A Binning Approach for Predicting Long-Term Prognosis in Multiple Sclerosis

Abstract

Multiple sclerosis is a complex disease with a highly heterogeneous disease course. Early treatment of multiple sclerosis patients could delay or even prevent disease worsening, but selecting the right treatment is difficult due to the heterogeneity. To alleviate this decision-making process, predictions of the long-term prognosis of the individual patient are of interest (especially at diagnosis, when not much is known yet). However, most prognosis studies for multiple sclerosis currently focus on a short-term binary endpoint, answering questions like "will the patient significantly progress in 2 years". In this paper, we present a novel approach that provides a comprehensive perspective on the long-term prognosis of the individual patient, by dividing the years after diagnosis up into bins and predicting the level of disability in each of these bins. Our approach addresses several general issues in observational datasets, such as sporadic measurements at irregular time-intervals, widely varying lengths of follow-up, and unequal number of measurements even for the same follow-up. We evaluated our approach on real-world clinical data from an observational single-center cohort of multiple sclerosis patients in Belgium. On this dataset, a regressor chain of random forests achieved a Pearson correlation of 0.72 between its cross-validated test set predictions and the actual disability measurements assessed by a clinician.