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ObjectiveTo explore the value of machine learning methods for predicting multiple sclerosis disease course.Methods1693 CLIMB study patients were classified as increased EDSS≥1.5 (worsening) or not (non-worsening) at up to five years after baseline visit. Support vector machines (SVM) were used to build the classifier, and compared to logistic regression (LR) using demographic, clinical and MRI data obtained at years one and two to predict EDSS at five years follow-up.ResultsBaseline data alone provided little predictive value. Clinical observation for one year improved overall SVM sensitivity to 62% and specificity to 65% in predicting worsening cases. The addition of one year MRI data improved sensitivity to 71% and specificity to 68%. Use of non-uniform misclassification costs in the SVM model, weighting towards increased sensitivity, improved predictions (up to 86%). Sensitivity, specificity, and overall accuracy improved minimally with additional follow-up data. Predictions improved within specific groups defined by baseline EDSS. LR performed more poorly than SVM in most cases. Race, family history of MS, and brain parenchymal fraction, ranked highly as predictors of the non-worsening group. Brain T2 lesion volume ranked highly as predictive of the worsening group.InterpretationSVM incorporating short-term clinical and brain MRI data, class imbalance corrective measures, and classification costs may be a promising means to predict MS disease course, and for selection of patients suitable for more aggressive treatment regimens.

Abstract:The Canadian Multiple Sclerosis Working Group has updated its treatment optimization recommendations (TORs) on the optimal use of disease-modifying therapies for patients with all forms of multiple sclerosis (MS). Recommendations provide guidance on initiating effective treatment early in the course of disease, monitoring response to therapy, and modifying or switching therapies to optimize disease control. The current TORs also address the treatment of pediatric MS, progressive MS and the identification and treatment of aggressive forms of the disease. Newer therapies offer improved efficacy, but also have potential safety concerns that must be adequately balanced, notably when treatment sequencing is considered. There are added discussions regarding the management of pregnancy, the future potential of biomarkers and consideration as to when it may be prudent to stop therapy. These TORs are meant to be used and interpreted by all neurologists with a special interest in the management of MS.

Neuromyelitis optica spectrum disorder (NMOSD) is a relapsing, inflammatory disease of the central nervous system marked by relapses often associated with poor recovery and long-term disability. Magnetic resonance imaging (MRI) is recognized as an important tool for timely diagnosis of NMOSD as, in combination with serologic testing, it aids in distinguishing NMOSD from possible mimics. Although the role of MRI for disease monitoring after diagnosis is not as well established, MRI may provide important prognostic information and help differentiate between relapses and pseudorelapses. Increasing evidence of subclinical disease activity and the emergence of newly approved, highly effective immunotherapies for NMOSD adjure us to re-evaluate MRI as a tool to guide optimal treatment selection and escalation throughout the disease course. In this article we review the role of MRI in NMOSD diagnosis, prognostication, disease monitoring, and treatment selection.

In this large, population-based MS cohort, we found stable incidence and increasing prevalence of MS; the latter largely reflected declining mortality. A spike in incidence in 2010 among younger patients and men at a time of widespread media coverage of MS suggests that these groups may be vulnerable to delayed diagnosis. We did not find a latitudinal gradient; however, most Ontarians live between the 42nd and 46th parallels, reducing our ability to detect an effect of latitude.

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This work employed an observational study design where treatment allocation by JCV serology allowed for treatment groups with well-balanced characteristics.