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Prognosis in multiple sclerosis : the predictors and prediction of specific functional impairments

University of British Columbia

The level of future impairment is difficult to predict with multiple sclerosis (MS). Previous MS prognosis studies have mainly examined mortality or overall impairment and have used limited analytic methods. The primary objectives of this study were to identify the predictors of later functional impairment and to determine how predictable these impairments are. A secondary objective was to compare prognosis modelling techniques. A retrospective cohort study was conducted using patient data from the Vancouver MS clinic (Canada). Some analyses used the level of impairment after 10-15 years of MS as an outcome and involved binary and ordinal logistic regression (BLR, OLR) modelling. Other analyses used the time from onset to a specific level of overall impairment (DSS 6) as the outcome and involved proportional hazards PH) modelling. Candidate predictors were demographic (e.g., onset age) or clinical (e.g., onset symptoms) in form. Model performance was assessed using both cross-validation and data from a Dutch clinic in Groningen. Indicators of performance included receiver-operator characteristic (ROC) curve analysis, Hosmer-Lemeshow chi-square analysis and Somers’ Dxy correlation analysis. Most of the previously recognized predictors were predictive of motor impairment. However, the predictive value of patient characteristics often depended on the type of impairment being predicted. Specific impairment types varied in their predictability. Moreover, differences in the distribution of impairment frequently resulted in underestimates of risk in the Dutch cohort. In terms of the patient characteristics identified as predictive, there was generally good agreement amongst the different models. OLR models performed as well as BLR models. While various predictors exist for all impairment types, the predictability of these impairments is low or moderate. In terms of patient counselling, knowledge of predictors can be communicated to patients about their prognosis. However, patients should also be informed that improved knowledge about predictors of impairments does not translate into high predictability of future function. Regarding the use of predictions in a patient care setting, only terms such as “low risk” or “high risk” should be used. Improvements to the modelling strategy as well as a better understanding of the underlying disease process should help to improve predictability.

Thesis/Dissertation

application/pdf

2009-04-27

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http://hdl.handle.net/2429/7598

Health Care and Epidemiology

University of British Columbia

UBCV

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