Diagnostic accuracy of phenotype classification in duchenne and becker muscular dystrophy using medical record data

Jennifer G. Andrews, Molly M. Lamb, Kristin Conway, Natalie Street, Christina Westfield, Emma Ciafaloni, Dennis Matthews, Christopher M Cunniff, Shree Pandya, Deborah J. Fox

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Dystrophinopathies are caused by mutations in DMD resulting in progressive muscle weakness. They are historically divided into the more severe Duchenne (DMD) and milder Becker (BMD) muscular dystrophy phenotypes. Classification is important for research and clinical care. The purpose of this study was to describe a multi-variable approach to classifying cases from the Muscular Dystrophy Surveillance, Tracking, and Research Network (MD STARnet) and to assess the accuracy of the diagnostic classification scheme. We used age at loss of mobility, molecular testing results, and age at symptom onset to classify cases as having DMD or BMD and to assess sensitivity and specificity. Mobility status showed low sensitivity and high specificity for predicting DMD (65.5% and 99.3%, respectively) and BMD (62.8% and 97.7%, respectively) phenotypes. Molecular testing showed 90.9% sensitivity and 66.4% specificity for DMD; 76.3% sensitivity and 90.0% specificity for BMD. Age of onset predicted DMD with sensitivity of 73.9% and specificity of 69.0%; BMD had 99.7% specificity and 36.7% sensitivity. Mobility status, molecular test results, and age at symptom onset are important but inconsistent measures for accurately classifying individuals into DMD or BMD phenotypes. These results have implications for prognosis in newly diagnosed individuals and for classifying phenotype in clinical trials.

Original languageEnglish (US)
Pages (from-to)481-495
Number of pages15
JournalJournal of Neuromuscular Diseases
Issue number4
StatePublished - 2018


  • Becker
  • Duchenne
  • Muscular dystrophies
  • diagnostic accuracy
  • dystrophinopathy
  • phenotype

ASJC Scopus subject areas

  • Neurology
  • Clinical Neurology


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