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Measurements of Motor Function and Other Clinical Outcome Parameters in Ambulant Children with Duchenne Muscular Dystrophy
Published on: January 12, 2019
Characterization of patients with Duchenne muscular dystrophy across previously developed health states
Francesco Muntoni1, Nathalie Goemans2, Nate Posner3
1Dubowitz Neuromuscular Centre, Great Ormond Street Institute of Child Health, London, United Kingdom.
Abstract:
Project HERCULES has developed a natural history model (NHM) of disease progression in Duchenne muscular dystrophy (DMD) that comprises eight ordered health states (two ambulatory states, one transfer state indicating increased caregiver burden in which patients cannot walk/run 10m or rise from floor but can still support their own weight, and five non-ambulatory states). The current study used data from nine sources (clinical trial placebo arms, one real-world dataset, and three natural history datasets) to further characterize patients with DMD according to these health states. The study included 1,173 patients across 5,306 visits. Patients were on average older and exhibited worse ambulatory, pulmonary, upper limb, and cardiac functions with each successive health state. Mean±SE ages increased monotonically across health states, starting with 8.47±0.07 for early ambulatory, 10.86±0.13 for late ambulatory, 11.65±0.35 for transfer state, and ranging from 13.17±0.32 to 16.84±0.37 for the non-ambulatory states. North Star Ambulatory Assessment (NSAA) total score, which measures motor function and ranges from 34 (best) to 0 (worst), was 23.7 (interquartile range [IQR]: 20-30) for early ambulatory patients, 12.7 (IQR: 9-16) for late ambulatory patients, and 3.9 (IQR: 2-5) for transfer patients. Pulmonary function as measured by mean±SE of forced vital capacity percent predicted (FVC%p) was 94.5±0.8 for early ambulatory, 89.1±1.4 for late ambulatory, and 80.2±2.8 for transfer states, and decreased from 77.2±1.7 to 20.6±1.6 across the five non-ambulatory health states. In summary, these findings further characterize health states and their interpretation in economic modeling and decision-making in DMD management.
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