Multiple sclerosis (MS) is a chronic autoimmune disease that affects the central nervous system. The progression and severity of MS varies by individual, but it is generally a disabling disease. Although medications have been developed to slow the disease progression and help manage symptoms, MS research has yet to result in a cure. Early diagnosis and treatment of the disease have been shown to be effective at slowing the development of disabilities. However, early MS diagnosis is difficult because symptoms are intermittent and shared with other diseases. Thus most previous works have focused on uncovering the risk factors associated with MS and predicting the progression of disease after a diagnosis rather than disease prediction. This paper investigates the use of data available in electronic medical records (EMRs) to create a risk prediction model; thereby helping clinicians perform the difficult task of diagnosing an MS patient. Our results demonstrate that even given a limited time window of patient data, one can achieve reasonable classification with an area under the receiver operating characteristic curve of 0.724. By restricting our features to common EMR components, the developed models also generalize to other healthcare systems.
Using multivariate analyses, individual risk of clinically definite multiple sclerosis (C DMS) after monosymptomatic optic neuritis (MO N) was quantified in a prospective study with clinical MO N onset during 1990 -95 in Stockholm, Sweden. During a mean follow-up time of 3.8 years, the presence of MS-like brain magnetic resonance imaging (MRI) lesions and oligoclonal immunoglobulin (Ig) G bands in cerebrospinal fluid (CSF) were strong prognostic markers of C DMS, with relative hazard ratios of 4.68 95 confidence interval (CI) 2.21 -9.91 and 5.39 (95 C I 1.56 -18.61), respectively. Age and season of clinical onset were also significant predictors, with relative hazard ratios of 1.76 (95 C I 1.02 -3.04) and 2.21 (95 C I 1.13 -3.98), respectively. Based on the above two strong predicto rs, individual probability of C DMS development after MO N was calculated in a three-quarter sample drawn from a cohort, with completion of follow-up at three years. The highest probability, 0.66 (95 C I 0.48 -0.80), wa...  Multiple sclerosis (MS) represents a spectrum of demyelination that depends on disease duration and clinical categorization. Most patients present with the relapsing–remitting form of the disease. The earliest clinical presentation of relapsing–remitting MS (RRMS) is the clinically isolated syndrome (CIS). Predicting which CIS patients are at high risk for MS is complicated by the disparity between clinical attacks and the extent of axon pathology. However, recent interferon-beta (IFN-β) trials have demonstrated a delay in time to the second demyelinating event with early treatment, and early treatment could also slow the progression from RRMS to secondary-progressive MS (SPMS). Clinical findings in combination with brain MRI and CSF analysis can be used in CIS patients to evaluate their risk for clinically definite MS (CDMS). Application of the McDonald criteria also allows an earlier MS diagnosis by using new MRI lesions to define dissemination in time. Early immunomodulatory therapy for selected CIS patients may eventually prevent future axon pathology and progression of disability in this lifelong disease.