title: Mathematical models and individualized outcome estimates in multiple sclerosis.
abstract: There is an urgent need for individualized outcome estimates in multiple sclerosis (MS). This is a prerequisite for selecting appropriate therapies in a disease the outcome of which may vary between malignant and benign forms. This question was addressed by using probabilistic mathematical models. The disease course is described by movements of the patient's condition through well-defined disease states. The data used are based upon 278 reports of definite and probable MS cases collected over a 20-year period (1957-1976) at l'Hôpital Neurologique de Lyon. A Markov model was first prepared. However, it only provided an overall presentation of the disease which was not very meaningful for the individual patient, and limited studies of potential prognostic variables to dichotomous variables and univariate analyses. A stochastic survival model was then elaborated. It was complex at the theoretical level but in practice, easy to use. Personalized prognosis of a patient could be ascertained by combining several single potential prognostic variables and using their actual value in the case of quantitative variables. Although these models still need to be refined and have to be validated from the present state of the patients and other patient cohorts, they provide for the first time a global description of disease course likelihood and prognosis. The survival model may be adapted to each patient.
authors.affiliation: Clinique de Neurologie, Hôpital Neurologique, Lyon, France.
authors.forename: C
authors.initials: C
authors.lastname: Confavreux
authors.forename: C
authors.initials: C
authors.lastname: Wolfson
mesh_terms: D000367:Age Factors; D005260:Female; D006801:Humans; D008297:Male; D008390:Markov Chains; D008962:Models, Theoretical; D009103:Multiple Sclerosis / Q000401:mortality*; D011336:Probability; D011379:Prognosis; D012737:Sex Factors; D013223:Statistics as Topic; D013269:Stochastic Processes; D016019:Survival Analysis