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.
Limited research has been done with regards to predicting the risk of developing MS. One work predicted MS in patients with mono symptomatic optic neuritis using MRI examination findings, oligoclonal bands in cerebrospinal fluid (CSF), immunoglobulin (Ig) G index, and the seasonal time of onset @cite_0 . Thrower @cite_25 suggested the use of clinical characteristics of optic neuritis and traverse myelitis to identify high-risk MS patients. More recently, De Jager et. al @cite_4 proposed a weighted genetic risk score (wGRS) based on genetic susceptibility loci in the context of environmental risk factors. However, prior research relies on specialized measurements that are performed to confirm a MS diagnosis. The approaches suggested do not generalize to all patients and fail to allow for early diagnosis and intervention of high-risk MS patients. @PARASPLIT
@cite_0 @cite_4 @cite_25
2146020893 2029975686 2126496021 2046451999
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. Background Individual magnetic resonance imaging (MRI) disease severity measures, such as atrophy or lesions, show weak relationships to clinical status in patients with multiple sclerosis (MS). Objective To combine MS-MRI measures of disease severity into a composite score. Design Retrospective analysis of prospectively collected data. Setting Community-based and referral subspecialty clinic in an academic hospital. Patients A total of 103 patients with MS, with a mean (SD) Expanded Disability Status Scale (EDSS) score of 3.3 (2.2), of whom 62 (60.2 ) had the relapsing-remitting, 33 (32.0 ) the secondary progressive, and 8 (7.8 ) the primary progressive form. Main Outcome Measures Brain MRI measures included baseline T2 hyperintense (T2LV) and T1 hypointense (T1LV) lesion volume and brain parenchymal fraction (BPF), a marker of global atrophy. The ratio of T1LV to T2LV (T1:T2) assessed lesion severity. A Magnetic Resonance Disease Severity Scale (MRDSS) score, on a continuous scale from 0 to 10, was derived for each patient using T2LV, BPF, and T1:T2. Results The MRDSS score averaged 5.1 (SD, 2.6). Baseline MRI and EDSS correlations were moderate for BPF, T1:T2, and MRDSS and weak for T2LV. The MRDSS showed a larger effect size than the individual MRI components in distinguishing patients with the relapsing-remitting form from those with the secondary progressive form. Models containing either T2LV or MRDSS were significantly associated with disability progression during the mean (SD) 3.2 (0.3)–year observation period, when adjusting for baseline EDSS score. Conclusion Combining brain MRI lesion and atrophy measures can predict MS clinical progression and provides the basis for developing an MRI-based continuous scale as a marker of MS disease severity. As discussed in Part I of this review, the geographic distribution of multiple sclerosis (MS) and the change in risk among migrants provide compelling evidence for the existence of strong environmental determinants of MS, where “environmental” is broadly defined to include differences in diet and other behaviors. As we did for infections, we focus here primarily on those factors that may contribute to explain the geographic variations in MS prevalence and the change in risk among migrants. Among these, sunlight exposure emerges as being the most likely candidate. Because the effects of sun exposure may be mediated by vitamin D, we also examine the evidence linking vitamin D intake or status to MS risk. Furthermore, we review the evidence on cigarette smoking, which cannot explain the geographic variations in MS risk, but may contribute to the recently reported increases in the female male ratio in MS incidence. Other proposed risk factors for MS are mentioned only briefly; although we recognize that some of these might be genuine, evidence is usually sparse and unpersuasive. Ann Neurol 2007