A web-based tool for personalized prediction of long-term disease course in patients with multiple sclerosis.
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ABSTRACT: Background and purpose
The Evidence-Based Decision Support Tool in Multiple Sclerosis (EBDiMS) is the first web-based prognostic calculator in multiple sclerosis (MS) capable of delivering individualized estimates of disease progression. It has recently been extended to provide long-term predictions based on the data from a large natural history cohort.Methods
We compared the predictive accuracy and consistency of EBDiMS with that of 17 neurologists highly specialized in MS.Results
We show that whilst the predictive accuracy was similar, neurologists showed a significant intra-rater and inter-rater variability.Conclusions
Because EBDiMS was consistent, it is of superior utility in a specialist setting. Further field testing of EBDiMS in non-specialist settings, and investigation of its usefulness for counselling patients in treatment decisions, is warranted.
SUBMITTER: Galea I
PROVIDER: S-EPMC4491319 | biostudies-literature | 2013 Jul
REPOSITORIES: biostudies-literature
Publications
A web-based tool for personalized prediction of long-term disease course in patients with multiple sclerosis.
Galea I I Lederer C C Neuhaus A A Muraro P A PA Scalfari A A Koch-Henriksen N N Heesen C C Koepke S S Stellmann P P Albrecht H H Winkelmann A A Weber F F Bahn E E Hauser M M Edan G G Ebers G G Daumer M M
European journal of neurology 20121207 7
<h4>Background and purpose</h4>The Evidence-Based Decision Support Tool in Multiple Sclerosis (EBDiMS) is the first web-based prognostic calculator in multiple sclerosis (MS) capable of delivering individualized estimates of disease progression. It has recently been extended to provide long-term predictions based on the data from a large natural history cohort.<h4>Methods</h4>We compared the predictive accuracy and consistency of EBDiMS with that of 17 neurologists highly specialized in MS.<h4>R ...[more]
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Project description:OBJECTIVE:Evidence for a role of microglia in the pathogenesis of multiple sclerosis (MS) is growing. We investigated association of microglial markers at time of diagnostic lumbar puncture (LP) with different aspects of disease activity (relapses, disability, magnetic resonance imaging parameters) up to 6?years later in a cohort of 143 patients. METHODS:In cerebrospinal fluid (CSF), we measured 3 macrophage and microglia-related proteins, chitotriosidase (CHIT1), chitinase-3-like protein 1 (CHI3L1 or YKL-40), and soluble triggering receptor expressed on myeloid cells 2 (sTREM2), as well as a marker of neuronal damage, neurofilament light chain (NfL), using enzyme-linked immunosorbent assay and electrochemiluminescence. We investigated the same microglia-related markers in publicly available RNA expression data from postmortem brain tissue. RESULTS:CHIT1 levels at diagnostic LP correlated with 2 aspects of long-term disease activity after correction for multiple testing. First, CHIT1 increased with reduced tissue integrity in lesions at a median 3?years later (p = 9.6E-04). Second, CHIT1 reflected disease severity at a median 5?years later (p = 1.2E-04). Together with known clinical covariates, CHIT1 levels explained 12% and 27% of variance in these 2 measures, respectively, and were able to distinguish slow and fast disability progression (area under the curve = 85%). CHIT1 was the best discriminator of chronic active versus chronic inactive lesions and the only marker correlated with NfL (r = 0.3, p = 0.0019). Associations with disease activity were, however, independent of NfL. INTERPRETATION:CHIT1 CSF levels measured during the diagnostic LP reflect microglial activation early on in MS and can be considered a valuable prognostic biomarker for future disease activity. ANN NEUROL 2020;87:633-645.
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Project description:<p>Progressive multiple sclerosis (PMS) is currently diagnosed retrospectively. Here, we work towards a set of biomarkers that could assist in early diagnosis of PMS. A selection of cerebrospinal fluid metabolites (n=15) was shown to differentiate between PMS and its preceding phenotype in an independent cohort (AUC=0.93). Complementing the classifier with conformal prediction showed that highly confident predictions could be made, and that three out of eight patients developing PMS within three years of sample collection were predicted as PMS at that time point. Finally, this methodology was applied to PMS patients part of a clinical trial for intrathecal treatment with rituximab. The methodology showed that 68% of the patients decreased their similarity to the PMS phenotype one year after treatment. In conclusion, the inclusion of confidence predictors contributes with more information compared to traditional machine learning, and this information is relevant for disease monitoring.</p><p><br></p><p><strong>Cohort 1 assays</strong> are reported in the current study <a href='https://www.ebi.ac.uk/metabolights/MTBLS1464' rel='noopener noreferrer' target='_blank'><strong>MTBLS1464</strong></a>.</p><p><strong>Cohort 2 assays</strong> are reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS558' rel='noopener noreferrer' target='_blank'><strong>MTBLS558</strong></a>.</p>
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Project description:ImportanceBlood sample-based biomarkers that are associated with clinically meaningful outcomes for patients with multiple sclerosis (MS) have not been developed.ObjectiveTo evaluate the potential of serum neurofilament light chain (sNFL) measurements as a biomarker of disease activity and progression in a longitudinal MS data set.Design, setting, and participantsSingle-center, ongoing, prospective observational cohort study of 607 patients with MS from the longitudinal EPIC (Expression, Proteomics, Imaging, Clinical) study at the University of California, San Francisco from July 1, 2004, through August 31, 2017. Clinical evaluations and sample collection were performed annually for 5 years, then at different time points for up to 12 years, with a median follow-up duration of 10 (interquartile range, 7-11) years. Serum NFL levels were measured using a sensitive single molecule array platform and compared with clinical and magnetic resonance imaging variables with the use of univariable and multivariable analyses.Main outcomes and measuresThe main outcomes were disability progression defined as clinically significant worsening on the Expanded Disability Status Scale (EDSS) score and brain fraction atrophy.ResultsMean (SD) age of the 607 study participants at study entry was 42.5 (9.8) years; 423 (69.7%) were women; and all participants were of non-Hispanic European descent. Of 3911 samples sequentially collected, 3904 passed quality control for quantification of sNFL. Baseline sNFL levels showed significant associations with EDSS score (β, 1.080; 95% CI, 1.047-1.114; P < .001), MS subtype (β, 1.478; 95% CI, 1.279-1.707; P < .001), and treatment status (β, 1.120; 95% CI, 1.007-1.245; P = .04). A significant interaction between EDSS worsening and change in levels of sNFL over time was found (β, 1.015; 95% CI, 1.007-1.023; P < .001). Baseline sNFL levels alone were associated with approximately 11.6% of the variance in brain fraction atrophy at year 10. In a multivariable analysis that considered sex, age, and disease duration, baseline sNFL levels were associated with 18.0% of the variance in brain fraction atrophy at year 10. After 5 years' follow-up, active treatment was associated with lower levels of sNFL, with high-potency treatments associated with the greater decreases in sNFL levels compared with platform therapies (high-potency vs untreated: β, 0.946; 95% CI, 0.915-0.976; P < .001; high-potency vs platform: β, 0.972; 95% CI, 0.948-0.998; P = .04).Conclusions and relevanceThis study found that statistically significant associations of sNFL with relevant clinical and neuroimaging outcomes in MS were confirmed and extended, supporting the potential of sNFL as an objective surrogate of ongoing MS disease activity. In this data set of patients with MS who received early treatment, the prognostic power of sNFL for relapse activity and long-term disability progression was limited. Further prospective studies are necessary to assess the assay's utility for decision-making in individual patients.
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Project description:BackgroundMultiple sclerosis is a chronic disease of uncertain aetiology. Variations in its disease course make it difficult to impossible to accurately determine the prognosis of individual patients. The Sylvia Lawry Centre for Multiple Sclerosis Research (SLCMSR) developed an "online analytical processing (OLAP)" tool that takes advantage of extant clinical trials data and allows one to model the near term future course of this chronic disease for an individual patient.ResultsFor a given patient the most similar patients of the SLCMSR database are intelligently selected by a model-based matching algorithm integrated into an OLAP-tool to enable real time, web-based statistical analyses. The underlying database (last update April 2005) contains 1,059 patients derived from 30 placebo arms of controlled clinical trials. Demographic information on the entire database and the portion selected for comparison are displayed. The result of the statistical comparison is provided as a display of the course of Expanded Disability Status Scale (EDSS) for individuals in the database with regions of probable progression over time, along with their mean relapse rate. Kaplan-Meier curves for time to sustained progression in the EDSS and time to requirement of constant assistance to walk (EDSS 6) are also displayed. The software-application OLAP anticipates the input MS patient's course on the basis of baseline values and the known course of disease for similar patients who have been followed in clinical trials.ConclusionThis simulation could be useful for physicians, researchers and other professionals who counsel patients on therapeutic options. The application can be modified for studying the natural history of other chronic diseases, if and when similar datasets on which the OLAP operates exist.
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Project description:BACKGROUND: The aim of this study was to assess the diagnostic accuracy (sensitivity and specificity) of clinical, imaging and motor evoked potentials (MEP) for predicting the short-term prognosis of multiple sclerosis (MS). METHODS: We obtained clinical data, MRI and MEP from a prospective cohort of 51 patients and 20 matched controls followed for two years. Clinical end-points recorded were: 1) expanded disability status scale (EDSS), 2) disability progression, and 3) new relapses. We constructed computational classifiers (Bayesian, random decision-trees, simple logistic-linear regression-and neural networks) and calculated their accuracy by means of a 10-fold cross-validation method. We also validated our findings with a second cohort of 96 MS patients from a second center. RESULTS: We found that disability at baseline, grey matter volume and MEP were the variables that better correlated with clinical end-points, although their diagnostic accuracy was low. However, classifiers combining the most informative variables, namely baseline disability (EDSS), MRI lesion load and central motor conduction time (CMCT), were much more accurate in predicting future disability. Using the most informative variables (especially EDSS and CMCT) we developed a neural network (NNet) that attained a good performance for predicting the EDSS change. The predictive ability of the neural network was validated in an independent cohort obtaining similar accuracy (80%) for predicting the change in the EDSS two years later. CONCLUSIONS: The usefulness of clinical variables for predicting the course of MS on an individual basis is limited, despite being associated with the disease course. By training a NNet with the most informative variables we achieved a good accuracy for predicting short-term disability.
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Project description:Multiple sclerosis (MS) presenting in the pediatric years can lead to landmark disability levels younger in life than adult onset MS and so therefore early and effective treatment remains paramount for long-term outcomes. The goals of MS therapeutics in adults have widened to address multiple mechanisms: anti-inflammatory, neuroprotective, and myelin repair, yet the optimal paradigm for MS therapies in the pediatric population is not known. Pediatric onset MS add complexities due to the ongoing development of the central nervous system and the immune system. Clinical trials have led to an increasing number of pharmaceutical therapies for adult onset MS (AOMS), one POMS randomized controlled trial is completed and other trials are ongoing, yet due to the low prevalence of POMS, the dynamic landscape and risk management of the MS disease modifying therapies (DMT) it remains more difficult to complete trials in POMS. There is consensus that controlled clinical trials leading to appropriate and safe therapies for POMS are important for a multitude of reasons that include unique pediatric pharmacokinetics, short and long-term safety, developmental issues, clinical benefits, and regulatory approval. This review will focus on new treatment goals, paradigm, strategies, monitoring, compliance, and products in the long-term treatment of POMS. The discussion will focus on these new concepts and the published data related to DMT use in POMS. This review provides significant insight into new concepts of treatment goals and current approaches to enhance the lives of the POMS patients now and in the future.
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Project description: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.
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Project description:The rate of disability accumulation varies across multiple sclerosis (MS) patients. Machine learning techniques may offer more powerful means to predict disease course in MS patients. In our study, 724 patients from the Comprehensive Longitudinal Investigation in MS at Brigham and Women's Hospital (CLIMB study) and 400 patients from the EPIC dataset, University of California, San Francisco, were included in the analysis. The primary outcome was an increase in Expanded Disability Status Scale (EDSS) ≥ 1.5 (worsening) or not (non-worsening) at up to 5 years after the baseline visit. Classification models were built using the CLIMB dataset with patients' clinical and MRI longitudinal observations in first 2 years, and further validated using the EPIC dataset. We compared the performance of three popular machine learning algorithms (SVM, Logistic Regression, and Random Forest) and three ensemble learning approaches (XGBoost, LightGBM, and a Meta-learner L). A "threshold" was established to trade-off the performance between the two classes. Predictive features were identified and compared among different models. Machine learning models achieved 0.79 and 0.83 AUC scores for the CLIMB and EPIC datasets, respectively, shortly after disease onset. Ensemble learning methods were more effective and robust compared to standalone algorithms. Two ensemble models, XGBoost and LightGBM were superior to the other four models evaluated in our study. Of variables evaluated, EDSS, Pyramidal Function, and Ambulatory Index were the top common predictors in forecasting the MS disease course. Machine learning techniques, in particular ensemble methods offer increased accuracy for the prediction of MS disease course.
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