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Prognosis of the individual course of disease - steps in developing a decision support tool for Multiple Sclerosis

M Daumer12*, A Neuhaus1, C Lederer12, M Scholz2, JS Wolinsky3, M Heiderhoff4 and the Sylvia Lawry Centre for Multiple Sclerosis Research

Author Affiliations

1 Sylvia Lawry Centre for Multiple Sclerosis Research, Munich, Germany

2 Trium Analysis Online GmbH, Munich, Germany

3 University of Texas Health Science Center, Houston, TX, USA

4 University Hospital Heidelberg, Department of General Practice and Health Services Research, Heidelberg, Germany

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BMC Medical Informatics and Decision Making 2007, 7:11  doi:10.1186/1472-6947-7-11

Published: 8 May 2007

Abstract

Background

Multiple sclerosis is a chronic disease of uncertain aetiology. Variations in its disease course make it difficult to impossible to accurately determine the prognosis of individual patients. The Sylvia Lawry Centre for Multiple Sclerosis Research (SLCMSR) developed an "online analytical processing (OLAP)" tool that takes advantage of extant clinical trials data and allows one to model the near term future course of this chronic disease for an individual patient.

Results

For a given patient the most similar patients of the SLCMSR database are intelligently selected by a model-based matching algorithm integrated into an OLAP-tool to enable real time, web-based statistical analyses. The underlying database (last update April 2005) contains 1,059 patients derived from 30 placebo arms of controlled clinical trials. Demographic information on the entire database and the portion selected for comparison are displayed. The result of the statistical comparison is provided as a display of the course of Expanded Disability Status Scale (EDSS) for individuals in the database with regions of probable progression over time, along with their mean relapse rate. Kaplan-Meier curves for time to sustained progression in the EDSS and time to requirement of constant assistance to walk (EDSS 6) are also displayed. The software-application OLAP anticipates the input MS patient's course on the basis of baseline values and the known course of disease for similar patients who have been followed in clinical trials.

Conclusion

This simulation could be useful for physicians, researchers and other professionals who counsel patients on therapeutic options. The application can be modified for studying the natural history of other chronic diseases, if and when similar datasets on which the OLAP operates exist.