Literature DB >> 11535229

Predicting secondary progression in relapsing-remitting multiple sclerosis: a Bayesian analysis.

R Bergamaschi1, C Berzuini, A Romani, V Cosi.   

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> or =4 outside relapse, to have sphincter or motor relapses and to reach moderate pyramidal involvement were also found to be unfavourable prognostic factors.

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Year:  2001        PMID: 11535229     DOI: 10.1016/s0022-510x(01)00572-x

Source DB:  PubMed          Journal:  J Neurol Sci        ISSN: 0022-510X            Impact factor:   3.181


  14 in total

Review 1.  Multiple sclerosis in the elderly patient.

Authors:  Amer Awad; Olaf Stüve
Journal:  Drugs Aging       Date:  2010-04-01       Impact factor: 3.923

2.  Predicting abdominal aortic aneurysm growth using patient-oriented growth models with two-step Bayesian inference.

Authors:  Emrah Akkoyun; Sebastian T Kwon; Aybar C Acar; Whal Lee; Seungik Baek
Journal:  Comput Biol Med       Date:  2020-01-13       Impact factor: 4.589

Review 3.  Aggressive multiple sclerosis: proposed definition and treatment algorithm.

Authors:  Carolina A Rush; Heather J MacLean; Mark S Freedman
Journal:  Nat Rev Neurol       Date:  2015-06-02       Impact factor: 42.937

4.  A multifactorial prognostic index in multiple sclerosis. Cerebrospinal fluid IgM oligoclonal bands and clinical features to predict the evolution of the disease.

Authors:  Jessica Mandrioli; Patrizia Sola; Roberta Bedin; Mariaelena Gambini; Elisa Merelli
Journal:  J Neurol       Date:  2008-06-13       Impact factor: 4.849

5.  Relation between EDSS and monosymptomatic or polysymptomatic onset in clinical manifestations of multiple sclerosis in Babol, northern Iran.

Authors:  Seyed Mohammad Masood Hojjati; Seyyed Ali Hojjati; Mobina Baes; Ali Bijani
Journal:  Caspian J Intern Med       Date:  2014

6.  Comorbidity delays diagnosis and increases disability at diagnosis in MS.

Authors:  R A Marrie; R Horwitz; G Cutter; T Tyry; D Campagnolo; T Vollmer
Journal:  Neurology       Date:  2008-10-29       Impact factor: 9.910

7.  Early prediction of the long term evolution of multiple sclerosis: the Bayesian Risk Estimate for Multiple Sclerosis (BREMS) score.

Authors:  Roberto Bergamaschi; Silvana Quaglini; Maria Trojano; Maria Pia Amato; Eleonora Tavazzi; Damiano Paolicelli; Valentina Zipoli; Alfredo Romani; Aurora Fuiani; Emilio Portaccio; Carlo Berzuini; Cristina Montomoli; Stefano Bastianello; Vittorio Cosi
Journal:  J Neurol Neurosurg Psychiatry       Date:  2007-01-12       Impact factor: 10.154

8.  Computational classifiers for predicting the short-term course of Multiple sclerosis.

Authors:  Bartolome Bejarano; Mariangela Bianco; Dolores Gonzalez-Moron; Jorge Sepulcre; Joaquin Goñi; Juan Arcocha; Oscar Soto; Ubaldo Del Carro; Giancarlo Comi; Letizia Leocani; Pablo Villoslada
Journal:  BMC Neurol       Date:  2011-06-07       Impact factor: 2.474

9.  Prediction of acute multiple sclerosis relapses by transcription levels of peripheral blood cells.

Authors:  Michael Gurevich; Tamir Tuller; Udi Rubinstein; Rotem Or-Bach; Anat Achiron
Journal:  BMC Med Genomics       Date:  2009-07-22       Impact factor: 3.063

10.  Outcome of beta-interferon treatment in relapsing-remitting multiple sclerosis: a Bayesian analysis.

Authors:  Killian O'Rourke; Cathal Walsh; Michael Hutchinson
Journal:  J Neurol       Date:  2007-08-14       Impact factor: 6.682

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