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Updated: Apr 5, 2026
The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool
Published on: June 30, 2014
Continuous prediction of secondary progression in the individual course of multiple sclerosis
Bengt Skoog1, Helen Tedeholm1, Björn Runmarker1
1University of Gothenburg, the Sahlgrenska Academy, Institute of Neuroscience and Physiology, Section of Clinical Neuroscience and Rehabilitation, Gothenburg, Sweden.
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).
Insights
Predicting multiple sclerosis (MS) secondary progression (SP) risk is more accurate using later-stage relapse data. A simplified "prediction score" using age and relapse details offers better individual risk assessment over time.
Area of Science:
- Neurology
- Epidemiology
Background:
- Traditional multiple sclerosis (MS) course prediction relied on early-onset features.
- This approach often underestimated individual long-term progression risks.
Purpose of the Study:
- To identify predictors for secondary progression (SP) risk at any point during relapsing-remitting MS.
- To develop a more accurate model for individual MS prognosis.
Main Methods:
- Analysis of an untreated MS incidence cohort (n=306) with 50-year follow-up.
- Poisson regression incorporating relapse data (n=749) and patient demographics (n=157) to predict SP.
- Development of a "prediction score" hazard function.
Main Results:
- The risk of transitioning to SPMS peaked at age 33.
- Significant predictors included age, recent relapse characteristics, and time since relapse.
- The "prediction score" ranged from <0.01 to >0.15 events per patient-year.
Conclusions:
- Individual SP risk in MS fluctuates and is linked to prior relapses.
- Later-stage predictors are more effective than early-onset ones for MS prognosis.
- A simplified three-variable model facilitates adaptable prediction tools, including web applications.
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