Developing a Natural History Model for Duchenne Muscular Dystrophy
- PMID: 38019449
- PMCID: PMC10781931
- DOI: 10.1007/s41669-023-00450-x
Developing a Natural History Model for Duchenne Muscular Dystrophy
Erratum in
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Correction: Developing a Natural History Model for Duchenne Muscular Dystrophy.Pharmacoecon Open. 2024 Jul;8(4):643. doi: 10.1007/s41669-024-00506-6. Pharmacoecon Open. 2024. PMID: 38958866 Free PMC article. No abstract available.
Abstract
Background: The aim of this study was to pool multiple data sets to build a patient-centric, data-informed, natural history model (NHM) for Duchenne muscular dystrophy (DMD) to estimate disease trajectory across patient lifetime under current standard of care in future economic evaluations. The study was conducted as part of Project HERCULES, a multi-stakeholder collaboration to develop tools to support health technology assessments of new treatments for DMD.
Methods: Health states were informed by a review of NHMs for DMD and input from clinicians, patients and caregivers, and defined using common outcomes in clinical trials and real-world practice. The primary source informing the NHM was the Critical Path Institute Duchenne Regulatory Science Consortium (D-RSC) database. This was supplemented with expert input obtained via an elicitation exercise, and a systematic literature review and meta-analysis of mortality data.
Results: The NHM includes ambulatory, transfer and non-ambulatory phases, which capture loss of ambulation, ability to weight bear and upper body and respiratory function, respectively. The NHM estimates patients spend approximately 9.5 years in ambulatory states, 1.5 years in the transfer state and the remainder of their lives in non-ambulatory states. Median predicted survival is 34.8 years (95% CI 34.1-35.8).
Conclusion: The model includes a detailed disease pathway for DMD, including the clinically and economically important transfer state. The NHM may be used to estimate the current trajectory of DMD in economic evaluations of new treatments, facilitating inclusion of a lifetime time horizon, and will help identify areas for further research.
© 2023. The Author(s).
Conflict of interest statement
Micki Hill has worked for GSK since April 2022. Keith Abrams is a partner and director of Visible Analytics Limited, a health technology assessment consultancy company; a National Institute for Health and Care Research (NIHR) Senior Investigator Emeritus (NF-SI-0512-10159); and a member of the National Institute for Health and Care Excellence (NICE) Diagnostics Advisory Committee. Jane Larkindale has worked for PepGen Inc. since April 2022. There are no other conflicts of interest for authors contributing to this manuscript.
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