Predicting North Star Ambulatory Assessment trajectories in Duchenne muscular dystrophy: A mixed-effects modeling approach
Abstract
The North Star Ambulatory Assessment (NSAA) is a widely used functional endpoint in drug development for ambulatory patients with Duchenne muscular dystrophy (DMD). Accurately predicting NSAA total score trajectories is important for designing randomized trials for novel therapies in DMD and for contextualizing outcomes, especially over longer-term follow-up (>18 months) when placebo-controlled studies are infeasible. We developed a prognostic model for NSAA total score trajectories over at most 5 years of follow-up for patients with DMD aged 4 to <16 years who were initially ambulatory and receiving corticosteroids but no other disease-modifying therapies. The model was based on longitudinal data from four natural history databases: UZ Leuven, PRO-DMD-01 (provided by CureDuchenne), the North Star Clinical Network, and iMDEX. Candidate predictors included age, height, weight, body mass index, steroid type and regime, NSAA total score, rise from floor velocity, and 10-meter walk/run velocity, as well as DMD genotype class, index year, and data source. Among N=416 patients at baseline, mean age was 8.2 years, mean NSAA total score was 24, and 61% were receiving prednisone and 39% deflazacort, with the majority having been treated with daily corticosteroid regimens (69%) relative to other regimens (31%). Patients had an average of four NSAA assessments post-baseline during a median follow-up of 2.6 years (inter-quartile range 1.9 to 3.6 years). The best-fitting model in the full study sample explained 39% of the variation in NSAA total score changes, with prediction errors of ±3.6, 5.1, 5.9, 7.5, 9.5 NSAA units during follow-up years 1–5, respectively. The most important predictors were baseline age, NSAA, rise from floor velocity, and 10-meter walk/run velocity. In conclusion, trajectories of ambulatory motor function in DMD, as measured by the NSAA total score, can be well-predicted using readily available baseline characteristics. We discuss applications of these predictions to DMD drug development.
My Take
One of the most challenging aspects of working in rare diseases is the inherent uncertainty in how each patient’s condition will progress. In Duchenne muscular dystrophy, families and clinicians often face difficult questions about what the future holds for a child’s ability to walk and perform daily activities. With this study, we aimed to move beyond population-level averages and develop tools that can generate truly individualized predictions.
What makes this work particularly meaningful is the methodological approach we took. Rather than simply fitting curves to average outcomes, we built a nonlinear mixed-effects model that captures both the typical disease trajectory and the substantial variability between patients. By leveraging baseline characteristics and early observations, the model learns each patient’s likely path and can project forward with quantified uncertainty. The 5-year prediction horizon is clinically relevant—it spans the window during which many therapeutic decisions are made.
From a practical standpoint, this type of prognostic model has multiple applications. It can help set realistic expectations in clinical discussions, identify patients who may be progressing faster or slower than expected, and critically, it provides the foundation for designing more efficient clinical trials. When we can predict what would have happened to a patient without treatment, we gain a powerful reference point for evaluating whether a therapy is making a difference.