#ECTRIMS2016 – Modeling Approach Able to Identify Likely Disease Trajectory in Progressive MS Patients

Patricia Inacio, PhD avatar

by Patricia Inacio, PhD |

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disease trajectory and MS

Long-term observations together with mathematical modeling present a way of predicting the likely disability trajectory of multiple sclerosis (MS) patients.

The approach was outlined in a presentation, titled “Long-term disability trajectories in primary progressive MS patients – a latent class growth analysis,” given at the 32nd Congress of the European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS), held in London on Sept. 14 –17.

In recent decades, a growing number of studies worldwide have focused on the epidemiology of MS — epidemiology being the study of disease causes and patterns in a given population — specifically in relation to primary progressive MS (PPMS) patients. These studies suggest a notable heterogeneity in patients’ rates of disability accumulation, denoted by the wide range, from 7 to 14 years, in which they progress from disease onset to EDSS 6, a milestone on the Expanded Disability Status Scale, or EDSS. The scale is a method of quantifying disability in multiple sclerosis and monitoring changes over time; the higher the EDSS score, the greater the disability.

In this study, researchers aimed to identify subgroups of PPMS patients with similar longitudinal EDSS trajectories over time. The team included data from all patients registered in an MSBase international registry, who had their first EDSS assessment within five years of onset.

In total, 853 patients with PPMS (51.7% females) from 24 countries and a mean age at onset of 42.4 years were included. Patients presented at baseline a median  2.4 years of disease duration, and EDSS scores that ranged from 2.5 to 5.5.

Using a mathematical modeling strategy called the latent class mixed model (LCMM), researchers detected three subgroups of patients with disability trajectories that were either mild (n=143), moderate (n=378), or severe (n=332). The median time for those in a specific subgroup to reach EDSS 4 was 14 years (mild trajectory), 5 years (moderate) and 3.7 years (severe); the probability of patients reaching EDSS 6 at 10 years was 0%, 46.5% and 83.1%, respectively.

Results suggested that this modeling approach could be used to predict the future disease course of PPMS patients in conjunction with early EDSS assessments: with only one year of monitoring patients’ EDSS, researchers correctly classified 73% of the patients in a given disability trajectory group. This number increased to 87% and 92% with three and five years of monitoring, respectively.

“Using long term observations and a LCMM modelling approach it is possible to build a dynamic model, to predict the future disability trajectory of a new patient with PPMS. In the design of future clinical trials in PPMS, with time to reach disability milestones as the primary endpoint, the existence of heterogeneous classes of patients should be considered,” the research team concluded in their ECTRIMS’ abstract.

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