Fully personalized modelling of Duchenne Muscular Dystrophy ambulation

Duchenne Muscular Dystrophy is a progressive neuromuscular disorder characterized by the gradual weakening and deterioration of muscles, leading to loss of ambulation in affected individuals. This decline in mobility can be effectively assessed using the North Star Ambulatory Assessment (NSAA) scores, along with measures such as the 10-m walk time and the time taken to rise from the floor. We propose a dynamic linear model to predict the trajectories of these clinical outcomes, with a primary focus on NSAA scores. Our model aims to assist clinicians in forecasting the progression of the disease, thereby enabling more informed and personalized treatment plans for their patients. We also evaluate the effectiveness of our models in generating synthetic NSAA score datasets. We assess the performance of our modelling approach and compare the results with those of a previous study. We show that the most robust model demonstrates narrower prediction intervals and improved quantile coverage, indicating superior predictive accuracy and reliability.

This article is part of the theme issue ‘Uncertainty quantification for healthcare and biological systems (Part 2)’.