Interferon Beta in the Management of Multiple Sclerosis

Intramuscular interferon beta-1a therapy provides a means of reducing the accumulation of physical disability in relapsing-remitting MS

Analysis of NAMCS data for multiple sclerosis, 1998–2004

BACKGROUND: To our knowledge, no study to date has investigated the prescribing patterns of immunomodulatory agents (IMAs) in an outpatient setting in the United States. To address this issue, we performed retrospective data analyses on National Ambulatory Medical Care Survey (NAMCS) data for MS patient visits between 1998 and 2004. METHODS: NAMCS data are a weighted estimate of the nationwide frequency of patients' outpatient clinic visits. We analyzed NAMCS data in the following categories: (1) the proportion of MS patient visits to neurologists, family practitioners or internists, (2) age/gender/race/geographical distribution patterns in patient visits, and (3) the proportion of patients on IMA treatment among established MS patients. RESULTS: There were an estimated 6.7 million multiple sclerosis (MS) patient visits to the clinics between 1998–2004. Neurologists recorded the most patient visits, 50.7%. Patient visits were mostly in the fourth and fifth decade age group (57.9%). The male to female ratio was 1:4. No statistical evidence was observed for a decline or increase in IMA usage. About 62% patients visiting neurologists and 92% seen by family practitioners/internists were not using IMAs. Our results suggest that between the years 1998–2003, the use of interferon-1a tended to decline while the use of interferon-1b and glatiramer acetate, increased. CONCLUSION: Strategies that lead to improved use of IMAs in the management of MS in the outpatient setting are needed

Alternative statistical methods for estimating efficacy of interferon beta-1b for multiple sclerosis clinical trials

<p>Abstract</p> <p>Background</p> <p>In the randomized study of interferon beta-1b (IFN beta-1b) for multiple sclerosis (MS), it has usually been evaluated the simple annual relapse rate as the study endpoint. This study aimed to investigate the performance of various regression models using information regarding the time to each recurrent event and considering the MS specific data generation process, and to estimate the treatment effect of a MS clinical trial data.</p> <p>Methods</p> <p>We conducted a simulation study with consideration of the pathological characteristics of MS, and applied alternative efficacy estimation methods to real clinical trial data, including 5 extended Cox regression models for time-to-event analysis, a Poisson regression model and a Poisson regression model with Generalized Estimating Equations (GEE). We adjusted for other important covariates that may have affected the outcome.</p> <p>Results</p> <p>We compared the simulation results for each model. The hazard ratios of real data were estimated for each model including the effects of other covariates. The results (hazard ratios of high-dose to low-dose) of all models were approximately 0.7 (range, 0.613 - 0.769), whereas the annual relapse rate ratio was 0.714.</p> <p>Conclusions</p> <p>The precision of the treatment estimation was increased by application of the alternative models. This suggests that the use of alternative models that include recurrence event data may provide better analyses.</p

Prediction of acute multiple sclerosis relapses by transcription levels of peripheral blood cells

<p>Abstract</p> <p>Background</p> <p>The ability to predict the spatial frequency of relapses in multiple sclerosis (MS) would enable physicians to decide when to intervene more aggressively and to plan clinical trials more accurately.</p> <p>Methods</p> <p>In the current study our objective was to determine if subsets of genes can predict the time to the next acute relapse in patients with MS. Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 patients with definite MS and 32 patients with clinically isolated syndrome (CIS). The dataset included the expression levels of 10,594 genes and annotated sequences corresponding to 22,215 gene-transcripts that appear in the microarray.</p> <p>Results</p> <p>We designed a two stage predictor. The first stage predictor was based on the expression level of 10 genes, and predicted the time to next relapse with a resolution of 500 days (error rate 0.079, p < 0.001). If the predicted relapse was to occur in less than 500 days, a second stage predictor based on an additional different set of 9 genes was used to give a more accurate estimation of the time till the next relapse (in resolution of 50 days). The error rate of the second stage predictor was 2.3 fold lower than the error rate of random predictions (error rate = 0.35, p < 0.001). The predictors were further evaluated and found effective both for untreated MS patients and for MS patients that subsequently received immunomodulatory treatments after the initial testing (the error rate of the first level predictor was < 0.18 with p < 0.001 for all the patient groups).</p> <p>Conclusion</p> <p>We conclude that gene expression analysis is a valuable tool that can be used in clinical practice to predict future MS disease activity. Similar approach can be also useful for dealing with other autoimmune diseases that characterized by relapsing-remitting nature.</p