Bengt Skoog1, Helen Tedeholm2, Björn Runmarker2, Anders Odén3, Oluf Andersen2. 1. University of Gothenburg, the Sahlgrenska Academy, Institute of Neuroscience and Physiology, Section of Clinical Neuroscience and Rehabilitation, Gothenburg, Sweden. Electronic address: bengt.skoog@vgregion.se. 2. University of Gothenburg, the Sahlgrenska Academy, Institute of Neuroscience and Physiology, Section of Clinical Neuroscience and Rehabilitation, Gothenburg, Sweden. 3. Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden.
Abstract
BACKGROUND: Prediction of the course of multiple sclerosis (MS) was traditionally based on features close to onset. OBJECTIVE: To evaluate predictors of the individual risk of secondary progression (SP) identified at any time during relapsing-remitting MS. METHODS: We analysed a database comprising an untreated MS incidence cohort (n=306) with five decades of follow-up. Data regarding predictors of all attacks (n=749) and demographics from patients (n=157) with at least one distinct second attack were included as covariates in a Poisson regression analysis with SP as outcome. RESULTS: The average hazard function of transition to SPMS was 0.046 events per patient year, showing a maximum at age 33. Three covariates were significant predictors: age, a descriptor of the most recent relapse, and the interaction between the descriptor and time since the relapse. A hazard function termed "prediction score" estimated the risk of SP as number of transition events per patient year (range <0.01 to >0.15). CONCLUSIONS: The insights gained from this study are that the risk of transition to SP varies over time in individual patients, that the risk of SP is linked to previous relapses, that predictors in the later stages of the course are more effective than the traditional onset predictors, and that the number of potential predictors can be reduced to a few (three in this study) essential items. This advanced simplification facilitates adaption of the "prediction score" to other (more recent, benign or treated) materials, and allows for compact web-based applications (http://msprediction.com).
BACKGROUND: Prediction of the course of multiple sclerosis (MS) was traditionally based on features close to onset. OBJECTIVE: To evaluate predictors of the individual risk of secondary progression (SP) identified at any time during relapsing-remitting MS. METHODS: We analysed a database comprising an untreated MS incidence cohort (n=306) with five decades of follow-up. Data regarding predictors of all attacks (n=749) and demographics from patients (n=157) with at least one distinct second attack were included as covariates in a Poisson regression analysis with SP as outcome. RESULTS: The average hazard function of transition to SPMS was 0.046 events per patient year, showing a maximum at age 33. Three covariates were significant predictors: age, a descriptor of the most recent relapse, and the interaction between the descriptor and time since the relapse. A hazard function termed "prediction score" estimated the risk of SP as number of transition events per patient year (range <0.01 to >0.15). CONCLUSIONS: The insights gained from this study are that the risk of transition to SP varies over time in individual patients, that the risk of SP is linked to previous relapses, that predictors in the later stages of the course are more effective than the traditional onset predictors, and that the number of potential predictors can be reduced to a few (three in this study) essential items. This advanced simplification facilitates adaption of the "prediction score" to other (more recent, benign or treated) materials, and allows for compact web-based applications (http://msprediction.com).
Authors: Thomas F Scott; Bernd C Kieseier; Scott D Newsome; Douglas L Arnold; Xiaojun You; Serena Hung; Bjoern Sperling Journal: Mult Scler J Exp Transl Clin Date: 2016-11-15
Authors: Fraser S Brown; Stella A Glasmacher; Patrick K A Kearns; Niall MacDougall; David Hunt; Peter Connick; Siddharthan Chandran Journal: PLoS One Date: 2020-05-26 Impact factor: 3.240
Authors: Tjalf Ziemssen; Daniela Piani-Meier; Bryan Bennett; Chloe Johnson; Katie Tinsley; Andrew Trigg; Thomas Hach; Frank Dahlke; Davorka Tomic; Chloe Tolley; Mark S Freedman Journal: J Med Internet Res Date: 2020-02-12 Impact factor: 5.428