Electrodiagnosis of ulnar neuropathy at the elbow (Une): A bayesian approach

Eric L. Logigian, Raissa Villanueva, Paul T. Twydell, Bennett Myers, Marlene Downs, David C. Preston, Milind J. Kothari, David N. Herrmann

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Introduction: In ulnar neuropathy at the elbow (UNE), we determined how electrodiagnostic cutoffs [across-elbow ulnar motor conduction velocity slowing (AECV-slowing), drop in across-elbow vs. forearm CV (AECV-drop)] depend on pretest probability (PreTP). Methods: Fifty clinically defined UNE patients and 50 controls underwent ulnar conduction testing recording abductor digiti minimi (ADM) and first dorsal interosseous (FDI), stimulating wrist, below-elbow, and 6-, 8-, and 10-cm more proximally. For various PreTPs of UNE, the cutoffs required to confirm UNE (defined as posttest probability=95%) were determined with receiver operator characteristic (ROC) curves and Bayes Theorem. Results: On ROC and Bayesian analyses, the ADM 10-cm montage was optimal. For PreTP=0.25, the confirmatory cutoffs were >23 m/s (AECV-drop), and <38 m/s (AECV-slowing); for PreTP=0.75, they were much less conservative: >14 m/s, and <47 m/s, respectively. Conclusions: (1) In UNE, electrodiagnostic cutoffs are critically dependent on PreTP; rigid cutoffs are problematic. (2) AE distances should be standardized and at least 10 cm.

Original languageEnglish (US)
Pages (from-to)337-344
Number of pages8
JournalMuscle and Nerve
Volume49
Issue number3
DOIs
StatePublished - Mar 2014

All Science Journal Classification (ASJC) codes

  • Physiology
  • Clinical Neurology
  • Cellular and Molecular Neuroscience
  • Physiology (medical)

Fingerprint Dive into the research topics of 'Electrodiagnosis of ulnar neuropathy at the elbow (Une): A bayesian approach'. Together they form a unique fingerprint.

Cite this