Title |
Treatment Algorithm for Infants Diagnosed with Spinal Muscular Atrophy through Newborn Screening
|
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Published in |
Journal of Neuromuscular Diseases, May 2018
|
DOI | 10.3233/jnd-180304 |
Pubmed ID | |
Authors |
Jacqueline Glascock, Jacinda Sampson, Amanda Haidet-Phillips, Anne Connolly, Basil Darras, John Day, Richard Finkel, R. Rodney Howell, Katherine Klinger, Nancy Kuntz, Thomas Prior, Perry B. Shieh, Thomas O. Crawford, Douglas Kerr, Jill Jarecki |
Abstract |
Spinal muscular atrophy (SMA) is an autosomal recessive disease characterized by the degeneration of alpha motor neurons in the spinal cord, leading to muscular atrophy. SMA is caused by deletions or mutations in the survival motor neuron 1 gene (SMN1). In humans, a nearly identical copy gene, SMN2, is present. Because SMN2 has been shown to decrease disease severity in a dose-dependent manner, SMN2 copy number is predictive of disease severity. To develop a treatment algorithm for SMA-positive infants identified through newborn screening based upon SMN2 copy number. A working group comprised of 15 SMA experts participated in a modified Delphi process, moderated by a neutral third-party expert, to develop treatment guidelines. The overarching recommendation is that all infants with two or three copies of SMN2 should receive immediate treatment (nā=ā13). For those infants in which immediate treatment is not recommended, guidelines were developed that outline the timing and appropriate screens and tests to be used to determine the timing of treatment initiation. The identification SMA affected infants via newborn screening presents an unprecedented opportunity for achievement of maximal therapeutic benefit through the administration of treatment pre-symptomatically. The recommendations provided here are intended to help formulate treatment guidelines for infants who test positive during the newborn screening process. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 2 | 40% |
Unknown | 3 | 60% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 4 | 80% |
Scientists | 1 | 20% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 245 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Other | 29 | 12% |
Researcher | 28 | 11% |
Student > Master | 28 | 11% |
Student > Bachelor | 21 | 9% |
Student > Ph. D. Student | 15 | 6% |
Other | 39 | 16% |
Unknown | 85 | 35% |
Readers by discipline | Count | As % |
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Medicine and Dentistry | 58 | 24% |
Biochemistry, Genetics and Molecular Biology | 30 | 12% |
Neuroscience | 17 | 7% |
Nursing and Health Professions | 13 | 5% |
Agricultural and Biological Sciences | 9 | 4% |
Other | 27 | 11% |
Unknown | 91 | 37% |