PREDICTION OF OUTCOME IN MULTIPLE-SCLEROSIS BASED ON MULTIVARIATE MODELS

An incidence cohort of 308 multiple sclerosis patients was followed up repeatedly during at least 25 years of disease. In the patients with acute onset, multivariate survival analyses were performed and predict ive models created. The endpoints DSS 6 and start of progressive disea se were used. A number of variables were tested. The most important of these for prediction and therefore included in these models were: age at onset, sex, degree of remission after relapse, mono- or polyregion al symptoms, type of affected nerve fibres, number of affected neurolo gical systems. The relapse rate did not correlate with prognosis. In t he predictive models, coefficients and risk ratios are provided that c an be used for calculating the risk of progression and DSS 6 or to pre dict the median time for these endpoints in individual patients. It wa s also found that the risk of progression is not constant, but has a m aximum a certain time after disease onset. For a patient with early on set, the risk is low in the beginning, but reaches a maximum level, wh ich is several times higher, after about 15 years. The patient with a late onset has a much higher risk of endpoint immediately after onset, but reaches the maximum in a few years, and after that the risk decre ases