predictors of risk for short-term treatment failure.4–6 From the perspective of MRI, the increase in the number of lesions during the first year of treatment was the main variable studied as a predictor, without having assessed the role of the percentage of brain volume change (%BVC) during that period.4,6 Considering the above, the objective of this study was to evaluate the value of %BVC during the first year of treatment with IFN-beta in predicting shortterm treatment failure.
Patients Correspondence to: J. I. Rojas, Multiple Sclerosis Center of Buenos Aires (CEMBA), Neurology Department, Hospital Italiano de Buenos Aires, Argentina, Pero´n 4272, (1411) Buenos Aires, Argentina. Email: juan.rojas@ hospitalitaliano.org.ar
ß W. S. Maney & Son Ltd 2014 DOI 10.1179/1743132813Y.0000000304
RRMS patients7–9 who initiated treatment with IFNbeta-1a sc 44 mcg three times a week were prospectively included between January 2005 and January 2009.
Neurological Research
2014
VOL .
36
NO .
7
615
Rojas et al.
Brain atrophy and response to interferon-beta
Figure 1 Study design. MRI: magnetic resonance image.
Methods All patients were included in an observational followup protocol. During the follow-up, visits were made every 12 weeks for 144 weeks and data were collected based on the number and characteristics of clinical relapses and EDSS changes. Brain MRI was performed with and without gadolinium at baseline and at 48 weeks of starting treatment. The analysis identified two phases: a prediction phase (the first 48 weeks) and the observation phase (weeks 49–144). During the observation phase, we identified those patients who had fulfilled treatment failure criteria (Fig. 1).
During the prediction phase (the first 48 weeks) for each patient, we considered the number of clinical relapses and the progression of EDSS. We compared MRI performed at baseline and 48 weeks of treatment to identify the presence of new lesions and %BVC. We considered a patient as positive for relapse during the prediction phase if the patient presented during the first 48 weeks at least one clinical relapse, defined as the onset of a new neurological symptoms that lasted more than 24 hours, in the absence of clinical uneventful, followed by a period of clinical stability or improvement of at least 30 days. The patient was considered positive for progression also at 48 weeks if EDSS increased § 1 or §1.5 points or if the baseline EDSS was 0, which was sustained for at least 3 months. And the patient was considered positive for MRI activity if . 2 new lesions (or larger in T2, FLAIR, or gadolinium-positive) were observed at 48 weeks. Regarding %BVC, we measured the change in brain volume between baseline and MRI performed at 48 weeks to be used as a predictor along with the other variables.
MRI protocol All patients underwent brain MRI 1.5 T resonator Siemens imaging techniques standardized for patients with demyelinating diseases (proton density, conventional T2, FLAIR, T1 without and with intravenous
Neurological Research
Analysis of brain atrophy Using SIENA (Structural Image Evaluation using Normalization of Atrophy)11, we measured the %BVC between MRI 1 (baseline) and MRI 2 (1 year after starting treatment).12
Observation Phase
Prediction Phase
616
contrast). MRI was evaluated by neuroradiologists blinded to the objective of the study who analyzed the presence of new or enlarger T2 lesion, by direct comparison with the baseline scan according to previously published guidelines.10 Visual analysis of contrast-enhanced T1W images together with proton density/T2 images was performed by the ratter.
2014
VOL .
36
NO .
7
This phase included the period between weeks 49 and 144 and aimed to identify those patients who met the criteria of treatment failure. The criteria used to determine therapeutic failure was the presence of at least one clinical relapse or progression of EDSS §1 or §1.5 point increase if the baseline EDSS was 0, which was sustained for at least 3 months. Ethics approval was obtained from the Ethics Committee of Hospital Italiano de Buenos Aires, Argentina. Informed consent was obtained from each patient included.
Statistical analysis We performed a logistic regression analysis to assess the association between the variables obtained in the phase of prediction and observation. The cumulative risk of the event was calculated by Cox regression analysis. P values ,0.05 were considered significant.
Results We included 71 patients (71.8% women) who were followed up for 144 weeks. The initial age of the study population was 30.3¡9.4 years. Of those patients included, 34 (48%) met the criteria for treatment failure. Baseline characteristics and the prediction phase of each group are presented in Table 1. Treatment adherence was over 80% according to the Morisky–Green scale.13 Only one (1.4%) patient was lost to follow-up. The averaged %BVC was 20.7¡0.42 (MRI 2–MRI 1). The following variables
Rojas et al.
Brain atrophy and response to interferon-beta
Table 1 Baseline characteristics and prediction phase of each group Treatment failure2 N (%) Age at onset (years) Female Disease duration (years) EDSS OBz 1 Predictive criteriaz{ 2 Predictive criteriaz{ 3 Predictive criteriaz{ %BVC Lost follow-up
39 (53.4) 30.5¡1.4 27 (69%) 1.5¡0.3 1.1¡0.2 37 (95) 16 (41) 5 (12) 0 20.5¡0.25 0
Treatment failurez
P*
34 (46.6) 30¡1.8 24 (71) 1.3¡0.2 1¡0.5 31 (91) 33 (97) 16 (47) 9 (26.5) 20.88¡0.4 1 (3.4)
… 0.83 0.59 0.34 0.25 0.12 ,0.01 ,0.01 ,0.01 ,0.01 0.22
Note: *Univariate. { Variable phase prediction using data from the first year under IFN-beta and considering positive MR, EDSS, or relapse criteria. %BVC 5 % brain volume change; OB5oligoclonal bands.
were predictive of the risk of treatment failure: (1) presence of relapseszEDSS progression: HR 5 4.6, 95% CI: 3.1–6.7 (P , 0.001); (2) presence of relapsez %BVC: HR 5 4.1, 95% CI: 3.2–7.3 (P 5 0.001); (3) presence of EDSS progressionzrelapsesznew MRI lesions: HR 5 10.1, 95% CI: 7.1–15.2 (P ,0.001); and (4) presence of EDSS progressionzrelapsesznew lesions on MRIz%BVC: HR 5 14.4, 95% CI: 11.4– 21.2 (P , 0.001) (Table 2).
Discussion The present study shows the role that a decrease in %BVC in the first year of treatment with IFN-beta would be a predictor of treatment failure in the short term. We observed that the group who met treatment failure criteria presented a higher decrease in %BVC when adjusting for other variables already known (relapses during the first year, disability progression, and new lesions on MRI). Previous studies have demonstrated the predictive value for treatment failure of the combination of relapses, EDSS progression, and new lesions during the first year of treatment.4–6 However, these studies did not included %BVC as a predictor of risk.
Table 2 Risk of treatment failure considering the criteria evaluated during the predictive phase (MRI, EDSS, relapses, and brain atrophy) P MRIz EDSSz Relapsez %BVC MRIz/EDSSz MRIz/relapsez MRIz/brain atrophy rate Relapsesz/EDSSz Relapsesz/%BVC EDSSz/%BVC MRIz/relapsesz/EDSSz MRIz/relapsesz/EDSSz/%BVC
HR
95% CI
0.36 1.1 0.65–1.7 0.11 1.4 0.87–1.9 0.09 1.8 0.68–2.7 0.1 1.3 0.76–2.5 0.07 1.8 0.7–2.3 0.05 2.4 0.89–3.6 0.09 2.1 0.9–2.9 ,0.001 4.6 3.1–6.7 ,0.001 4.1 3.2–7.3 0.06 1.6 0.75–2.3 ,0.001 10.1 7.1–15.2 ,0.001 14.4 11.4–21.2
Note: MRI 5 magnetic resonance image; %BVC 5 % brain volume change.
In our model, the inclusion of brain atrophy coupled with the presence of relapse, EDSS progression, and new lesions per year increased by approximately four times the risk of treatment failure. The finding is likely to be a result of an increase in neurodegenerative issues at the central nervous system.14 However, the pathophysiological mechanism of this observation is not clear at present. The limitations of the study should be noted. Primarily, it is an observational study, with the risk that this implies. However, given the objective of the measurements and the close monitoring of patients, the possibility of bias is reduced significantly. In conclusion, we found that the decrease in %BVC per year after starting treatment with clinical and radiological factors would increase the predictive value for identifying patients at risk of treatment failure during the follow-up. Given the partial efficacy of current treatments for RRMS, it is necessary to identify early markers of response in order to optimize therapy for each patient.15–17
Conflict of Interest Juan Ignacio Rojas has received honoraria from Novartis as a scientific advisor. He has received travel grants and attended courses and conferences on behalf of Merck-Serono Argentina, Novartis Argentina. Liliana Patrucco has received honoraria for scientific and research grants from Teva Tuteur, Merck Serono, Biogen Idec, and Bayer Schering. Cristina Besada declares no conflict of interest. Jimena Miguez declares no conflict of interest Edgardo Cristiano has received fees for consultations as a scientific advisory board member and for travel to meetings, conferences, and clinical trials of the following companies: Avanir, Bayer, Biogen, Merck, Novartis, and Teva.
References 1 Randomised double-blind placebo-controlled study of interferon beta-1a in relapsing/remitting multiple sclerosis. PRISMS
Neurological Research
2014
VOL .
36
NO .
7
617
Rojas et al.
2
3
4 5 6 7
8
9
618
Brain atrophy and response to interferon-beta
(Prevention of Relapses and Disability by Interferon beta-1a Subcutaneously in Multiple Sclerosis) Study Group. Lancet. 1998;352(9139):1498–504. Placebo-controlled multicentre randomised trial of interferon beta-1b in treatment of secondary progressive multiple sclerosis. European Study Group on interferon beta-1b in secondary progressive MS. Lancet. 1998;352(9139):1491–7. Jacobs LD, Cookfair DL, Rudick RA, Herndon RM, Richert JR, Salazar AM, et al. Intramuscular interferon beta-1a for disease progression in relapsing multiple sclerosis. The Multiple Sclerosis Collaborative Research Group (MSCRG). Ann Neurol. 1996;39(3):285–94. Rio J, Comabella M, Montalban X. Predicting responders to therapies for multiple sclerosis. Nat Rev Neurol. 2009;5(10): 553–60. Rio J, Nos C, Tintore M, Tellez N, Galan I, Pelayo R, et al. Defining the response to interferon-beta in relapsing–remitting multiple sclerosis patients. Ann Neurol. 2006;59(2):344–52. Horakova D, Kalincik T, Dolezal O, Krasensky J, Vaneckova M, Seidl Z, et al. Early predictors of non-response to interferon in multiple sclerosis. Acta Neurol Scand. 2012;126(6):390–7. Polman CH, Reingold SC, Edan G, Filippi M, Hartung HP, Kappos L, et al. Diagnostic criteria for multiple sclerosis: 2005 revisions to the ‘McDonald Criteria’. Ann Neurol. 2005;58(6): 840–6. McDonald WI, Compston A, Edan G, Goodkin D, Hartung HP, Lublin FD, et al. Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis. Ann Neurol. 2001;50(1):121– 7. Poser CM, Paty DW, Scheinberg L, McDonald WI, Davis FA, Ebers GC, et al. New diagnostic criteria for multiple sclerosis:
Neurological Research
2014
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36
NO .
7
10
11 12
13
14 15
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