Journal of the Neurological Sciences 287 S1 (2009) S50–S55 Prognosis of the individual course of disease: the elements of time, hetereogeneity and precision Martin Daumer a, *, Anneke Neuhaus a , Joseph Herbert b , George Ebers c a Sylvia Lawry Centre for Multiple Sclerosis Research, Munich, Germany b NYU Hospital for Joint Diseases, New York, USA c Department of Clinical Neurology, University of Oxford, Oxford, United Kingdom article info Keywords: Multiple sclerosis Clinical trials Actimetry Disease progression Outcome measure Prognosis summary There is no gold standard in monitoring disease activity for clinical trials in multiple sclerosis. Various outcome measures, including relapses, disability and magnetic resonance imaging (MRI) measures have been used to demonstrate the efficacy of the different available therapies for multiple sclerosis. Recently, the potential limitations of these measures have received increasing attention, and these have stimulated research into more appropriate and sensitive outcome measures for clinical trials. For example, it has been shown that widely-used MRI measures add little, if any, independent information to that provided by more clinically relevant measures such as relapses and disability. Similarly, the Expanded Disability status Scale (EDSS), which is the most widely-used measure of disability related to multiple sclerosis, is insufficiently sensitive to detect robust changes in disability over the timeframes usually used in clinical trials. An alternative to the EDSS is the Multiple Sclerosis Severity Score (MSSS), a severity scale which relates clinical disability to disease duration. The MSSS was originally developed from a database of nearly ten thousand patients from eleven European countries and Australia and has since been reproduced in an independent dataset of 1134 patients from the placebo arms of randomised clinical trials. Based on the MSSS score, disease severity can be defined, which shows stability over time and may provide evidence-based decision support for patient management. Another alternative to measure disability is the objective quantification of physical activity. There is evidence that recent developments in pervasive computing using tiny accelerometers may have the potential to increase the reliability and precision of motor assessment, especially in the mid-range of the EDSS. The outcome measures discussed have potential use as online tools for evidence-based decision support which are increasingly being used in medical research and clinical decision-making. © 2009 Elsevier B.V. All rights reserved. Introduction Randomised clinical trials of disease-modifying agents in multiple sclerosis have yielded important information on the potential benefits of new therapies and have provided data that is sufficiently robust to demonstrate the efficacy of six such therapies, which have been approved for the treatment of multiple sclerosis on this basis. Nonetheless, for a number of clinically relevant endpoints, and in particular for disability, data generated by these trials have not been interpretable in a way that is universally accepted. The reasons for this are likely to relate to statistical power or to endpoints used in clinical trials. Indeed, given the heterogeneous presentation of the disease and its extremely variable clinical course, it is difficult to detect subtle treatment effects over the relatively short, in terms *Corresponding author. Martin Daumer. Sylvia Lawry Centre for MS Research, Hohenlindener Str. 1, 81677 Munich, Germany. Tel.: +49 89 2060 269 50; fax: +49 89 2060 269 51. E-mail address: daumer@slcmsr.org (M. Daumer). of the course of the disease, duration of placebo-controlled trials (generally two years or less) in populations of generally <200 patients per arm. In addition, the sensitivity of certain evaluation scales may be inadequate to discriminate pertinent treatment effects from random noise [1]. For these reasons, it would be useful to have a patient database that is sufficiently large and well- calibrated to assess the sensitivity of outcome measures and to model the evolution over time of critical disease attributes such as disability. Since 2001, the Sylvia Lawry Centre for Multiple Sclerosis Research (SLCMSR) in Munich has been assembling such a database and using it to evaluate the pertinence of different outcome measures. The Ian McDonald MS database This database was established as an initiative of the Multiple Sclerosis International Federation (MSIF), who wished to fund an international research and resource centre at which sophisticated statistical methods could be used to identify clinical and magnetic 0022-510X/ $ – see front matter © 2009 Elsevier B.V. All rights reserved.