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.
Abstract With the aid of a Bayesian statistical model of the natural course of relapsing remitting Multiple Sclerosis (MS), we identify short-term clinical predictors of long-term evolution of the disease, with particular focus on predicting onset of secondary progressive course (failure event) on the basis of patient information available at an early stage of disease. The model specifies the full joint probability distribution for a set of variables including early indicator variables (observed during the early stage of disease), intermediate indicator variables (observed throughout the course of disease, prefailure) and the time to failure. Our model treats the intermediate indicators as a surrogate response event, so that in right-censored patients, these indicators provide supplementary information pointing towards the unobserved failure times. Moreover, the full probability modelling approach allows the considerable uncertainty which affects certain early indicators, such as the early relapse rates, to be incorporated in the analysis. With such a model, the ability of early indicators to predict failure can be assessed more accurately and reliably, and explained in terms of the relationship between early and intermediate indicators. Moreover, a model with the aforementioned features allows us to characterize the pattern of disease course in high-risk patients, and to identify short-term manifestations which are strongly related to long-term evolution of disease, as potential surrogate responses in clinical trials. Our analysis is based on longitudinal data from 186 MS patients with a relapsing–remitting initial course. The following important early predictors of the time to progression emerged: age; number of neurological functional systems (FSs) involved; sphincter, or motor, or motor-sensory symptoms; presence of sequelae after onset. During the first 3 years of follow up, to reach EDSS≥4 outside relapse, to have sphincter or motor relapses and to reach moderate pyramidal involvement were also found to be unfavourable prognostic factors. Background: The Expanded Disability Status Scale (EDSS) is widely used to rate multiple sclerosis (MS) disability, but lack of disease duration information limits utility in assessing severity. EDSS ranking at specific disease durations was used to devise the MS Severity Score, which is gaining popularity for predicting outcomes. As this requires validation in longitudinal cohorts, we aimed to assess the utility of EDSS ranking as a predictor of 5-year outcome in the MSBase Registry. Methods: Rank stability of EDSS over time was examined in the MSBase Registry, a large multicentre MS cohort. Scores were ranked for 5-year intervals, and correlation of rank across intervals was assessed using Spearman's rank correlation. EDSS progression outcomes at 10 years were disaggregated by 5-year EDSS scores. Results: Correlation coefficients for EDSS rank over 5-year intervals increased with MS duration: years 1-6=0.55, years 4-9=0.74, years 7-12=0.80 and years 10-15=0.83. EDSS progression risk at 10 years after onset was highly dependent on EDSS at 5 years; one-point progression risk was greater for EDSS score of >2 than ≤2. Two-point progression was uncommon for EDSS score of <2 and more common at EDSS score of 4. Conclusions: EDSS rank stability increases with disease duration, probably due to reduced relapses and less random variation in later disease. After 4 years duration, EDSS rank was highly predictive of EDSS rank 5 years later. Risk of progression by 10 years was highly dependent on EDSS score at 5 years duration. We confirm the utility of EDSS ranking to predict 5-year outcome in individuals 4 years after disease onset. Aim: We propose a simple tool for early prediction of unfavorable long-term evolution of multiple sclerosis (MS). Methods: A Bayesian model allowed us to calculate, within the first year of disease and for each patient, the Bayesian Risk Estimate for MS (BREMS) score that represents the risk of reaching secondary progression (SP). Results: The median BREMS were higher in 158 patients who reached SP within 10 years in comparison with 1087 progression-free patients (0.69 vs. 0.30, p<0.0001). BREMS value was related to SP-risk in the whole cohort (p<0.0001) and in the subgroup of 535 patients who had never been treated with immune therapies, thus fairly representing the natural history of disease (p<0.000001). Conclusions: BREMS can be useful both to identify the patients who are candidates or not for early or for more aggressive therapies, and to improve the design and the analysis of clinical therapeutic trials and of observational studies.