Dataset Information

Predicting relapsing-remitting dynamics in multiple sclerosis using discrete distribution models: a population approach.

ABSTRACT: Relapsing-remitting dynamics are a hallmark of autoimmune diseases such as Multiple Sclerosis (MS). A clinical relapse in MS reflects an acute focal inflammatory event in the central nervous system that affects signal conduction by damaging myelinated axons. Those events are evident in T1-weighted post-contrast magnetic resonance imaging (MRI) as contrast enhancing lesions (CEL). CEL dynamics are considered unpredictable and are characterized by high intra- and inter-patient variability. Here, a population approach (nonlinear mixed-effects models) was applied to analyse of CEL progression, aiming to propose a model that adequately captures CEL dynamics.We explored several discrete distribution models to CEL counts observed in nine MS patients undergoing a monthly MRI for 48 months. All patients were enrolled in the study free of immunosuppressive drugs, except for intravenous methylprednisolone or oral prednisone taper for a clinical relapse. Analyses were performed with the nonlinear mixed-effect modelling software NONMEM 7.2. Although several models were able to adequately characterize the observed CEL dynamics, the negative binomial distribution model had the best predictive ability. Significant improvements in fitting were observed when the CEL counts from previous months were incorporated to predict the current month's CEL count. The predictive capacity of the model was validated using a second cohort of fourteen patients who underwent monthly MRIs during 6-months. This analysis also identified and quantified the effect of steroids for the relapse treatment.The model was able to characterize the observed relapsing-remitting CEL dynamic and to quantify the inter-patient variability. Moreover, the nature of the effect of steroid treatment suggested that this therapy helps resolve older CELs yet does not affect newly appearing active lesions in that month. This model could be used for design of future longitudinal studies and clinical trials, as well as for the evaluation of new therapies.

SUBMITTER: Velez de Mendizabal N

PROVIDER: S-EPMC3764125 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predicting relapsing-remitting dynamics in multiple sclerosis using discrete distribution models: a population approach.

Velez de Mendizabal Nieves N Hutmacher Matthew M MM Troconiz Iñaki F IF Goñi Joaquín J Villoslada Pablo P Bagnato Francesca F Bies Robert R RR

PloS one 20130905 9

<h4>Background</h4>Relapsing-remitting dynamics are a hallmark of autoimmune diseases such as Multiple Sclerosis (MS). A clinical relapse in MS reflects an acute focal inflammatory event in the central nervous system that affects signal conduction by damaging myelinated axons. Those events are evident in T1-weighted post-contrast magnetic resonance imaging (MRI) as contrast enhancing lesions (CEL). CEL dynamics are considered unpredictable and are characterized by high intra- and inter-patient v ...[more]

PMID: 24039924

Similar Datasets

Project description:Multiple sclerosis (MS) is a chronic demyelinating disease of the central nervous system and mainly affects young adults. Its natural history has changed in recent years with the advent of disease-modifying drugs, which have been available since the early 1990s. The increasing number of first-line and second-line treatment options, together with the variable course of the disease and patient lifestyles and expectations, makes the therapeutic decision a real challenge. The aim of this review is to give a comprehensive overview of the main present and some future drugs for relapsing-remitting MS, including risk-benefit considerations, to enable readers to draw their own conclusions regarding the risk-benefit assessment of personalized treatment strategies, taking into account not only treatment-related but also disease-related risks. We performed a Medline literature search to identify studies on the treatment of MS with risk stratification and risk-benefit considerations. We focused our attention on studies of disease-modifying, immunomodulating, and immunosuppressive drugs, including monoclonal antibodies. Here we offer personal considerations, stemming from long-term experience in the treatment of MS and thorough discussions with other neurologists closely involved in the care of patients with the disease. MS specialists need to know not only the specific risks and benefits of single drugs, but also about drug interactions, either in simultaneous or serial combination therapy, and patient comorbidities, preferences, and fears. This has to be put into perspective, considering also the risks of untreated disease in patients with different clinical and radiological characteristics. There is no single best treatment strategy, but therapy has to be tailored to the patient. This is a time-consuming task, rich in complexity, and influenced by the attitude towards risk on the parts of both the patient and the clinical team. The broader the MS drug market becomes, the harder it will be for the clinician to help the patient decide which therapeutic strategy to opt for.

Dataset Information

Predicting relapsing-remitting dynamics in multiple sclerosis using discrete distribution models: a population approach.

Publications

Predicting relapsing-remitting dynamics in multiple sclerosis using discrete distribution models: a population approach.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets