calculated using only onset variables according to the
- 0:05 x age (in decades)
- – 1:07 (if female gender)
- + 0:93 (if sphincter onset)
- + 0:62 (if pure motor onset)
- + 0:81 (if motor and sensory onset)
- + 0:32 x number of neurological functional systems
involved at onset
- +0:52 (if incomplete
recovery after onset)
about relapses within the first year it allows easier and earlier
score calculation, i.e. at the onset of the disease. What they find that MSers with a high BREMSO score (above the median or in the 3rd and 4th quartiles) do a lot worse than those with a low BREMSO score (in the first quartile or less that the 25th-percentile). This is an odd comparison as it excludes subjects who fall in the second quartile (between the 25th and 50th-percentile). It is a pity that the paper does not actually give you the data to calculate the cut-offs, which makes it impossible to know if your own BREMSO score falls into a good (1st quartile) or poor (3rd & 4th quartiles) prognostic groups, or for that matter ‘no man’s land (2nd quartile)’.
|An example of a risk score predicting the development of SPMS|
Epub: Bergamaschi et al. BREMSO: a simple score to predict early the natural course of multiple sclerosis. Eur J Neurol. 2015 Mar 25. doi: 10.1111/ene.12696.
BACKGROUND AND PURPOSE: Early prediction of long-term disease evolution is a major challenge in the management of multiple sclerosis (MS). Our aim was to predict the natural course of MS using the Bayesian Risk Estimate for MS at Onset (BREMSO), which gives an individual risk score calculated from demographic and clinical variables collected at disease onset.
METHODS: An observational study was carried out collecting data from MS patients included in MSBase, an international registry. Disease impact was studied using the Multiple Sclerosis Severity Score (MSSS) and time to secondary progression (SP). To evaluate the natural history of the disease, patients were analysed only if they did not receive immune therapies or only up to the time of starting these therapies.
RESULTS: Data from 14 211 patients were analysed. The median BREMSO score was significantly higher in the subgroups of patients whose disease had a major clinical impact (MSSS≥ third quartile vs. ≤ first quartile, P < 0.00001) and who reached SP (P < 0.00001). The BREMSO showed good specificity (79%) as a tool for predicting the clinical impact of MS.
CONCLUSIONS: BREMSO is a simple tool which can be used in the early stages of MS to predict its evolution, supporting therapeutic decisions in an observational setting.