New MS Testing Model Can Predict If Disease Will Intensify in Patients

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.

MS is a chronic, progressive neurodegenerative disorder that results from an attack on the central nervous system (brain, spinal cord and optical nerves) by the body’s own immune system, causing inflammation and damage to the myelin layer that covers and protects neurons resulting in motor function impairment (coordination, balance, speech and vision), irreversible neurological disability and paralysis. Most MS patients experience their first symptoms between 20 and 40 years of age and it is estimated that more than 2.3 million people in the world suffer from the disease. There is currently no cure for MS and the disease course is highly unpredictable.

Now, researchers have developed a model capable of predicting whether the disease will intensify. This method relies heavily on each patient’s history, allowing a personalized prediction for each case.

“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 every six months. In these follow-up visits, their disability level is assessed, namely through the expanded disability severity scale score and relapse state. In the newly developed 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: first, 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.

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The model, based on the information provided, will then determine the odds that MS will evolve to a milder stage, progress to a more aggressive stage, or maintain its stage for the next six months until the next follow-up visit.

“If the model suggests you are likely to be in a more debilitated state six months from now, your doctor might recommend a more rigorous treatment regime,” explained Dr. Engler.

This model is also helpful in reducing fears in MS patients. Alison Wadsworth received an MS diagnosis 25 years ago and has continued a very active life. According to her, the information she received from literature after the diagnosis reflected very negative future perspectives. “I would have loved knowing that there are many of us that manage to lead an almost-normal life with diet and exercise and lifestyle changes rather than becoming dependent on medicine that is very expensive,” said Wadsworth.

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.

“This is important because currently, the majority of MS treatments are effective in preventing new relapses, however to date, most of these therapies have shown little impact on overall disease progression,” noted the study’s co-author Dr. Tanuja Chitnis. “This tool may help to identify new treatments which improve overall disability measures.”

Dr. Engler and the study’s co-author Dr. Brian Healy have published several studies on MS. “As with anyone publishing medical literature, you hope that what you find makes a difference out there,” concluded Dr. Engler. “Every day, there are new medical findings, and you hope that proven methods are implemented.”