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New MS Testing Model Can Predict Intensity of Disease

September 15th, 2015



A new study published in the journal Statistical Methods in Medical Research revealed a new method to determine the disease course in multiple sclerosis (MS) patients. The study is entitled “Joint assessment of dependent discrete disease state processes” and was conducted by researchers at the Brigham Young University, Brigham and Women’s Hospital and Massachusetts General Hospital.

Researchers have developed a model that can predict whether the disease will intensify. This method relies on the patient’s history, which allows for a personalized prediction for each patient. “The goal all along has been to develop personalized transition probabilities with regard to where they are in the disease process and where they’re most likely to go in the near future,” said the study’s lead author Dr. David Engler in a news release.

MS patients usually visit their doctors twice a year. In these follow-up visits, their disability level is assessed, through the expanded disability severity scale score and relapse state. In this model, a doctor would simply introduce the patient’s data on relapse and disability score of the last two follow-up visits, plus some demographic data concerning the patient. The data is collected from the patient by his or doctor in a two-step process: The patients indicate if they suffered a relapse since their last doctor visit. If so, patients then rate the intensity of the relapse symptoms on a 21-point scale (0, 0.5, 1.0 … 9.0, 9.5, 10). This information is then entered into the testing model.

This model would then determine the odds that the individual’s disease would get better, get worse or stay the same. If the model suggests that the patient will be in worse shape in 6 months, the doctor would have the opportunity to give the patient a more aggressive treatment. The model will also quell the fears of patients who may be anxious about the disease getting worse.


The model was tested in a cohort of 1,123 MS patients in Boston and found to be well-suited to evaluate disease course, and for the identification of predictors of a transition between the MS relapse-remitting phase of the disease and the secondary progressive phase.