Accounting for disease modifying therapy in models of clinical progression in multiple sclerosis

Healy, Brian C.; Engler, David; Gholipour, Taha; Weiner, Howard L.; Bakshi, Rohit; Chitnis, Tanuja

doi:10.1016/j.jns.2010.12.024

Cited by 7 publications

(8 citation statements)

References 18 publications

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“…Past CIS and RRMS studies have reported that older age at onset is a poor prognostic indicator. 18 , 20 – 23 Consistent with these studies, we found that older age at CIS diagnosis was an independent predictor of both 3- and 12-month sustained disability worsening events. Our analysis shows no independent adverse effect of male sex, consistent with previous studies.…”

Section: Discussionsupporting

confidence: 87%

“…The effect of pyramidal system dysfunction is consistent with previous reports from RRMS cohorts that have identified initial motor symptoms as a poor prognostic indicator. 17 , 19 , 20 , 23 Our results extend previous analyses 27 by demonstrating that, ARR is independently predictive of 3-month individual worsening events in early MS. However, this relationship between ARR and disability worsening requires further examination, and will be interrogated further as our data set matures.…”

Section: Discussionsupporting

confidence: 83%

“…Our analysis shows no independent adverse effect of male sex, consistent with previous studies. 18 , 20 , 22 , 23 …”

Section: Discussionmentioning

confidence: 99%

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Predictors of disability worsening in clinically isolated syndrome

Jokubaitis

Spelman

Kalinčík

et al. 2015

Ann Clin Transl Neurol

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ObjectiveTo assess demographic, clinical, magnetic resonance imaging, and treatment exposure predictors of time to 3 or 12-month confirmed disability worsening in clinically isolated syndrome (CIS) and early multiple sclerosis (MS).MethodsWe utilized the MSBase Incident Study (MSBasis), a prospective cohort study of outcome after CIS. Predictors of time to first 3 and 12-month confirmed expanded disability status scale worsening were analyzed using Cox proportional hazards regression.ResultsAbout 1989 patients were analyzed, the largest seen-from-onset cohort reported to-date. A total of 391 patients had a first 3-month confirmed disability worsening event, of which 307 were sustained for 12 months. Older age at CIS onset (adjusted hazard ratio: aHR 1.17, 95% 1.06, 1.30), pyramidal (aHR 1.45, 95% CI 1.13, 1.89) and ambulation (HR 1.60, 95% CI 1.09, 2.34) system dysfunction, annualized relapse rate (aHR 1.20, 95% CI 1.18, 1.22), and lower proportion of observation time on treatment were associated with 3-month confirmed worsening. Predictors of time to 12-month sustained worsening included pyramidal system dysfunction (Hazard ratio: aHR 1.38, 95% CI 1.05, 1.83), and older age at CIS onset (aHR 1.17, 95% CI 1.04, 1.31). Greater proportion of follow-up time exposed to treatment was associated with greater reductions in the rate of worsening.InterpretationThis study provides class IV evidence for a strong protective effect of disease-modifying treatment to reduce disability worsening events in patients with CIS and early MS, and confirms age and pyramidal dysfunction at onset as risk factors.

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Section: Discussionsupporting

confidence: 87%

Section: Discussionsupporting

confidence: 83%

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Predictors of disability worsening in clinically isolated syndrome

Jokubaitis

Spelman

Kalinčík

et al. 2015

Ann Clin Transl Neurol

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“…This lack of understanding is reflected in the current treatments for MS, most of which target only symptoms or are administered together with global immuno-suppressants, which can have serious adverse side effects. Although immunoregulatory drugs that specifically target immune cells have been developed [10], they reduce the number of exacerbations only in a small proportion of patients and are beneficial only in relapsing–remitting forms of MS [11, 12]. Therefore, new therapies that target specific pathways involved in MS pathogenesis are needed.…”

Section: Introductionmentioning

confidence: 99%

Tyrosine Kinase Inhibitors Ameliorate Autoimmune Encephalomyelitis in a Mouse Model of Multiple Sclerosis

et al. 2011

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Multiple sclerosis is an autoimmune disease of the central nervous system characterized by neuroinflammation and demyelination. Although considered a T cell-mediated disease, multiple sclerosis involves the activation of both adaptive and innate immune cells, as well as resident cells of the central nervous system, which synergize in inducing inflammation and thereby demyelination. Differentiation, survival, and inflammatory functions of innate immune cells and of astrocytes of the central nervous system are regulated by tyrosine kinases. Here, we show that imatinib, sorafenib, and GW2580—small molecule tyrosine kinase inhibitors can each—prevent the development of disease and treat established disease in a mouse model of multiple sclerosis. In vitro, imatinib and sorafenib inhibited astrocyte proliferation mediated by the tyrosine kinase platelet-derived growth factor receptor (PDGFR), whereas GW2580 and sorafenib inhibited macrophage tumor necrosis factor (TNF) production mediated by the tyrosine kinases c-Fms and PDGFR, respectively. In vivo, amelioration of disease by GW2580 was associated with a reduction in the proportion of macrophages and T cells in the CNS infiltrate, as well as a reduction in the levels of circulating TNF. Our findings suggest that GW2580 and the FDA-approved drugs imatinib and sorafenib have potential as novel therapeutics for the treatment of autoimmune demyelinating disease.

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“…The specific goal is to predict whether the patients' accumulated disability measured by their EDSS scores (explained below) will increase by at least two at the five year mark using information from the first two years of clinical visits. Earlier research reveals that classifying MS patients based on their data from an initial visit performs only slightly better than random [8] and thus doctors need to monitor patients for a short time period to accumulate sufficient data to make an accurate judgment.…”

Section: Challenges In Predicting Disease Outcomementioning

confidence: 99%

Addressing Human Subjectivity via Transfer Learning: An Application to Predicting Disease Outcome in Multiple Sclerosis Patients

Zhao

Brodley

Chitnis

et al. 2014

Proceedings of the 2014 SIAM International Conference on Data Mining

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Predicting disease course is critical in chronic progressive diseases such as multiple sclerosis (MS). In our work we are applying machine learning methods to longitudinal records of MS patients to build a classifier that predicts whether a patient will have a significant increase in disability at the five year mark using information from the first two years of clinical visits. This prediction is key for choosing among the available treatments as some have more troubling side-effect profiles. Two challenges arise while learning with this data. First, patient data involves the physician's (possibly subjective) evaluation. Because a patient's data may come from one doctor on the first visit and different doctors on subsequent visits, it can be difficult to form an accurate predictor of disease outcome. In particular, some physicians may be biased in one direction, scoring each patient as more severe than would other physicians, while others may be biased in the opposite direction. Another challenge is that it is much easier to classify the cases with low future disability compared to cases with high future disability at early stages of the disease. This asymmetric property is due to the nature of the disease rather than class imbalance. In this paper we introduce a new transfer learning approach to handle these challenges. The algorithm builds a single SVM classifier for each doctor by dividing the entire dataset into primary (instances from the doctor) and auxiliary (instances from other doctors) sets. When applied to our dataset of MS patients our new approach is able to realize a significant increase in prediction performance over approaches that form a single SVM classifier for the entire dataset or for the physician's dataset alone.

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Accounting for disease modifying therapy in models of clinical progression in multiple sclerosis

Cited by 7 publications

References 18 publications

Predictors of disability worsening in clinically isolated syndrome

Predictors of disability worsening in clinically isolated syndrome

Tyrosine Kinase Inhibitors Ameliorate Autoimmune Encephalomyelitis in a Mouse Model of Multiple Sclerosis

Addressing Human Subjectivity via Transfer Learning: An Application to Predicting Disease Outcome in Multiple Sclerosis Patients

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